arXiv Daily Digest - 2026-05-04

We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection, diffusion, learned closures, boundary handling) in a query-conditioned way. Rather than learning a monolithic map, HyCOP learns a policy over short programs - which module to apply and for how long - conditioned on regime features and state statistics. Modules may be numerical sub-solvers or learned components, enabling hybrid surrogates evaluated at arbitrary query times without autoregressive rollout. Across diverse PDE benchmarks, HyCOP produces interpretable programs, delivers order-of-magnitude OOD improvements over monolithic neural operators, and supports modular transfer through dictionary updates (e.g., boundary swaps, residual enrichment). Our theory characterizes expressivity and gives an error decomposition that separates composition error from module error and doubles as a process-level diagnostic.

Large language models (LLMs) often achieve strong performance on reasoning benchmarks, but final-answer accuracy alone does not show whether they faithfully execute the procedure specified in a prompt. We study this question through a controlled diagnostic benchmark for procedural execution, where models are given a step-wise arithmetic algorithm and two numeric inputs, and must return the final computed value. The benchmark uses simple arithmetic operations but increases complexity through algorithm length and look-back dependencies over intermediate variables. Across 14 models and 55 datasets, average first-answer accuracy drops from 61% on 5-step procedures to 20% on 95-step procedures. Generation-level analysis shows that failures often involve missing answers, premature answers, self-correction after an initial error, under-executed traces, and hallucinated extra steps. These findings suggest that apparent reasoning ability can mask substantial weaknesses in faithful instruction execution.

While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a "Visual Signal Dilution" phenomenon, where the accumulation of textual history expands the attention partition function, causing visual attention to decay inversely with generated sequence length. To counteract this, we propose Persistent Visual Memory (PVM), a lightweight learnable module designed to ensure sustained, on-demand visual perception. Integrated as a parallel branch alongside the Feed-Forward Network (FFN) in LVLMs, PVM establishes a distance-agnostic retrieval pathway that directly provides visual embeddings for precise visual perception, thereby structurally mitigating the signal suppression inherent to deep generation. Extensive experiments on Qwen3-VL models demonstrate that PVM brings notable improvements with negligible parameter overhead, delivering consistent average accuracy gains across both 4B and 8B scales, particularly in complex reasoning tasks that demand persistent visual perception. Furthermore, in-depth analysis reveals that PVM can resist length-induced signal decay and accelerate internal prediction convergence.

Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benchmarks. However, it is unclear whether such success transfers to computational scientific workflows, where tasks require not only strong coding ability, but also the ability to navigate complex, domain-specific procedures and to interpret results in the context of scientific claims. To address this question, we present AutoMat, a benchmark for evaluating LLM-based agents' ability to reproduce claims from computational materials science. AutoMat poses three interrelated challenges: recovering underspecified computational procedures, navigating specialized toolchains, and determining whether the resulting evidence supports a claim. By working closely with subject matter experts, we curate a set of claims from real materials science papers to test whether coding agents can recover and execute the end-to-end workflow needed to support (or undermine) such claims. We then evaluate multiple representative coding agent settings across several foundation models. Our results show that current LLM-based agents obtain low overall success rates on AutoMat, with the best-performing setting achieving a success rate of only 54.1%. Error analysis further reveals that agents perform worst when workflows must be reconstructed from paper text alone and that they fail primarily due to incomplete procedures, methodological deviations, and execution fragility. Taken together, these findings position AutoMat as both a benchmark for computational scientific reproducibility and a tool for diagnosing the current limitations of agentic systems in AI-for-science settings.

Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are not detectable from data or code alone. Existing chart datasets also rarely provide fully aligned artifacts, such as executable code, dataset context, and question-answer pairs. We present a structured LLM-based workflow that decomposes chart generation into dataset screening, plot proposal, code synthesis, rendering, validation-driven refinement, description generation, and question-answer generation. By incorporating rendered-output validation, the workflow addresses visualization-specific failure modes such as readability and semantic mismatch. It treats chart generation as an inspectable process rather than a one-shot prompt-to-code task, retaining each chart with its code, dataset context, description, and question-answer pairs. Applied to UCI datasets, the workflow produces 1,500 charts from 74 datasets, spanning 24 chart families and paired with 30,003 question-answer pairs. We evaluate 16 multimodal LLMs (MLLMs) on these chart-question pairs. The results show that chart-syntax questions are nearly saturated, while value extraction, comparison, and reasoning remain more challenging, illustrating the workflow's utility for diagnostic studies of chart-grounded multimodal reasoning.

Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a multi-agent plan execution platform that interprets natural-language plans while enforcing stepwise execution through constraints and rubrics. RunAgent bridges the expressiveness of natural language with the determinism of programming via an agentic language with explicit control constructs (e.g., \texttt{IF}, \texttt{GOTO}, \texttt{FORALL}). Beyond verifying syntactic and semantic verification of the step output, which is performed based on the specific instruction of each step, RunAgent autonomously derives and validates constraints based on the description of the task and its instance at each step. RunAgent also dynamically selects among LLM-based reasoning, tool usage, and code generation and execution (e.g., in Python), and incorporates error correction mechanisms to ensure correctness. Finally, RunAgent filters the context history by retaining only relevant information during the execution of each step. Evaluations on Natural-plan and SciBench Datasets demonstrate that RunAgent outperforms baseline LLMs and state-of-the-art PlanGEN methods.

Background: Patient-facing medical chatbots based on retrieval-augmented generation (RAG) are increasingly promoted to deliver accessible, grounded health information. AI-assisted development lowers the barrier to building them, but they still demand rigorous security, privacy, and governance controls. Objective: To report an anonymized, non-destructive security assessment of a publicly accessible patient-facing medical RAG chatbot and identify governance lessons for safe deployment of generative AI in health. Methods: We used a two-stage strategy. First, Claude Opus 4.6 supported exploratory prompt-based testing and structured vulnerability hypotheses. Second, candidate findings were manually verified using Chrome Developer Tools, inspecting browser-visible network traffic, payloads, API schemas, configuration objects, and stored interaction data. Results: The LLM-assisted phase identified a critical vulnerability: sensitive system and RAG configuration appeared exposed through client-server communication rather than restricted server-side. Manual verification confirmed that ordinary browser inspection allowed collection of the system prompt, model and embedding configuration, retrieval parameters, backend endpoints, API schema, document and chunk metadata, knowledge-base content, and the 1,000 most recent patient-chatbot conversations. The deployment also contradicted its privacy assurances: full conversation records, including health-related queries, were retrievable without authentication. Conclusions: Serious privacy and security failures in patient-facing RAG chatbots can be identified with standard browser tools, without specialist skills or authentication; independent review should be a prerequisite for deployment. Commercial LLMs accelerated this assessment, including under a false developer persona; assistance available to auditors is equally available to adversaries.

With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed tomography using deep learning. Although low-dose computed tomography reduces radiation exposure to patients, it also introduces more noise, which may interfere with visual interpretation by physicians and affect diagnostic results. To address this problem, inspired by Cycle-GAN for unsupervised learning, this paper proposes an end-to-end unsupervised low-dose computed tomography denoising framework. The proposed framework combines a U-Net structure for multi-scale feature extraction, an attention mechanism for feature fusion, and a residual network for feature transformation. It also introduces perceptual loss to improve the network for the characteristics of medical images. In addition, we construct a real low-dose computed tomography dataset and design a large number of comparative experiments to validate the proposed method, using both image-based evaluation metrics and medical evaluation criteria. Compared with classical methods, the main advantage of this paper is that it addresses the limitation that real clinical data cannot be directly used for supervised learning, while still achieving excellent performance. The experimental results are also professionally evaluated by imaging physicians and meet clinical needs.

Key-Value (KV) cache has become a de facto component of modern Large Vision-Language Models (LVLMs) for inference. While it enhances decoding efficiency in Large Language Models (LLMs), its direct adoption in LVLMs introduces substantial GPU memory overhead due to the large number of vision tokens processed during the prefill stage. To tackle this problem, we propose LightKV, a novel approach that reduces KV cache size by exploiting the redundancy among vision-token embeddings. Guided by text prompts, LightKV employs cross-modality message passing to aggregate informative messages across vision tokens and progressively compress them during prefill. This prompt-aware guidance distinguishes our method from prior vision-only compression strategies. We evaluate LightKV on eight open-source LVLMs across eight public benchmark datasets, e.g., MME and SeedBench. Experimental results demonstrate that with only 55% of the original vision tokens, LightKV (a) halves the vision-token KV cache size, (b) reduces computation by up to 40%, and (c) preserves general-purpose performance while significantly outperforming existing baselines.

While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy updates directly in the action space. To bridge this gap, a geometry-aware RL algorithm that explicitly incorporates value-based similarity into the policy update, State-Action Value Geometry Optimization (SAVGO), is proposed. In detail, SAVGO learns a joint state-action embedding space in which pairs with similar action-value estimates exhibit high cosine similarity, while dissimilar pairs are mapped to distinct directions. This learned geometry enables the generation of a similarity kernel over candidate actions sampled at each update, allowing policy improvement to be guided directly toward higher-value regions beyond local gradient-based updates. As a result, representation learning, value estimation, and policy optimization are unified within a single geometry-consistent objective, while preserving the scalability of off-policy actor-critic training. The proposed method is evaluated on standard MuJoCo continuous-control benchmarks, demonstrating improvements over strong baselines on challenging high-dimensional tasks. Ablation studies are done to analyze the contributions of value-geometry learning and similarity-based policy updates.

Reliable spatial analysis in GIScience requires preserving coordinate semantics, topology, units, and geographic plausibility. Current LLM-based GIS systems generate fluent scripts but rarely enforce these geographic rules at scale. We present GeoContra, a verification and repair framework for LLM-driven Python GIS workflows. It represents each task as an executable geospatial contract-including natural-language questions, schemas, CRS metadata, expected outputs, spatial predicates, topology, metrics, required operations, and forbidden shortcuts. Generated programs undergo static rule inspection, runtime validation, and semantic verification, with violations fed back into a bounded repair loop. Evaluated on 7,079 real geospatial tasks across 15 Boston-area zones, 9 task families, and 11 open-source models (600 runs each), GeoContra improves spatial correctness on closed models from 47.6% to 77.5% for DeepSeek-V4 and from 57.7% to 81.5% for Kimi-K2.5. Across 11 open models, average correctness rises by 26.6%. GeoContra turns fluent code production into verifiable spatial analysis, catching negative travel times, CRS/field-schema violations, missing predicates, and brittle output casts that otherwise yield executable but geographically invalid results.

In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presumes a correspondence between output metrics and internal system states that may not hold in adaptive systems. In this study, the vertical dimension of occlusion (VDO) is considered as a constraint applied to an adaptive neuromechanical system, enabling the exploration of system-level responses under controlled variations. A single-case design in a patient with Parkinson's disease allows an intra-individual analysis across repeated conditions.The analysis is structured across three complementary levels: (i) aggregated linear metrics describing observable performance, (ii) a dynamical systems framework describing temporal organization in state space, and (iii) a latent space representation obtained through unsupervised embedding. The results show that conditions with comparable observable performance may correspond to different organizations in both state space and latent space representations. This dissociation highlights a limitation of aggregated metrics and suggests that similar outputs may arise from non-equivalent system states. A fourth level is proposed as a purely conceptual extension describing potential relationships between system states. This level is not implemented and is not derived from experimental data. These observations are strictly exploratory and non-causal. The proposed framework does not establish mechanistic, predictive, or directional relationships, but provides a structured approach for analyzing constraint-driven systems across multiple levels of representation.

A speaker encoder used in multilingual voice cloning should treat the same speaker identically regardless of which script the audio was uttered in. Off-the-shelf encoders do not, and the failure is accent-conditional. On a 1043-pair Western-accented voice corpus across English, Hindi, Telugu, and Tamil, WavLM-base-plus-sv loses 0.082 absolute cosine similarity when the same voice changes script and ECAPA-TDNN loses 0.105. On a 1369-pair Indian-accented voice corpus, the gap shrinks to 0.006 (WavLM-SV) and 0.044 (ECAPA-TDNN). The leak is largest where it matters most for cross-script TTS: when a system projects a non-Indic-trained voice into Indic scripts. We present LASE (Language-Adversarial Speaker Encoder), a small projection head over frozen WavLM-base-plus trained with two losses: a supervised contrastive loss over voice identity, and a gradient-reversal cross-entropy against a 4-language classifier that pushes the embedding to be language-uninformative while remaining speaker-informative. Trained on 1118 quality-gated cross-script pairs synthesised from 8 commercial multilingual voices, LASE's residual gap is consistent with zero on both corpora (Delta = 0.013 Western, Delta = 0.026 Indian; both bootstrap 95% CIs include zero) and amplifies the cross-script-vs-floor margin 2.4-2.7x over both baselines. An ECAPA+GRL ablation shows the GRL objective improves either backbone but the WavLM choice contributes too. In synthetic multi-speaker diarisation, LASE matches ECAPA-TDNN on cross-script speaker recall (0.788 vs 0.789) with ~100x less training data. We release the r1 checkpoint, both corpora, and the bootstrap recipe.

The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, compassion) and anti-social sentiment (e.g., threats, opposition, blame) at different topics, all in the same message. While many natural language processing (NLP) tools classify or score a text's overall sentiment as positive, neutral, or negative, these tools cannot report that positive and negative sentiments coexist, and they cannot report the target of those sentiments. This paper presents the Directed Social Regard (DSR) approach to multi-dimensional, multi-valence sentiment analysis, comprised of a pair of transformer-based models that (1) detects span-level targets of sentiment in a message and then (2) scores all spans within the message context along three (-1, 1) axes of regard that are motivated by social science theories of moral disengagement and moral framing. We present a data collection and annotation strategy for DSR dataset construction, a transformer-based architecture for span-level scoring, and a validation study with promising results. We apply the validated DSR model on six third-party datasets of online media and report meaningful correlations between DSR outputs and the labels and topics in these pre-existing social science datasets.

The transformer is the most popular neural architecture for language modeling. The cornerstone of the transformer is its global attention mechanism, which lets the model aggregate information from all preceding tokens before generating the next token. One common variant of attention is called local attention, which restricts each token to aggregating information from a bounded window of predecessors, reducing the quadratic cost of global attention to linear. Although this restriction is usually motivated by efficiency, it has also been found to improve model quality, a phenomenon that has so far lacked a satisfactory explanation. We provide a formal account of this phenomenon in terms of recognizer expressivity. It has been shown that fixed-precision transformers with global attention correspond to a fragment of linear temporal logic containing a single past operator. We additionally prove that adding local attention introduces a second temporal operator, strictly enlarging the class of recognizable regular languages. Moreover, global and local attention are expressively complementary: neither subsumes the other, and combining them yields the richest fragment. Experiments on formal language recognition and natural language modeling corroborate the theory, showing that hybrid global--local transformers outperform their global-only counterparts.

We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit feedback, the contribution of individual arms is not received in full-bandit feedback, making the setting significantly more challenging. To compute arm contributions in BCMAB-FBF, we first extend the Shapley value, a classical solution concept from cooperative game theory, to the $K$-Shapley value, which captures the marginal contribution of an agent restricted to a set of size at most $K$. We show that $K$-Shapley value is a unique solution concept that satisfies Symmetry, Linearity, Null player, and efficiency properties. We next propose K-SVFair-FBF, a fairness-aware bandit algorithm that adaptively estimates $K$-Shapley value with unknown valuation function. Unlike standard bandit literature on full bandit feedback, K-SVFair-FBF not only learns the valuation function under full feedback setting but also mitigates the noise arising from Monte Carlo approximations. Theoretically, we prove that K-SVFair-FBF achieves $O(T^{3/4})$ regret bound on fairness regret. Through experiments on federated learning and social influence maximization datasets, we demonstrate that our approach achieves fairness and performs more effectively than existing baselines.

This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the DeepONet framework. We consider a 2D square domain with an inclusion of arbitrary boundary geometry at its center. This inclusion acts as a scatterer for an incoming harmonic wave. The aim is to learn the operator linking the geometry of the scatterer to the resulting scattered field. A signed distance function to the boundary of the inner inclusion, evaluated at several points in the domain, is used to encode its geometry. It serves as input for the branch part of the DeepONet architecture, while local information is used as input for the trunk part. This approach enables the encoding of arbitrary geometries, whether they are parameterized or not. The evaluation of the model on unseen geometries is compared with its finite element method (FEM) equivalent to test its generalization capabilities. The trained network weights implicitly embed the local physics and their interaction with the domain geometry. If the training space sufficiently covers the target evaluation space, the model can generalize accordingly. Furthermore, it can be refined to extend to another region of interest without retraining from scratch. This framework also avoids the need to remesh the domain for each geometry. The proposed approach delivers a computationally lighter surrogate model than FEM alternatives and avoids relying on FEM-generated training data.

Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling. Research on the application of RMs in code generation, however, has been comparatively sparse, with existing work largely focusing on execution feedback. This choice constrains post-training to optimizing functional correctness over self-contained executable code. In this work, we examine the training and evaluation of multilingual, multi-criteria code RMs. To this end, we first compile Themis-CodeRewardBench, a benchmark to evaluate code RMs across five preference dimensions (i.e., criteria) and eight programming languages, on which we profile 50+ code, math, and general-purpose RMs. Observing the limited proficiency of current RMs beyond scoring for functional correctness, we develop Themis-CodePreference, the largest open-source collection of code preferences to date (more than 350k preference pairs), and use it to train Themis-RM, a suite of multilingual code reward models for flexible multi-criteria scoring, ranging in size from 600M to 32B parameters. Our experiments and ablations demonstrate positive scaling trends, strong cross-lingual transfer when training on diverse preferences, and the importance of multi-criteria training for reliable code reward modeling.

Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-agent domains because expansion must consider an exponentially large set of joint actions, severely limiting exploration under realistic search budgets. We propose NonZero, which keeps multi-agent MCTS tractable by running surrogate-guided selection over a low-dimensional nonlinear representation using an interaction-guided proposal rule, instead of directly exploring the full joint-action space. Our exploration uses an interaction score: single-agent deviations are ranked by predicted gain, while two-agent deviations are scored by a mixed-difference measure that reveals coordination benefits even when no single agent can improve alone. We formalize candidate proposal as a bandit problem over local deviations and derive a proposal rule, NonZero, with a sublinear local-regret guarantee for reaching approximate graph-local optima without enumerating the joint-action space. Empirically, NonZero improves sample efficiency and final performance on MatGame, SMAC, and SMACv2 relative to strong model-based and model-free baselines under matched search budgets.

Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Interval bound propagation (IBP) is a popular certified training method in classical machine learning, where the lower and upper bounds are tracked throughout the model. These bounds are used during training to ensure that the model is certified to predict the correct label even under adversarial perturbations. While IBP is successful in classical domain, there are limited certified training efforts in quantum domain. In this paper, we present quantum interval bound propagation (QIBP) to establish a certified training routine for quantum machine learning, certifying the accuracy of models under adversarial perturbations. We implement QIBP using both interval and affine arithmetic to explore the tradeoffs between the two implementations in terms of accuracy and other design considerations. Extensive evaluation demonstrates that the resulting certified trained models have robust decision boundaries, guaranteed to predict the correct class for the samples within the trained adversarial robustness bounds.

LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many resources to invest. While the usefulness and feasibility of Bayesian approaches remain unclear for LLM inference, this position paper argues that the control layer of an agentic AI system (that orchestrates LLMs and tools) is a clear case where Bayesian principles should shine. Bayesian decision theory provides a framework for agentic systems that can help to maintain beliefs over task-relevant latent quantities, to update these beliefs from observed agentic and human-AI interactions, and to choose actions. Making LLMs themselves explicitly Bayesian belief-updating engines remains computationally intensive and conceptually nontrivial as a general modeling target. In contrast, this paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system, not necessarily the LLM agent parameters. This paper articulates practical properties for Bayesian control that fit modern agentic AI systems and human-AI collaboration, and provides concrete examples and design patterns to illustrate how calibrated beliefs and utility-aware policies can improve agentic AI orchestration.

Randomized-subspace methods reduce the cost of first-order optimization by using only low-dimensional projected-gradient information, a feature that is attractive in forward-mode automatic differentiation and communication-limited settings. While Nesterov acceleration is well understood for full-gradient and coordinate-based methods, obtaining accelerated methods for general subspace sketches that use only projected-gradient information and can improve over full-dimensional Nesterov acceleration in oracle complexity is technically nontrivial. We develop randomized-subspace Nesterov accelerated gradient methods for smooth convex and smooth strongly convex optimization under matrix smoothness and generic sketch moment assumptions. The key technical ingredient is a three-sequence formulation tailored to matrix smoothness, which recovers the corresponding classical Nesterov methods in the full-dimensional case. The resulting theory establishes accelerated oracle-complexity guarantees and makes explicit how matrix smoothness and the sketch distribution enter the complexity. It also provides a unified basis for comparing sketch families and identifying when randomized-subspace acceleration improves over full-dimensional Nesterov acceleration in oracle complexity.

With the proliferation of Electronic Health Records (EHRs), a critical challenge in building predictive models is determining the optimal historical data time window to maximize accuracy. This study investigates the impact of various observation windows ranging from the day of surgery to three years prior on predicting 30-day readmission following hip and knee arthroplasties. The dataset encompasses both structured encounter records (over 4 million) and unstructured clinical notes (80,000) from 7,174 patients. To extract meaning from the clinical notes, we employed a suite of non neural (BOW, count BOW, TF IDF, LDA) and neural encoders (BERT, 1D CNN, BiLSTM, Average). We subsequently evaluated models utilizing clinical notes alone, structured data alone, and a combination of both modalities. Our results demonstrate that the optimal time window for unstructured clinical notes is significantly shorter than for structured data, maximum predictive performance was achieved using notes from just three to six months prior to surgery. In contrast, performance using structured data improved as the time window lengthened, but strictly plateaued after twelve months. These modality-specific temporal patterns remained consistent regardless of model complexity or encoder type. Ultimately, these findings challenge the general assumption that more historical data inherently yields better machine learning predictions, establishing targeted time-window guidelines for optimizing readmission prediction models.

Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundant or even harmful. Effective tool use, therefore, hinges on a core LLM decision: whether to call or not call a tool, when performing a task. This decision is particularly challenging for web search tools, where the benefits of external information depend on the model's internal knowledge and its ability to integrate potentially noisy tool responses. We introduce a principled framework inspired by decision-making theory to evaluate web search tool-use decisions along three key factors: necessity, utility, and affordability. Our analysis combines two complementary lenses: a normative perspective that infers true need and utility from an optimal allocation of tool calls, and a descriptive perspective that infers the model's self-perceived need and utility from their observed behaviors. We find that models' perceived need and utility of tool calls are often misaligned with their true need and utility. Building on this framework, we train lightweight estimators of need and utility based on models' hidden states. Our estimators enable simple controllers that can improve decision quality and lead to stronger task performance than the self-perceived set up across three tasks and six models.

Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles forgotten knowledge across both modalities and client gradient subspaces, hindering federated unlearning. Previous federated unlearning approaches neither sever the cross-modal reconstruction channel mediated by bilinear coupling nor separate forget-exclusive update directions from those shared with retained clients. We identify an Anchor Principle for federated multimodal contrastive unlearning: forgotten alignments persist through three residual anchors arising from bilinear cross-modal coupling, principal-angle subspace entanglement, and continued federated updates. At the modality level, we show that bilateral displacement of both visual and language branches closes the cross-modal reconstruction channel. Correspondingly, our method addresses subspace entanglement through Cosine--Sine decomposition of client-update subspaces, isolating forget-exclusive directions from retain support. Moreover, we propose a direction-selective Forget Lock that bounds residual drift across rounds. Combining these strategies, we present EASE, an Entanglement-Aware Subspace Excision framework that closes all three anchor channels under a unified design. EASE demonstrates consistent superiority across multiple datasets and unlearning scenarios, for instance, matching the retrain reference to within 0.2 and 4.2 R@1 points on the forget and retain sides under client unlearning on Flickr30K with CLIP-B/32.

While Graph Foundation Models (GFMs) have achieved remarkable success in homogeneous graphs, extending them to multi-domain heterogeneous graphs (MDHGs) remains a formidable challenge due to cross-type feature shifts and intra-domain relation gaps. Existing global feature alignment methods (PCA or SVD) enforce a shared feature space blindly, which distorts type-specific semantics and disrupts original topologies, inevitably leading to "Type Collapse" and "Relation Confusion". To address these fundamental limitations, we propose Decoupled relation Subspace Alignment (DRSA), a novel, plug-and-play relation-driven alignment framework. DRSA fundamentally shifts the paradigm by decoupling feature semantics from relation structures. Specifically, it introduces a dual-relation subspace projection mechanism to coordinate cross-type interactions within a shared low-rank relation subspace explicitly. Furthermore, a feature-structure decoupled representation is designed to decompose aligned features into a semantic projection component and a structural residual term, adaptively absorbing intra-domain variations. Optimized via a stable alternating minimization strategy based on Block Coordinate Descent, DRSA constructs a well-calibrated, structure-aware latent space. Extensive experiments on multiple real-world benchmark datasets demonstrate that DRSA can be seamlessly integrated as a universal preprocessing module, significantly and consistently enhancing the cross-domain and few-shot knowledge transfer capabilities of state-of-the-art GFMs. The code is available at: https://github.com/zhengziyu77/DSRA.

Combinatorial complexes have unified set-based (e.g., graphs, hypergraphs) and part-whole (e.g., simplicial, cellular complexes) structures into a common topological framework. Existing topological neural networks and Weisfeiler-Lehman variants remain fragmented, lacking a unified theoretical foundation for topological deep learning. In this work, we introduce the Combinatorial Complex Weisfeiler-Lehman (CCWL) test, an axiomatic-style extension of the WL test to combinatorial complexes. CCWL formalizes topological message passing through four types of neighborhood relation and provides a unified perspective on the expressive power of higher-order variants. We further prove that upper and lower neighborhoods are sufficient among the four adjacent WL tests to reach the expressivity of the full CCWL framework across topological structures of combinatorial complexes. Building on this framework, we also propose the Combinatorial Complex Isomorphism Network (CCIN) and evaluate it on synthetic and real-world benchmarks. Experimental results indicate CCIN outperforms baseline methods and offers a generalized expressive framework for topological deep learning.

We propose Decentralized Proximal Stochastic Gradient Langevin Dynamics (DE-PSGLD), a decentralized Markov chain Monte Carlo (MCMC) algorithm for sampling from a log-concave probability distribution constrained to a convex domain. Constraints are enforced through a shared proximal regularization based on the Moreau-Yosida envelope, enabling unconstrained updates while preserving consistency with the target constrained posterior. We establish non-asymptotic convergence guarantees in the 2-Wasserstein distance for both individual agent iterates and their network averages. Our analysis shows that DE-PSGLD converges to a regularized Gibbs distribution and quantifies the bias introduced by the proximal approximation. We evaluate DE-PSGLD for different sampling problems on synthetic and real datasets. As the first decentralized approach for constrained domains, our algorithm exhibits fast posterior concentration and high predictive accuracy.

The Room Acoustics and Speaker Distance Estimation (SDE) Challenge at ICASSP 2025 explores the effectiveness of augmented room impulse response (RIR) data for improving SDE model performance. This challenge at GenDARA involves generating RIRs to supplement sparse datasets and fine-tuning SDE models with the augmented data. We employ the open-source fast diffuse room impulse response generator (FastRIR) conditioned only on speaker and listener locations. We design a quality filter to ensure generated RIR alignment with challenge RIRs, and hyperparameter optimization is employed for model fine-tuning. Our approach reduces the mean absolute error (MAE) of the five positions from 1.66m to 0.6m for GWA rooms and from 2.18m to 0.69m for Treble rooms, with results demonstrating that the augmentation approach significantly improves estimation accuracy, particularly at medium to long distances.

Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learned features relate to graph structure. Many networks naturally admit a role-mixture view, where nodes are best described as mixtures over latent archetypal factors. Motivated by this structure, we propose a compositional graph embedding framework grounded in Aitchison geometry, the canonical geometry for comparing mixtures. Nodes are represented as simplex-valued compositions and embedded via isometric log-ratio (ILR) coordinates, which preserve Aitchison distances while enabling unconstrained optimization in Euclidean space. This yields intrinsically interpretable embeddings whose geometry reflects relative trade-offs among archetypes and supports coherent behavior under component restriction; we consider both fixed and learnable ILR bases. Across node classification and link prediction, our method achieves competitive performance with strong baselines while providing explainability by construction rather than post-hoc. Finally, subcompositional coherence enables principled component restriction: removing and renormalizing subsets preserves a well-defined geometry, which we exploit via subcompositional dimensionality removal to probe how archetype groups influence representations and predictions.

Effectively stratifying patient risk in chronic diseases like glaucoma is a major clinical challenge. Clinicians need tools to identify patients at high risk of progression from sparse and irregularly-sampled electronic health records (EHRs). We propose a novel deep kernel learning (DKL) architecture that leverages a Gaussian Process (GP) backend. The GP's kernel is defined by a transformer-based feature extractor applied to clinical-BERT embeddings to model glaucoma patient trajectories from multimodal EHR data. Our method successfully identifies three clinically distinct patient subgroups. Crucially, the model learns to decouple disease progression from current severity, identifying a high-risk group with a worsening trajectory despite having better average visual acuity than a second, stably poor group. This reveals that the model learns to identify progression risk rather than just the current disease state. This ability to stratify patients based on their risk trajectory progression offers a powerful tool for clinical decision support, enabling targeted interventions for high-risk individuals and improving the management of glaucoma care.

Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical behavior, posing serious compliance risks. To systematically evaluate LLM safety in finance, we propose FinSafetyBench, a bilingual (English-Chinese) red-teaming benchmark designed to test an LLM's refusal of requests that violate financial compliance. Grounded in real-world financial crime cases and ethics standards, the benchmark comprises 14 subcategories spanning financial crimes and ethical violations. Through extensive experiments on general-purpose and finance-specialized LLMs under three representative attack settings, we identify critical vulnerabilities that allow adversarial prompts to bypass compliance safeguards. Further analysis reveals stronger susceptibility in Chinese contexts and highlights the limitations of prompt-level defenses against sophisticated or implicit manipulation strategies.

Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences over long interactions. Existing memory systems mainly rely on static, hand-crafted update rules; although reinforcement learning (RL)-based agents learn memory updates, sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization. Drawing on memory schema theory and the functional division between prefrontal regions and hippocampus regions, we introduce MemCoE, a cognition-inspired two-stage optimization framework that learns how memory should be organized and what information to update. In the first stage, we propose Memory Guideline Induction to optimize a global guideline via contrastive feedback interpreted as textual gradients; in the second stage, Guideline-Aligned Memory Policy Optimization uses the induced guideline to define structured process rewards and performs multi-turn RL to learn a guideline-following memory evolution policy. We evaluate on three personalization memory benchmarks, covering explicit/implicit preference and different sizes and noise, and observe consistent improvements over strong baselines with favorable robustness, transferability, and efficiency.

Federated learning (FL) holds great potential for medical applications. However, statistical heterogeneity across healthcare institutions poses a major challenge for FL, as the global model struggles both to generalize across unseen patient populations and to adapt to the unique data distributions of individual hospitals. This heterogeneity also exacerbates forgetting at both the global and local level, resulting in previous learned patient patterns to be misclassified after model updates. While prior work has largely treated generalization and personalization as separate challenges, we show that a better balance between the two can be achieved through selective alignment with the global model and a modified aggregation scheme, which together mitigate the effects of statistical heterogeneity. Specifically, we introduce FedKPer, which introduces knowledge personalization into the training stage of each local device. Afterwards, generalization is considered via the global model aggregation process, where local updates that are reliable and label-diverse are emphasized. We evaluate the performance of FedKPer, devising additional metrics that relate to common consequences of forgetting. Overall, we demonstrate FedKPer improves the generalization-personalization trade-off without sacrificing retention.

We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight question budgets. Classical Bayesian design and computerized adaptive testing typically rely on restrictive parametric assumptions or expensive posterior approximations, limiting their use in heterogeneous, high-dimensional, and cold-start settings. We introduce a persona-induced latent variable model that represents a user's state through membership in a finite dictionary of AI personas, each offering response distributions produced by a large language model. This yields expressive priors with closed-form posterior updates and efficient finite-mixture predictions, enabling scalable Bayesian design for sequential item selection. Experiments on synthetic data and WorldValuesBench demonstrate that persona-based posteriors deliver accurate probabilistic predictions and an interpretable adaptive elicitation pipeline.

Distributed blackbox consensus optimization is a fundamental problem in multi-agent systems, where agents must improve a global objective using only local objective queries and limited neighbor communication. Existing methods largely rely on handcrafted update rules and static cooperation patterns, which often struggle to balance local adaptation, global coordination, and communication efficiency in heterogeneous nonconvex environments. In this paper, we take an initial step toward trajectory-driven self-design for distributed black-box consensus optimization. We first redesign the agent-level swarm dynamics with an adaptive internal mechanism tailored to decentralized consensus settings, improving the balance between exploration, convergence, and local escape. Built on top of this adaptive execution layer, we propose Learning to Act and Cooperate (LACMAS), a trajectorydriven framework in which large language models provide sparse highlevel guidance for shaping both agentinternal action behaviors and agentexternal cooperation patterns from historical optimization trajectories. We further introduce a phased cognitive scheduling strategy to activate different forms of adaptation in a resource-aware manner. Experiments on standard distributed black-box benchmarks and real-world distributed tasks show that LAC-MAS consistently improves solution quality, convergence efficiency, and communication efficiency over strong baselines, suggesting a practical route from handcrafted distributed coordination toward self-designing multi-agent optimization systems.

As Large Language Models (LLMs) are increasingly deployed in cross-linguistic contexts, ensuring safety in diverse regulatory and cultural environments has become a critical challenge. However, existing multilingual benchmarks largely rely on general risk taxonomies and machine translation, which confines guardrail models to these predefined categories and hinders their ability to align with region-specific regulations and cultural nuances. To bridge these gaps, we introduce ML-Bench, a policy-grounded multilingual safety benchmark covering 14 languages. ML-Bench is constructed directly from regional regulations, where risk categories and fine-grained rules derived from jurisdiction-specific legal texts are directly used to guide the generation of multilingual safety data, enabling culturally and legally aligned evaluation across languages. Building on ML-Bench, we develop ML-Guard, a Diffusion Large Language Model (dLLM)-based guardrail model that supports multilingual safety judgment and policy-conditioned compliance assessment. ML-Guard has two variants, one 1.5B lightweight model for fast `safe/unsafe' checking and a more capable 7B model for customized compliance checking with detailed explanations. We conduct extensive experiments against 11 strong guardrail baselines across 6 existing multilingual safety benchmarks and our ML-Bench, and show that ML-Guard consistently outperforms prior methods. We hope that ML-Bench and ML-Guard can help advance the development of regulation-aware and culturally aligned multilingual guardrail systems.

Recent megakernel designs for Mixture-of-Experts (MoE) inference fuse expert computation with fine-grained, GPU-initiated communication into a single persistent GPU kernel, and outperform collective-based MoE on a single node by overlapping data transfer with compute at tile granularity. This benefit does not carry over cleanly to multi-node inference, where experts span many nodes connected by an RDMA fabric. Communication-bound MoE models regress by up to $10\times$ on 8 nodes, and the regression worsens with node count. We trace this regression to hidden serialization in proxy-based RDMA transports. The ordering requirement between each tile transfer and its completion signal forces a fence that drains the NIC pipeline, and its cost grows with the number of concurrent transfers. As a result, models whose per-expert compute is too small to absorb this inflated network latency expose communication on the critical path. We present \emph{Perseus}, which eliminates this serialization through two techniques. \emph{Decoupled signaling} batches fences at per-destination granularity, reducing fence count by $8\times$. \emph{NIC-side ordering} replaces proxy stalls with hardware fence flags, so the proxy never blocks. On proxy-based transports, Perseus achieves up to 10.3$\times$ end-to-end speedup. Perseus on IBRC matches or exceeds IBGDA GPU-direct by up to 1.2$\times$, which shows that serialization, rather than the choice between proxy-based and GPU-direct transport, is what bounds multi-node megakernel performance.

While Large Language Models have achieved notable success on formal mathematics benchmarks such as MiniF2F, it remains unclear whether these results stem from genuine logical reasoning or semantic pattern matching against pre-training data. This paper identifies Architectural Reasoning: the ability to synthesize formal proofs using exclusively local axioms and definitions within an alien math domain, as the necessary ability for future automated theorem discovery AI. We use the Obfuscated Natural Number Game, a benchmark to evaluate Architectural Reasoning. By renaming identifiers in the Natural Number Game in Lean 4, we created a zero-knowledge, closed environment. We evaluate state-of-the-art models, finding a universal latency tax where obfuscation increases inference time. The results also reveal a divergence in robustness: while general models (Claude-Sonnet-4.5, GPT-4o) suffer performance degradation, reasoning models (DeepSeek-R1, GPT-5, DeepSeek-Prover-V2) maintain the same accuracy despite the absence of semantic cues. These findings provide a quantitative metric for assessing the true capacity for mathematical reasoning.

Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating progress: they are often narrow in scope, quickly saturated, and rarely updated. This makes it hard to compare models reliably and track progress over time. Instead, we need evaluation platforms: continuously maintained systems that run, aggregate, and analyze evaluations across many benchmarks to give a comprehensive picture of model performance within a broad domain. In this work, we build on the original MathArena benchmark by substantially broadening its scope from final-answer olympiad problems to a continuously maintained evaluation platform for mathematical reasoning with LLMs. MathArena now covers a much wider range of tasks, including proof-based competitions, research-level arXiv problems, and formal proof generation in Lean. Additionally, we maintain a clear evaluation protocol for all models and regularly design new benchmarks as model capabilities improve to ensure that MathArena remains challenging. Notably, the strongest model, GPT-5.5, now reaches 98% on the 2026 USA Math Olympiad and 74% on research-level questions, showing that frontier models can now comfortably solve extremely challenging mathematical problems. This highlights the importance of continuously maintained evaluation platforms like MathArena to track the rapid progress of LLMs in mathematical reasoning.

Safety is a primary challenge in real-world reinforcement learning (RL). Formulating safety requirements as state-wise constraints has become a prominent paradigm. Handling state-wise constraints with the Lagrangian method requires a distinct multiplier for every state, necessitating neural networks to approximate them as a multiplier network. However, applying standard dual gradient ascent to multiplier networks induces severe training oscillations. This is because the inherent instability of dual ascent is exacerbated by network generalization -- local overshoots and delayed updates propagate to adjacent states, further amplifying policy fluctuations. Existing stabilization techniques are designed for scalar multipliers, which are inadequate for state-dependent multiplier networks. To address this challenge, we propose an augmented Lagrangian multiplier network (ALaM) framework for stable learning of state-wise multipliers. ALaM consists of two key components. First, a quadratic penalty is introduced into the augmented Lagrangian to compensate for delayed multiplier updates and establish the local convexity near the optimum, thereby mitigating policy oscillations. Second, the multiplier network is trained via supervised regression toward a dual target, which stabilizes training and promotes convergence. Theoretically, we show that ALaM guarantees multiplier convergence and thus recovers the optimal policy of the constrained problem. Building on this framework, we integrate soft actor-critic (SAC) with ALaM to develop the SAC-ALaM algorithm. Experiments demonstrate that SAC-ALaM outperforms state-of-the-art safe RL baselines in both safety and return, while also stabilizing training dynamics and learning well-calibrated multipliers for risk identification.

We present a training-free approach for controllable 3D inpainting based on initial noise optimization. In the structured 3D latent diffusion framework, we observe that the underlying geometric structure is established during the early stages of the diffusion process and exhibits high sensitivity to the initial noise. Such characteristics compromise stability in tasks like inpainting and editing, where the model must ensure strict alignment with the existing context while synthesizing a new structure. In this paper, we introduce a strategy to optimize the initial noise within the structured 3D latent diffusion framework, ensuring high-fidelity 3D inpainting. Specifically, we update the initial noise by leveraging a backpropagation approximation grounded in the rectified flow model, with the spectral parameterization specially designed for robust and efficient structured 3D latent optimization. Experiments demonstrate consistent improvements in contextual consistency and prompt alignment over representative training-free inpainting baselines, establishing initial noise control as an independent dimension for 3D inpainting, orthogonal to conventional sampling trajectory manipulation.

Sequence learning reduces to similarity-based retrieval over a temporally indexed representation space, a constraint on any sequence model, not a property of a specific architecture. We show that a spiking Sparse Distributed Memory sequence machine (2007) and the transformer (2017) independently instantiate the same five functional operations (encoding, context maintenance, associative retrieval, storage, and decoding), with cosine similarity as the shared retrieval primitive in both. We formalise a Phase-Latency Isomorphism showing that sinusoidal positional phase and spike timing are linearly related, and prove that dot product attention is invariant to this mapping up to a global scale factor on the positional component (Lemma 1). Empirically, frequency-compressed positional encoding fails to converge on a positionally demanding copy task, while a learned rank-based embedding matches or exceeds sinusoidal encoding, indicating that the critical property for positional representation is distance discriminability under dot-product similarity, not sinusoidal form. Time, phase, and rank are three instantiations of the same computational primitive, an ordered index whose structure survives similarity-based retrieval.

For a risk-averse finite-horizon Markov Decision Problem, we introduce a special class of Markov coherent risk measures, called mini-batch measures. We also define the class of multipattern risk-averse problems that generalizes the class of linear systems. We use both concepts in a feature-based $Q$-learning method with multipattern $Q$-factor approximation and we prove a high-probability regret bound of $\mathcal{O}\big(H^2 N^H \sqrt{ K}\big)$, where $H$ is the horizon, $N$ is the mini-batch size, and $K$ is the number of episodes. We also propose an economical version of the $Q$-learning method that streamlines the policy evaluation (backward) step. The theoretical results are illustrated on a stochastic assignment problem and a short-horizon multi-armed bandit problem.

Fine-tuning LLMs is necessary for various dedicated downstream tasks, but classic backpropagation-based fine-tuning methods require substantial GPU memory. To this end, a recent work, MeZO, which relies solely on forward passes to fine-tune LLMs, significantly reduces GPU requirements at the cost of slower convergence due to its indifference to loss landscapes. Standard solutions, such as Adam, explore loss landscapes by estimating the first- and second-order moments and storing them in memory to guide the model's movement through dimensions with lower curvature and vice versa. However, directly applying Adam negates MeZO's advantage as it will triple the memory requirement. In light of this, we propose AdaMeZO, a zeroth-order optimizer that leverages Adam-style first- and second-moment estimates without maintaining them in memory. We present a theoretical analysis of AdaMeZO, corroborated by extensive experiments demonstrating AdaMeZO's performance, showing that AdaMeZO can outperform MeZO while requiring up to $70\%$ fewer forward passes. Trajectory visualizations affirm AdaMeZO's ability to adapt to diverse loss landscapes.

Assigning one of K options to each of N groups under a total cost budget is a recurring problem in machine learning, appearing in mixed-precision quantization, non-uniform pruning, and expert selection. The objective (model loss) depends jointly on all assignments and does not decompose across groups, which prevents combinatorial solvers from optimizing the true objective directly and limits them to proxy objectives. Evolutionary search evaluates the actual loss but lacks gradient information, while penalty-based methods provide gradients but enforce the budget only approximately and require sensitive hyperparameter tuning. We observe that under softmax relaxation, the budget constraint defines a smooth Riemannian manifold in logit space with particularly simple geometry: the normal vector is available in closed form, shifting logits along the cost vector changes expected cost monotonically, allowing binary-search retraction, and vector transport reduces to a single inner product. Building on this structure, we propose Riemannian Constrained Optimization (RCO), which augments a standard Adam update with tangent projection, binary-search retraction, and momentum transport. Combined with Gumbel straight-through estimation and budget-constrained dynamic programming for discrete feasibility, RCO enables first-order optimization of the true objective under exact budget enforcement, without introducing constraint hyperparameters. On synthetic knapsack problems with known optima, the manifold-based constraint handling recovers optimal solutions, whereas penalty methods plateau at 83% of optimal. On LLM compression tasks, including mixed-precision quantization and MoE expert pruning, RCO matches or exceeds evolutionary search methods while requiring 3x to 16x lower wall-clock cost on the evaluated configurations.

Automated pediatric electrocardiogram (ECG) diagnosis remains challenging because models trained predominantly on adult data suffer from substantial cross-population mismatch, while pediatric labels are often scarce. We present PEACE (Pediatric-Adult ECG Alignment via Cross-modal Enhancement), a structured cross-modal alignment framework for adult-to-pediatric ECG transfer. PEACE integrates tri-axial clinical semantic decomposition, label-query feature extraction, and curriculum-gated optimization to align transferable adult ECG representations with pediatric diagnostic targets. Since ZZU-pECG provides no paired clinical reports, we generate label-conditioned semantic descriptors using Gemini with concise clinical prompts and use them only as auxiliary training supervision; inference remains ECG-only. On ZZU-pECG, PEACE achieves 59.39%, 79.03%, and 90.89% AUC under zero-shot, 50-shot, and full fine-tuning settings, respectively, and reaches 96.65% AUC on the shared PTB-XL label space. These results suggest that structured clinical semantic supervision can improve low-resource adult-to-pediatric ECG transfer, while prospective clinical validation and more explicit age-aware modeling remain necessary before real-world deployment.

Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually useful for the task it is meant to serve. In safety-critical settings, low average error can coexist with dangerous failures in exactly the high-risk regimes that matter most. We present a task-aware evaluation framework for blood glucose forecasting built around two downstream uses: hypoglycemia early warning and insulin dosing decision support. For early warning, we evaluate on real data from three clinical cohorts using event-level recall and false alarms per patient-day, metrics that reflect operational alarm burden rather than aggregate accuracy. We show that models appearing acceptable overall, with recall above 0.9 on the full test set, can fail badly in the post-bolus slice, where insulin-on-board is elevated and missed warnings carry the greatest clinical consequences. Standard forecasting evaluation, however, does not test whether a model can reason about the effects of actions, a requirement for supporting insulin dosing decisions. We therefore add a second, interventional arm using the FDA-accepted UVA/Padova simulator, where we evaluate whether forecasters can predict glucose responses to altered insulin plans in paired factual/counterfactual scenarios. We show that models that look strong on real-data forecasting often fail to predict the direction, magnitude, or ranking of intervention effects, and choose poor insulin doses when evaluated under a clinically motivated cost. Taken together, the two arms reveal a consistent gap between forecasting accuracy and task-relevant usefulness. We release the benchmark, the standardized preprocessing pipeline for public cohorts, and the simulator-based interventional dataset as a reproducible toolkit.

Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. However, learning multimodal EBM by maximum likelihood requires Markov Chain Monte Carlo (MCMC) sampling in the joint data space, where noise-initialized Langevin dynamics often mixes poorly and fails to discover coherent inter-modal relationships. Multimodal VAEs have made progress in capturing such inter-modal dependencies by introducing a shared latent generator and a joint inference model. However, both the shared latent generator and joint inference model are parameterized as unimodal Gaussian (or Laplace), which severely limits their ability to approximate the complex structure induced by multimodal data. In this work, we study the learning problem of the multimodal EBM, shared latent generator, and joint inference model. We present a learning framework that effectively interweaves their MLE updates with corresponding MCMC refinements in both the data and latent spaces. Specifically, the generator is learned to produce coherent multimodal samples that serve as strong initial states for EBM sampling, while the inference model is learned to provide informative latent initializations for generator posterior sampling. Together, these two models serve as complementary models that enable effective EBM sampling and learning, yielding realistic and coherent multimodal EBM samples. Extensive experiments demonstrate superior performance for multimodal synthesis quality and coherence compared to various baselines. We conduct various analyses and ablation studies to validate the effectiveness and scalability of the proposed multimodal framework.

Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves as a core capability for autonomous GUI agents. Recent reinforcement learning methods (e.g., GRPO) have achieved strong performance, but they rely on expensive multiple rollouts and suffer from sparse signals on hard samples. These limitations make on-policy self-distillation (OPSD), which provides dense token-level supervision from a single rollout, a promising alternative. However, its applicability to GUI grounding remains unexplored. In this paper, we present GUI-SD, the first OPSD framework tailored for GUI grounding. First, it constructs a visually enriched privileged context for the teacher using a target bounding box and a Gaussian soft mask, providing informative guidance without leaking exact coordinates. Second, it employs entropy-guided distillation, which adaptively weights tokens based on digit significance and teacher confidence, concentrating optimization on the most impactful and reliable positions. Extensive experiments on six representative GUI grounding benchmarks show that GUI-SD consistently outperforms GRPO-based methods and naive OPSD in both accuracy and training efficiency. Code and training data are available at https://zhangyan-ucas.github.io/GUI-SD/.

Both Dimensionality Reduction (DR) and Graph Drawing (GD) aim to visualize abstract, non-linear structures, yet rely on different optimization paradigms. This contrast is evident in Multidimensional Scaling (MDS), which typically depends on the SMACOF algorithm despite graph drawing results showing that simpler stochastic optimization schemes can be more effective for the same objective. We bridge these domains by adapting Stochastic Gradient Descent (SGD) techniques from graph drawing to vector data embedding. We present a scikit-learn compatible estimator that minimizes global stress through local pairwise updates, improving upon the existing implementation. Experiments on standard high-dimensional benchmarks show that our stochastic solver converges substantially faster than SMACOF while achieving comparable or lower stress.

Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These methods scale unfavorably with foundation-scale MLIPs, and their member-disagreement signals correlate weakly with per-molecule prediction error. Here we probe the frozen per-atom representations of a pretrained MLIP with a compact discriminative classifier, recasting MLIP uncertainty quantification as selective classification rather than error regression. The resulting method, PROBE (Post-hoc Reliability frOm Backbone Embeddings), produces a per-prediction reliability probability that monotonically tracks actual error without modification to the underlying model. Across large held-out evaluation sets and two structurally distinct MLIP architectures, PROBE outperforms ensemble disagreement as a binary reliability signal, which strengthens with the expressiveness of the backbone representation, implying a favorable scaling trajectory toward foundation-scale MLIPs. Multi-head self-attention additionally yields per-atom importance maps, providing chemically interpretable diagnostics at no additional computational cost. PROBE is post-hoc and architecture-agnostic, and is directly deployable on any MLIP that exposes per-atom representations.

Autonomous science is transforming how we discover materials and chemical systems for advanced energy technologies. However, many initially promising systems never reach deployment. This "valley of death" stems from optimization that prioritizes laboratory metrics over industrial viability. We propose a new strategy: "born-qualified" autonomous development, which embeds manufacturability, cost, and durability constraints from the outset. This approach is enabled by four pillars, including the development of multi-objective metrics, causal models, a modular infrastructure, and embedding manufacturing in the discovery loop. Realizing this vision will require sustained, community-wide commitment, but the potential return on that investment is commensurate with the scale of the challenge.

Machine unlearning aims to unlearn data points from a learned model, offering a principled way to process data-deletion requests and mitigate privacy risks without full retraining. Prior work has mainly studied unsupervised / supervised machine unlearning, leaving unlearning for sequential decision-making systems far less understood. We initiate the first study of a foundational sequential decision-making problem: offline stochastic multi-armed bandits (MAB). We formalize the privacy constraint for offline MAB and measure utility by the post-unlearning decision quality. We conduct a systematic study of both single- and multi-source unlearning scenarios under two data-generation models, the fixed-sample model and the distribution model. For these settings, our algorithmic design is built on two canonical base algorithms: Gaussian mechanism and rollback, and we propose adaptive algorithms that switch between them according to the data regime and privacy constraint. We further introduce a mixing procedure that elucidates the rationale behind these baselines. We provide performance guarantees across the above settings and establish lower bounds under both dataset models. Experiments validate the predicted tradeoffs and demonstrate the effectiveness of the proposed methods.

Dimensionality reduction (DR) techniques are often characterized by whether they preserve global, high-level structures in the data or local, neighborhood structures. This distinction matters in visualization: global methods can obscure clusters while local methods can over-emphasize them. Yet, even when clusters appear distinct, their relative arrangement in the projection may be arbitrary or misleading, a common issue in techniques such as t-SNE and UMAP. Existing cluster quality metrics either only measure cluster separability or assume spherical, globular clusters in the original space. We introduce the Class Angular Distortion Index (CADI), a metric that uses internal angles among point triples to determine the faithfulness of cluster organization in a projection. We show cases on both real and synthetic data where existing cluster metrics fail, but CADI provides an interpretable result. Since it relies on computing angles, CADI is also differentiable, enabling optimization. We demonstrate this with a CADI-based DR technique.

Automatic generation of executable Blender code from natural language remains challenging, with state-of-the-art LLMs producing frequent syntactic errors and geometrically inconsistent objects. We present BlenderRAG, a retrieval-augmented generation system that operates on a curated multimodal dataset of 500 expert-validated examples (text, code, image) across 50 object categories. By retrieving semantically similar examples during generation, BlenderRAG improves compilation success rates from 40.8% to 70.0% and semantic normalized alignment from 0.41 to 0.77 (CLIP similarity) across four state-of-the-art LLMs, without requiring fine-tuning or specialized hardware, making it immediately accessible for deployment. The dataset and code will be available at https://github.com/MaxRondelli/BlenderRAG.

We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages). Task A evaluates standalone retrieval quality, while Task C assesses end-to-end retrieval-augmented generation (RAG) in multi-turn conversational settings, requiring both accurate answer generation and faithful grounding in retrieved evidence. Our approach implements a hierarchical parent-child RAG pipeline that separates fine-grained child-level retrieval from parent-level context reconstruction during generation. Documents are segmented into overlapping sentence-based child chunks, while full documents are preserved as parent units to provide coherent context. Retrieval combines hybrid dense-sparse search, tunable weighting, and embedding-based similarity rescoring over child chunks. Retrieved evidence is aggregated at the parent level and supplied to an instruction-tuned language model for response generation. H-RAG achieves an nDCG@5 score of 0.4271 on Task A and a harmonic mean score of 0.3241 on Task C (RB_agg: 0.2488, RL_F: 0.2703, RB_llm: 0.6508), underscoring the importance of retrieval configuration and parent-level aggregation in multi-turn RAG performance.

Text-to-SQL enables non-expert users to query databases in natural language, yet real-world schemas often suffer from ambiguous, abbreviated, or inconsistent naming conventions that degrade model accuracy. Existing approaches treat schemas as fixed and address errors downstream. In this paper, we frame schema refinement as a constrained optimization problem: find a renaming function that maximizes downstream Text-to-SQL execution accuracy while preserving query equivalence through database views. We analyze the computational hardness of this problem, which motivates a column-wise greedy decomposition, and instantiate it as EGRefine: a four-phase pipeline that screens ambiguous columns, generates context-aware candidate names, verifies them through execution-grounded feedback, and materializes the result as non-destructive SQL views. The pipeline carries two structural properties: column-local non-degradation, ensured by the conservative selection rule in the verification phase, and database-level query equivalence, ensured by the view-based materialization phase. Together they make the resulting refinement safe by construction at the column level, with cross-column and prompt-level interactions handled empirically rather than analytically. Across controlled schema-degradation, real-world, and enterprise benchmarks, EGRefine recovers accuracy lost to schema naming noise where applicable and correctly abstains where the underlying task exceeds current Text-to-SQL capabilities, with refined schemas transferring across model families to enable refine-once, serve-many-models deployment. Code and data are publicly available at https://github.com/ai-jiaqian/EGRefine.

Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A high-quality scientific taxonomy offers a structured and hierarchical representation of a research field, facilitating literature exploration and topic navigation, as well as enabling downstream applications such as trend analysis, idea generation, and information retrieval. However, existing taxonomy generation approaches often suffer from structural inconsistencies and semantic misalignment across hierarchical levels. Through empirical analysis, we find that these issues largely stem from inadequate modeling of hierarchical semantic consistency. To address this limitation, we propose a semantic-consistent taxonomy generation (SC-Taxo) framework that leverages large language models (LLMs) with hierarchy-aware refinement stages to ensure semantic consistency. Specifically, SC-Taxo introduces a bidirectional heading generation mechanism that jointly performs bottom-up abstraction and top-down semantic constraint, while further capturing peer-level semantic dependencies to enhance horizontal consistency. Experiments on multiple benchmark datasets demonstrate consistent improvements in hierarchy alignment and heading quality, and additional evaluation on Chinese scientific literature validates its robust cross-lingual generalization.

We investigate the extent to which cosine similarity between paragraph embeddings is invariant under machine translation, using the Manifesto Corpus of over 2,800 political party platforms in 28 languages translated to English via the EU eTranslation service. Rather than measuring translation-induced semantic shift directly we measure the stability of pairwise similarity relationships across embedding models, and use inter-model disagreement on original-language text as a calibrated invariance threshold. This yields a per-language non-inferiority test for four hypotheses about how translation interacts with embedding choice, with verdicts that distinguish languages where translation demonstrably preserves semantic structure from those where it demonstrably degrades it and from those where the available evidence does not resolve the question. The framework is corpus- and pipeline-agnostic and extends naturally to downstream tasks. Applied to our data, it identifies ten languages with translation invariance and four with detectable distortion.

Realistic evaluation of LLM serving systems requires online workloads, dynamic arrivals, queueing, and the serving engine's local scheduling for execution batching, but running such experiments on GPUs is expensive. Existing simulators reduce this cost, but often operate offline or in time-warped mode, re-implement serving-engine schedulers, or require accurate operator/kernel-level latency models. We present LLM-Emu, a serving-native emulator for vLLM that preserves the production HTTP, scheduling, KV-cache, and output-processing paths while replacing only GPU forward execution with profile-sampled latency and synthetic output tokens. Tested on two different GPUs, four model variants, two model families, two attention backends, and both Poisson and bursty ShareGPT workloads, LLM-Emu closely tracks real vLLM serving behavior: TPOT and ITL stay within $4.8\%$ absolute error, E2E latency within $5.3\%$, and output throughput within $1.9\%$; TTFT is less stable, with maximum error $10.4\%$, reflecting its sensitivity to admission and queue state. These results suggest that lightweight, serving-native emulation can support practical online experimentation for LLM-serving systems. LLM-Emu is open sourced at https://github.com/AKafakA/llm-emu.

SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadth while RLVR enhances reasoning depth. Yet integrating these complementary strengths remains a formidable challenge. Sequential training can cause catastrophic forgetting, and joint optimization often suffers from severe gradient conflicts. We analyze SFT and RLVR through the lens of task vectors and reveal three structural properties behind these failures: a 30* magnitude disparity, 45* sign interference, and heterogeneous module-wise update distributions. These findings show SFT and RLVR are difficult to integrate directly, but they also suggest that the two paradigms modify partly complementary components of the model. Motivated by these observations, we propose Decoupled Test-time Synthesis (DoTS), a post-hoc framework allows SFT and RLVR checkpoints to be trained independently and synthesizes their capabilities only at inference time via task vector arithmetic, without updating model parameters. To reduce interference, DOTS applies selective sparsification with norm-preserving rescaling. It then uses Bayesian optimization on a small set of unlabeled queries to search for combination coefficients on the Pareto frontier of consistency and perplexity. Empirically, \ours matches or exceeds the performance of training-based SFT--RLVR integration methods across multiple mathematical reasoning benchmarks, incurring only $\sim$3\% of the computational cost. When applied to stronger post-trained checkpoints, DOTS surpasses SOTA models and generalizes to out-of-domain benchmarks without re-tuning. Code is available at https://github.com/chaohaoyuan/DoTS.

Probing is widely used to study which features can be decoded from language model representations. However, the common decoding probe approach has two limitations that we aim to solve with our new encoding probe approach: contributions of different features to model representations cannot be directly compared, and feature correlations can affect probing results. We present an Encoding Probe that reverses this direction and reconstructs internal representations of models using interpretable features. We evaluate this method on text and speech transformer models, using feature sets spanning acoustics, phonetics, syntax, lexicon, and speaker identity. Our results suggest that speaker-related effects vary strongly across different training objectives and datasets, while syntactic and lexical features contribute independently to reconstruction. These results show that the Encoding Probe provides a complementary perspective on interpreting model representations beyond decodability.

Sparse MoE routing fails at domain transitions, where the current token belongs to one distribution and the next to another. In a controlled experiment (4 experts, 5 seeds), standard affinity routing assigns only 0.006 +/- 0.001 probability to the correct expert at the transition. Three lightweight gate modifications raise this to 0.748 +/- 0.002 (124x), cutting experts needed for 99% coverage from infeasible to a small constant: temporal memory (beta), a per-expert LIF membrane potential accumulating routing context across tokens; precision-weighted gating (Pi), a per-expert inverse variance of recent prediction error, yielding 31x contrast between reliable and unreliable experts; and anticipatory routing, a next-state predictor conditioned on the beta-accumulated hidden state. The mechanisms draw from Friston's Free Energy Principle and use LIF dynamics from spiking neural networks. An ablation across all 2^3 subsets reveals a super-additive beta x Ant interaction: anticipation alone gives nothing (+0.000 +/- 0.001); beta alone gives modest gain (+0.295 +/- 0.013); combined they close 75% of the oracle gap (+0.741 +/- 0.002, exceeding the sum by +0.446 +/- 0.014). This is structural: a stateless predictor cannot detect approaching transitions because pre-transition tokens are distributionally identical to within-domain tokens. In a character-level MoE LM (5 seeds), beta-routing reduces transition-step BPC from 6.56 +/- 0.01 (Standard) to 4.01 +/- 0.15 (beta-MoE); the beta + Ant gate places 0.86 +/- 0.02 probability on the correct domain expert before that domain appears in input, vs 0.42 +/- 0.12 for Standard MoE. Reference implementations (~200 lines each): https://github.com/russellwmy/affinity-is-not-enough

Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modelling. Existing methods for uncertainty modelling face a fundamental dilemma: Bayesian approaches provide principled estimates but remain computationally prohibitive, while efficient second-order predictors lack rigorous derivations connecting their specific objectives to epistemic uncertainty quantification. To resolve this dilemma, we introduce Dirichlet-approximated possibilistic posterior predictions (DAPPr), a principled framework leveraging possibility theory. We define a possibilistic posterior over parameters, projects this posterior to the prediction space via supremum operators, and approximates the projected posterior using learnable Dirichlet possibility functions. This projection-and-approximation strategy yields a simple training objective with closed-form solutions. Extensive experiments across diverse benchmarks demonstrate that our approach achieves competitive or superior uncertainty quantification performance compared to state-of-the-art evidential deep learning methods while maintaining both principled derivation and computational efficiency. Code will be available at https://github.com/MaxwellYaoNi/DAPPr.

In Machine Learning, an accepted definition of fairness of a decision taken by a classifier is that it should not depend on protected features, such as gender. Unfortunately, when constraints exist between features, such dependencies can be obscured by the constraints. To avoid this problem, we propose that a decision be considered fair if it has a fair explanation. We define a fair explanation as a prime-implicant reason for the decision that does not contain any protected feature (where the constraints are taken into account in the definition of prime-implicant). Surprisingly, ignoring constraints can completely change the fairness of a decision (according to this definition) even in the absence of constraints between protected and unprotected features. Three possible definitions of fairness of a classifier are that for all its decisions (1) there are only fair explanations, (2) there is at least one fair explanation, or (3) changing protected features does not change the outcome. We identify the relationships between these different definitions of fairness and study the computational complexity of testing fairness of classifiers.

The visual modality of vision-language models (VLMs) is an underexplored attack surface for bypassing safety alignment. We introduce four jailbreak attacks exploiting the vision component: (1) encoding harmful instructions as visual symbol sequences with a decoding legend, (2) replacing harmful objects with benign substitutes (e.g., bomb -> banana) then prompting for harmful actions using the substitute term, (3) replacing harmful text in images (e.g., on book covers) with benign words while visual context preserves the original meaning, and (4) visual analogy puzzles whose solution requires inferring a prohibited concept. Evaluating across six frontier VLMs, our visual attacks bypass safety alignment and expose a cross-modality alignment gap: text-based safety training does not automatically generalize to harmful intent conveyed visually. For example, our visual cipher achieves 40.9% attack success on Claude-Haiku-4.5 versus 10.7% for an equivalent textual cipher. To further our insight into the attack mechanism, we present preliminary interpretability and mitigation results. These findings highlight that robust VLM alignment requires treating vision as a first-class target for safety post-training.

Expectations about the support of artificial intelligence (AI) may influence interaction outcomes similar to placebos. Such expectations may result from AI washing, a practice of overstating a system's AI capabilities when actual functionality is limited. For example, some computer mice are marketed as "AI-assisted" despite lacking AI in core functions. In a within-subjects study, 28 participants completed Fitts' Law tasks with a computer mouse under three conditions: no support, supposed predictive AI support, and supposed biosignal-enhanced AI support. Objective Fitts' Law performance indicators and subjective performance expectations, perceived workload, and perceived usability were measured. Compared to baseline, participants expected significantly improved performance in placebo conditions. However, these expectations did not translate into differences in objective or subjective assessments. This paper contributes evidence that AI washing inflates user expectations without altering actual interaction outcomes, highlighting a critical transparency issue. By exposing how deceptive AI marketing can shape user expectations, we underscore the need for accountability in AI product claims. Further, we establish Fitts' Law as a rigorous methodological lens for auditing AI-labelled input devices.

Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being based on Newton boosting: a second-order descent step in the space of decision trees. Despite its empirical success, the global convergence of Newton boosting is poorly understood compared to first-order boosting. In this paper, we introduce Restricted Newton Descent, which studies convex optimization with Newton's method on Hilbert spaces with inexact iterates, based on the concepts of cosine angle and weak gradient edge. Within this framework, we recover Newton boosting with GBDTs and classical finite-dimensional theory as special cases. We first prove that vanilla Newton boosting achieves a linear rate of convergence for smooth, strongly convex losses that satisfy a Hessian-dominance condition. To handle general convex losses with Lipschitz Hessians, we extend a recent gradient regularized Newton scheme to the restricted weak learner setting. This scheme minimally modifies the classical algorithm by introducing an adaptive $\ell_2$-regularization term proportional to the square root of the gradient norm at each iteration. We establish a $\mathcal{O}(\frac{1}{k^2})$ rate for this scheme, thereby obtaining a globally convergent second-order GBDT algorithm with a rate matching that of first-order boosting with Nesterov momentum. In numerical experiments, we show that our scheme converges while vanilla Newton boosting may diverge.

Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particularly evident in the Electric Capacitated Vehicle Routing Problem, where instances differ in structure, demand patterns, and energy constraints. This paper investigates instance-aware parameter configuration for Bilevel Late Acceptance Hill Climbing, a state-of-the-art metaheuristic for the Electric Capacitated Vehicle Routing Problem. An offline tuning procedure is used to obtain instance-specific parameter labels, which are then mapped from instance features via a regression model to enable parameter prediction for unseen instances prior to execution. Experimental results on the IEEE WCCI 2020 benchmark and its extensions show that the proposed approach achieves an average objective value reduction of $0.28\%$ across eight held-out test instances relative to a globally tuned configuration. This corresponds to a significant cost reduction in multimillion-dollar transportation operations.

Scientific discovery is an extended process of ideation--surveying prior work, forming hypotheses, and refining reasoning--yet existing approaches treat this phase as a brief preamble despite its central role in research. We introduce SCISENSE, a sensemaking-grounded framework that operationalizes ideation as a structured sequence of eight cognitive stages (Pirolli \& Card, 2005). We construct SCISENSE-Traj, a 100K-scale dataset of citation-conditioned research trajectories in two modes: Target, where an LLM reconstructs the ideation path leading to a known paper from its cited works, and Infer, where the LLM proposes novel directions from the same citations. We distill these into SCISENSE-LM, a family of sensemaking LLMs spanning 3B to 70B parameters. Contrary to the assumption that looser supervision promotes greater exploration, Target-trained models achieve a 2.0\% improvement in trajectory quality over Infer-trained models while also producing more novel and diverse outputs. This advantage propagates downstream: coding agents conditioned on Target trajectories produce research artifacts with higher executability and quality than those conditioned on Infer trajectories. This suggests that targeted ideation reduces cognitive burden on downstream agents, freeing them to explore more creatively. SCISENSE offers both a practical tool for augmenting LLM-driven research workflows and a principled testbed for studying how planning shapes scientific discovery.

Partial driving automation creates a tension: drivers remain legally responsible for vehicle behaviour, yet their active control is significantly reduced. This reduction undermines the engagement and sense of agency needed to intervene safely. Meaningful human control (MHC) has been proposed as a normative framework to address this tension. However, empirical methods for evaluating whether existing systems actually provide MHC remain underdeveloped. In this study, we investigated the extent to which drivers experience MHC when interacting with partially automated driving systems. Twenty-four drivers completed a simulator study involving silent automation failures under two modes - haptic shared control (HSC) and traded control (TC). We derived behavioural metrics from telemetry data, subjective perception scores from post-trial surveys and used them to test hypothesised relations between them derived from the properties of systems under MHC. The confirmatory analysis showed a significant negative correlation between the perception of the automated vehicle (AV) understanding the driver and conflict in steering torques. An exploratory analysis also revealed a surprising positive correlation between reaction times and the perception of sufficient control. Qualitative feedback from open-ended post-experiment questionnaires revealed that mismatches in intentions between the driver and automation, lack of safety, and resistance to driver inputs contribute to the reduction of perceived MHC, while subtle haptic guidance aligned with driver intent had a positive effect. These findings suggest that future designs should prioritise effortless driver interventions, transparent communication of automation intent, and context-sensitive authority allocation to strengthen meaningful human control in partially automated driving.

To efficiently support Large Language Models (LLMs), modern GPGPU architectures have introduced new features and programming paradigms, such as warp specialization. These features enable temporal overlap between the producer and consumer, as well as between matrix multiplication and activation function operations, substantially improving performance. To conduct effective AI infrastructure and computer architecture research, cycle-accurate simulators that support these new features, together with analytical models that faithfully capture workload characteristics, are essential. However, existing academic tools provide limited support for these emerging requirements. Existing cycle-accurate simulators do not incorporate new NVIDIA GPU features, such as the Tensor Memory Accelerator (TMA), in a timely manner. Moreover, existing analytical models can misestimate DRAM traffic under certain configurations. In this paper, we build a simulation pipeline from FlashAttention-3 kernel instrumentation to cycle-accurate simulation. The simulator achieves a mean absolute percentage error (MAPE) of 5.7\% and a maximum absolute percentage error of 12.7\% against H800. We also provide a theoretical analysis of FlashAttention-3 and explain why existing analytical models can produce inaccurate traffic estimates.

Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effective and diverse attacks in red-teaming is important, but achieving both is challenging. Generative Flow Networks (GFNs) that perform distribution matching are a promising methods, but they are notorious for training instability and mode collapse. In particular, unstable rewards in red-teaming accelerate mode collapse. We propose Stable-GFN (S-GFN), which eliminates partition function $Z$ estimation in GFN and reduces training instability. S-GFN avoids Z-estimation through pairwise comparisons and employs a robust masking methodology against noisy rewards. Additionally, we propose a fluency stabilizer to prevent the model from getting stuck in local optima that produce gibberish. S-GFN provides more stable training while maintaining the optimal policy of GFN. We demonstrate the overwhelming attack performance and diversity of S-GFN across various settings.

AI agents that interact with graphical user interfaces (GUIs) require effective observation representations for reliable grounding. The accessibility tree is a commonly used text-based format that encodes UI element attributes, but it suffers from redundancy and lacks structural information such as spatial relationships among elements. We propose A11y-Compressor, a framework that transforms linearized accessibility trees into compact and structured representations. Our implementation, Compressed-a11y, applies a lightweight and structured transformation pipeline with modal detection, redundancy reduction, and semantic structuring. Experiments on the OSWorld benchmark show that Compressed-a11y reduces input tokens to 22% of the original while improving task success rates by 5.1 percentage points on average.

Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apoptosis. Existing unbalanced Optimal Transport (OT) methods treat mass as a continuous fluid, performing inference at the population level. However, this macroscopic view often fails to capture the discrete, jump-like nature of birth-death events at single-cell resolution, which is essential for understanding lineage branching and fate decisions. We present Unbalanced Schrödinger Bridge (USB), a simulation-free framework for learning underlying dynamics that effectively integrates both stochastic and unbalanced effects which also models the discrete, jump-like birth-death dynamics at single-cell resolution. Theoretically, USB provides a tractable solution to the Branching Schrödinger Bridge (BSB) problem, offering a rigorous microscopic interpretation where individual cells undergo both Brownian motion and discrete birth-death jumps. Technically, the method implements an efficient solver by introducing a simulation-free training objective that effectively scales to high-dimensional omics data. Empirically, we demonstrate on both simulated and real-world datasets that USB not only achieves trajectory reconstruction performance better than or comparable to deterministic baselines but also uniquely enables realistic discrete simulation of birth-death dynamics at single-cell resolution.

Quantization is a key method for reducing the GPU memory requirement of training large language models (LLMs). Yet, current approaches are ineffective for 4-bit activations and 8-bit gradients, which would easily cause slow convergence or accuracy loss. To address this, we introduce AGoQ, incorporating two new techniques: 1) a layer-aware activation quantization algorithm that allocates appropriate bit-widths for activations of various layers based on their types and pipeline stages to achieve near 4-bit activation storage, and 2) a gradient quantization algorithm that reduces memory usage and shortens communication time by employing 8-bit gradient storage and precision-preserving 8-bit All-Reduce communication. We conduct extensive experiments using different sizes of LLMs on two GPU clusters (up to 64 GPUs), and the experimental results show that our AGoQ reduces the memory by up to 52\% and achieves up to 1.34$\times$ improvement of training speed compared to state-of-the-art training systems Megatron-LM (w/ or w/o ZeRO), COAT and DeepSpeed with 8B to 32B LLaMA models, while achieving convergence loss on pretraining and comparable accuracy on downstream tasks with LLaMA architectures.

Blood vessel segmentation and -tracing are essential tasks in many medical imaging applications. Although numerous methods exist, the prevailing segment-then-fix paradigm is fundamentally limited regarding its suitability for modeling the task of complete and topologically accurate vascular network reconstruction. Here, we propose an approach to extract topologically more accurate vascular graphs from 3D image data, building upon highly successful ideas from the related biomedical tasks of cell segmentation and -tracking. Our approach first predicts voxel-wise vessel direction vectors joint with standard vessel segmentation masks. Second, to extract the vascular graph from these predictions, we introduce a direction-vector-guided extension of the TEASAR algorithm. Our approach achieves state-of-the-art performance on three benchmark datasets, spanning both synthetic and real imagery. We further demonstrate the applicability of our approach to challenging 3D micro-CT scans of rat heart vasculature. Finally, we propose meaningful and interpretable measures of topological error, namely false splits and false merges for graphs. Overall, our approach substantially improves the topological accuracy of reconstructed vascular graphs, being able to separate closely apposed vessel segments and handle multiple vascular trees within a single volume.

Scaling laws for Large Language Models (LLMs) establish that model quality improves with computational scale, yet edge deployment imposes strict constraints on compute, memory, and power. Since General Matrix Multiplication (GEMM) accounts for up to 90\% of inference time, efficient GEMM acceleration is critical for edge AI. The Adaptive Intelligent Engines available in the AMD Versal adaptive SoCs are well suited for this task, but existing state-of-the-art (SOTA) frameworks maximize performance through spatial scaling, distributing workloads across hundreds of cores -- an approach that fails on resource-limited edge SoCs due to physical implementation failures, bandwidth saturation, and excessive resource consumption. We propose Tempus, a Resource-Invariant Temporal GEMM framework for the AMD Versal AI Edge SoC. Rather than expanding hardware resources with matrix size, Tempus employs a fixed compute block of 16 AIE-ML cores, achieving scalability through iterative graph execution and algorithmic data tiling and replication in the Programmable Logic. High-speed cascade streaming ensures low-latency partial sum reduction at Initiation Interval (II) of 1, while a deadlock-free DATAFLOW protocol maximizes transfer-compute overlap and PLIO reuse. Evaluated on GEMM workloads, Tempus achieves 607 GOPS at 10.677 W total on-chip power. By characterizing system-level efficiency through the Platform-Aware Utility (PAU) metric, we prove that Tempus achieves a 211.2x higher prominence factor than the leading spatial SOTA (ARIES). Furthermore, the framework maintains a 0.00\% utilization of URAM/DSP, yielding 22.0x core frugality, 7.1x power frugality, and a 6.3x reduction in I/O demand, establishing a sustainable, scalable foundation for edge LLM inference.

Autonomous driving systems (ADS) are increasingly deployed in real traffic, yet testing remains fundamentally challenging due to open environments, complex scenarios, and the lack of established processes and metrics. Despite extensive research, a gap persists between academic advances and their applicability in industrial practice. To address this, we conduct an interactive rapid review in collaboration with 21 practitioners from a leading automotive company. Practitioners identified 12 key challenges in ADS testing, and prioritised two as the most critical issues, namely approaches to and completeness of testing for End-to-End (E2E) ADS. We analyzed 17 research studies relevant to these two challenges, most of which focus on generating critical testing scenarios, and subsequently assessed their relevance and applicability in practice. Our study provides the first practitioner-driven review and evaluation of current ADS testing research, reveals practical challenges in ADS testing, offers rapid insights for practitioners, and highlights the need for more context-aware, industry-relevant solutions to bridge the gap between research and practice.

Retrieval-augmented generation (RAG) enhances large language models with external knowledge, and tree-based RAG organizes documents into hierarchical indexes to support queries at multiple granularities. However, existing Tree-RAG methods designed for single-document retrieval face critical challenges in scaling to cross-document multi-hop questions: (1) poor distribution adaptability, where $k$-means clustering introduces noise due to rigid distribution assumptions; (2) structural isolation, as tree indexes lack explicit cross-document connections; and (3) coarse abstraction, which obscures fine-grained details. To address these limitations, we propose $Ψ$-RAG, a tree-RAG framework with two key components. First, a hierarchical abstract tree index built through an iterative "merging and collapse" process that adapts to data distributions without a priori assumption. Second, a multi-granular retrieval agent that intelligently interacts with the knowledge base with reorganized queries and an agent-powered hybrid retriever. $Ψ$-RAG supports diverse tasks from token-level question answering to document-level summarization. On cross-document multi-hop QA benchmarks, it outperforms RAPTOR by 25.9% and HippoRAG 2 by 7.4% in average F1 score. Code is available at https://github.com/Newiz430/Psi-RAG.

AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermediate state between steps and inflating end-to-end latency by 3-8x. We argue that this request-level abstraction is fundamentally mismatched to compound AI workloads, and propose a shift to program-level scheduling: treating the entire agent workflow (not individual inference calls) as the first-class schedulable unit. We present SAGA, a distributed scheduler that implements this abstraction through three mechanisms: (1) Agent Execution Graphs that capture workflow structure to predict KV cache reuse across tool-call boundaries, achieving within 1.31x of Bélády's optimal offline policy; (2) session-affinity batching with work stealing that co-locates correlated requests while maintaining global load balance; and (3) Agent Fair Share, a task-completion-time fairness metric with provable bounded-deviation guarantees. On a 64-GPU cluster serving SWE-bench coding agents and WebArena browser tasks, SAGA reduces task completion time by 1.64x (geometric mean, p < 0.001) over vLLM v0.15.1 with prefix caching and affinity routing, while improving GPU memory utilization by 1.22x and achieving 99.2% SLO attainment under multi-tenant interference. These latency gains come at a quantified cost: approximately 30% lower peak throughput than throughput-optimal batch scheduling, a tradeoff appropriate for the latency-sensitive interactive deployments that dominate compound AI usage. Our results demonstrate that workflow-aware scheduling is essential for efficient compound AI serving.

Lissajous confocal laser endomicroscopy (CLE) is a promising solution for high speed in vivo optical biopsy for handheld scenarios. However, Lissajous scanning traces a resonant trajectory and samples only the visited pixels per frame; at high frame rates, many pixels remain unvisited, creating structured holes. In this work, we introduce the first benchmark for high-rate Lissajous CLE, consisting of low-quality video clips paired with high-quality reference images. The reference images are wide-FOV mosaics obtained by stitching stabilized, slow-scan frames of the same tissue, enabling temporally aligned supervision. Using this dataset, we propose MIRA, a lightweight recurrent framework for Lissajous CLE restoration that iteratively aggregates temporal context through feature reuse and displacement alignment. Our experiments demonstrate that MIRA outperforms both lightweight and high-complexity baselines in restoration quality while maintaining a favorable computational efficiency suitable for clinical deployment.

The operational landscape of local Large Language Model (LLM) inference has shifted from lightweight models to datacenter-class weights exceeding 70B parameters, creating profound systems challenges for consumer hardware. This paper presents a systematic empirical analysis of the Nvidia and Apple Silicon ecosystems, specifically characterizing the distinct intra-architecture trade-offs required to deploy these massive models. On the Nvidia Blackwell architecture, we identify a critical "Backend Dichotomy" within the TensorRT-LLM stack: while the new NVFP4 quantization format delivers a 1.6x throughput advantage over optimized BF16 baselines (151 tokens/s vs. 92 tokens/s), realizing this performance requires navigating complex runtime constraints that trade startup latency for generation speed. Furthermore, we characterize the "VRAM Wall" for 70B+ models: on discrete GPUs, users face a destructive choice between aggressive quantization (e.g., Q2) that degrades model intelligence to fit in VRAM, or PCIe-bottlenecked CPU offloading, which reduces throughput by over 90% compared to full-GPU execution. Conversely, Apple's Unified Memory Architecture (UMA) circumvents these bottlenecks, enabling linear scaling for 80B parameter models at practical 4-bit precisions. This architectural divergence extends to operational sustainability, where Apple's SoC design demonstrates up to a 23x advantage in energy efficiency (tokens/joule). We conclude that for consumer-grade inference, the optimal hardware is defined by a complex interplay between compute density (Nvidia) and memory capacity (Apple), moderated by the significant "ecosystem friction" of proprietary quantization workflows.

Leveraging continuous solar energy harvesting at high efficiency, space data centers are envisioned as a promising platform for executing energy-intensive large language models (LLMs). Recognizing this advantage, space and AI conglomerates (e.g., SpaceX, Google) are actively investing in this vision. One key challenge, however, is the efficient distributed deployment of a large-scale LLM in a satellite network due to the limited onboard computing and communication resources. This gives rise to a placement problem that involves partitioning and mapping model components to satellites such that the fundamentally different model architecture and network topology can be reconciled to ensure low-latency token generation. To address this problem, we present the Space Network of Experts (Space-XNet) framework targeting the distributed execution of a popular mixture-of-experts (MoE) model in space. The proposed placement strategies are two-level: (1) layer placement, which assigns MoE layers to satellite subnets; and (2) intra-layer expert placement, which assigns individual experts to satellites associated with the same layer/subnet. For layer placement, we exploit the ring-like communication pattern of autoregressive inference to partition the satellite constellation along the orbiting direction into subnets arranged on a ring, each hosting one MoE layer. Based on this architecture, we formulate and solve an optimization problem for intra-layer expert placement to map experts with heterogeneous activation probabilities onto satellites. The derived strategy reveals an intuitive principle: a frequently activated expert should be mapped to a satellite on a routing path with low expected latency. Experiments over a thousand-satellite constellation show that Space-XNet achieves at least a threefold latency reduction compared with conventional random and ablation-based placement strategies.

Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and content moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.

Complex physical systems, from supersonic turbulence to the macroscopic structure of the universe, are governed by continuous multiscale dynamics. While modern machine learning architectures excel at mapping the high-dimensional observables of these systems, it remains unclear whether they internalize the governing physical laws or merely interpolate discrete statistical correlations. Standard Explainable AI (XAI) architectures, particularly perturbation-based and gradient-saliency methods, rely on pixel-wise perturbations, which generate unphysical artifacts and push inputs off the valid empirical distribution. To resolve this, we introduce a diagnostic framework driven by Constrained Diffusion Decomposition (CDD), a diffusion-based multiscale data decomposition algorithm that enables physically constrained data generation and model evaluation via scale-aware modifications. Applying this framework to a Denoising Diffusion Probabilistic Model (DDPM), we execute deterministic interventions directly within the continuous, CDD-based scale space. We demonstrate that under moderate physical perturbations, the unconstrained generative model exhibits localized structural freezing and non-linear instability rather than continuous PDE-like responses. The network fails to maintain cross-scale continuity, causing the generative trajectory to diverge when pushed into unseen physical states. By synthesizing a continuum of physically coherent states, this scale-informed methodology establishes a controlled test ground to evaluate algorithmic vulnerabilities, providing the rigorous physical constraints necessary for future architectures to respect the multiscale causality of the natural universe.

We present a unique, multitask dataset comprising 143 drug and drug candidate molecules, each evaluated on in vitro, parallel artificial-membrane permeability assays (PAMPA) using six different model membranes. Using this resource, we systematically assess the effectiveness of various molecular descriptors and regression models in predicting passive membrane permeability. The studied models range from simple linear regression to a modern pre-trained transformer architecture. Particular attention is given to the trade-off between predictive performance and model interpretability, highlighting the challenges introduced by machine learning approaches. To our knowledge, this is the most comprehensive study on simultaneous modeling of multiple organ-specific PAMPA membranes to date, offering novel insights into membrane-specific permeability profiles. We found that expert-designed physico-chemical property descriptors are more fitting for a limited sample size permeabilty study than deep learning based representations.

We model utterance production as probabilistic cost-sensitive choice over contextual alternatives, using information-theoretic notions of cost. We distinguish between goal-directed alternatives that realise a fixed communicative intent and goal-agnostic alternatives defined only by contextual plausibility, allowing us to derive speaker- and listener-oriented interpretations of different cost measures. We present a procedure to generate both types of alternative sets using language models. Analysing production choices in open-ended dialogue under both deterministic and probabilistic cost minimisation, we find that surprisal minimisation relative to goal-directed alternatives provides the strongest predictive account under both analyses. By contrast, uniform information density and length-based costs exhibit weaker and less consistent predictive power across conditions. More broadly, our study suggests that alternative-conditioned optimisation with LM-generated alternatives provides a principled framework for studying speaker and listener pressures in naturalistic language production.

Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented generation (RAG) and agentic search. Unlike human users, LLMs are constrained by limited attention budgets and are uniquely vulnerable to noise; misleading or irrelevant information is no longer just a nuisance, but a direct cause of hallucinations and reasoning failures. In this perspective paper, we argue that denoising-maximizing usable evidence density and verifiability within a context window-is becoming the primary bottleneck across the full information access pipeline. We conceptualize this paradigm shift through a four-stage framework of IR challenges: from inaccessible to undiscoverable, to misaligned, and finally to unverifiable. Furthermore, we provide a pipeline-organized taxonomy of signal-to-noise optimization techniques, spanning indexing, retrieval, context engineering, verification, and agentic workflow. We also present research works on information denoising in domains that rely heavily on retrieval such as lifelong assistant, coding agent, deep research, and multimodal understanding.

Energy efficiency has emerged as a vital attribute of software quality, with significant implications for both environmental sustainability and operational costs. However, existing profiling tools operate only at runtime and coarse granularity, typically capturing energy at the process or method level. Such tools fail to expose how small code blocks, such as functions, loops, and conditionals, contribute to energy consumption, preventing developers from reasoning about and comparing the energy efficiency of programming constructs during design-time. To address this gap, we propose EnCoDe, a methodology for fine-grained, design-time energy estimation, with the following key contributions: (1) PowerLens, a novel measurement methodology that achieves reliable sub-millisecond energy readings for small code blocks; (2) Extensive empirical study on code blocks extracted from over 18,000 Python programs, uncovering linear and non-linear relationships between energy consumption and static code features such as structural, complexity, density, and contextual characteristics, resulting in a first-of-its-kind fine-grained dataset; and (3) Predictive modeling, in which machine learning models are trained on these features to accurately estimate and classify block-level energy consumption at design-time. Our results demonstrate stable, reproducible block-level estimations, with regressors achieving R^2 = 0.75 and classifiers achieving 80.6% accuracy in identifying energy hotspots, enabling developers to localize and address inefficient code regions early in the development process without execution.

Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipeline that jointly optimizes reconstruction and generation, enabling direct supervision from generation results to the tokenizer. This contrasts with prior two-stage approaches that train tokenizers and generative models separately. We further investigate leveraging vision foundation models to improve 1D tokenizers for autoregressive modeling. Our autoregressive generative model achieves strong empirical results, including a state-of-the-art FID score of 1.48 without guidance on ImageNet 256x256 generation.

In financial predictions, the performance of machine learning models is often assessed by Rank IC, which is the Spearman rank correlation between the model predictions and the realized asset returns. Despite its wide adoption, most existing models are trained using regression losses or ranking objectives that may not align with Rank IC. We propose LambdaRankIC, a novel learning-to-rank approach that directly optimizes Rank IC. We circumvent the non-differentiability of the ranking operator by deriving the closed-form expression for the lambda gradients induced by the pairwise rank swaps, which enables efficient gradient-based optimization within the LambdaRank framework. We implement LambdaRankIC as a custom objective in XGBoost. Theoretically, we show that our approach optimizes an upper bound on Rank IC. We evaluate the proposed approach on both simulated and real-world financial data. In simulation studies, LambdaRankIC accurately recovers the true ranking structure in noiseless settings and consistently outperforms regression-based and NDCG-oriented ranking methods under low signal-to-noise ratios and heavy-tailed noise regimes. In empirical experiments using real market data, LambdaRankIC achieves the best out-of-sample performance on evaluation metrics commonly used in finance, including Rank IC, ICIR, monthly return, and Sharpe ratio. These results show that directly optimizing Rank IC can yield substantial improvements over conventional learning objectives in financial predictions when the full-order ranking quality is the primary goal.

In federated contextual linear bandits, high data dimensionality incurs prohibitive computation and communication costs: local agents perform $O(d^3)$-time determinant computation and upload $O(d^2)$ parameters, making existing algorithms unscalable, where $d$ is the dimension of data. To relieve these scaling bottlenecks, this paper proposes Federated Sketch Contextual Linear Bandits (FSCLB). On the computation side, FSCLB uses SVD to indirectly obtain the determinant required for communication, eliminating the prohibitive cost of direct determinant calculation and cutting complexity from $O(d^3)$ to $O(l^2d)$ per round, where $l< d$ is the sketch size. On the communication side, FSCLB introduces a double-sketch strategy that reduces both upload and download costs from $O(d^2)$ to $O(ld)$. Naively involving sketch update into federated contextual linear bandits can destroy the local increment and invalidate the asynchronous communication condition; FSCLB solves this by replacing the covariance matrix with the sketch matrix when deciding whether to communicate. Theoretically, FSCLB achieves a regret bound of $\widetilde{O} ((\sqrt{d}+\sqrt{M\varepsilon_l})\sqrt{lT})$, where $\varepsilon_l$ is the upper bounded by the spectral tail of the covariance matrix; when $l$ exceeds the rank of the covariance matrix, the bound simplifies to $\widetilde{O}(\sqrt{ldT})$, matching the optimal no-sketch regret. Experiments on both synthetic and real-world datasets show that FSCLB significantly reduces computational and communication costs by over 90 \% while sacrificing only a negligible amount of cumulative reward.

Recent advances in user modeling make it feasible to conduct open-ended inference over a person's everyday computer use. Despite longstanding visions of systems that deeply understand our actions and the purposes they serve in our lives, existing systems only capture what a person is doing in the moment -- not why they are doing it -- limiting these systems to surface-level support. We introduce striving co-creation, a process for inferring broader life goals from unstructured observations of computer use. Grounded in Activity Theory and Emmons' personal strivings framework, our system progressively constructs a hierarchical representation of a person's activities. Crucially, strivings are difficult to fully resolve from observation alone, as the same action can be driven by many different goals. Our system therefore supports an editing interface that gives people agency over how they are understood by the system, feeding their corrections back into subsequent rounds of striving induction. In a week-long field deployment (N=14), we find that our co-creation process produces strivings that are representative of participants' long-term goals and gives them greater agency than baseline methods.

Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods depend heavily on the distance metric that lets us identify examples in the dataset that are most critical for detecting the anomaly. To optimize the performance of such methods we study and devise a metric learning method that learns the distance metric to reflect best the conditional anomaly pattern.

We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information from the graph as possible. One of the relevant applications for this setting is marketing in social networks, where the marketer aims at finding and taking advantage of the most influential customers. The existing approaches for bandit problems on graphs require either partial or complete knowledge of the graph. In this paper, we do not assume any knowledge of the graph, but we consider a setting where it can be gradually discovered in a sequential and active way. At each round, the learner chooses a node of the graph and the only information it receives is a stochastic set of the nodes that the chosen node is currently influencing. To address this setting, we propose BARE, a bandit strategy for which we prove a regret guarantee that scales with the detectable dimension, a problem dependent quantity that is often much smaller than the number of nodes.

In multi-armed bandits, the most-explored arms are the most informative, while reward maximization typically pulls only the best arm. We study the tradeoff between identifying arm means accurately and accumulating reward, and present an algorithm with regret guarantees that interpolates between the two objectives. We provide both upper and lower bounds and validate empirically.

Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing high-dimensional KPI time series. The scale and cost of incident labelling make supervised approaches impractical, motivating unsupervised anomaly detection robust to context shifts and nonstationarity. We propose \textbf{C-MTAD-GAT} (\emph{Context-aware Multivariate Time-series Anomaly Detection with Graph Attention}), an anomaly detection framework designed to operate as a single shared model across large populations of network elements. The model combines temporal and feature-wise graph attention with lightweight static and dynamic context conditioning and a dual-head decoder for reconstruction and multi-step forecasting. It produces per-element, per-feature anomaly scores, converted to alerts via fully unsupervised thresholds calibrated from validation residuals. On the TELCO dataset released with DC-VAE \cite{garcia2023onemodel}, C-MTAD-GAT improves event-level affiliation and pointwise F1 while generating fewer alarms than prior graph-attention and VAE-based baselines. We then apply the same system to nation-scale radio access and evolved packet core control-plane counter data from a mobile network operator, where it is deployed. Operator feedback indicates the alerts are actionable and support daily monitoring, showing scalability across domains without relying on labelled incidents.

Multi-task learning (MTL) has emerged as a pivotal paradigm in machine learning by leveraging shared structures across multiple related tasks. Despite its empirical success, the development of likelihood-based efficiently solvable algorithms--even for shared linear representations--remains largely underdeveloped, primarily due to the non-convex structure intrinsic to matrix factorization. This paper introduces a first-order algorithm that jointly learns a shared representation and task-specific parameters, with guaranteed efficiency. Notably, it converges in $\widetilde{\mathcal{O}}(1)$ iterations and attains a \emph{near-optimal} estimation error of $\widetilde{\mathcal{O}}(dk/(TN))$, \emph{improving} over existing likelihood-based methods by a factor of $k$, where $d$, $k$, $T$, $N$ denote input dimension, representation dimension, task count, and samples per task, respectively. Our results justify that likelihood-based first-order methods can efficiently solve the MTL problem.

As software systems grow in scale, developers face increasing difficulty in selecting appropriate Application Programming Interfaces (APIs) from numerous options. Efficiently identifying APIs that satisfy functional requirements has become a key challenge. To evaluate the semantic understanding of existing query-based API recommendation methods, this paper constructs Q-ARE (Query-based API Recommendation Evaluation), a dataset based on open-source Java projects from GitHub. Methods and their invocation chains are analyzed to identify third-party APIs directly or indirectly invoked by target methods, recursively expanding multi-level invocations to unify hierarchical call structures into API recommendation target sets. Furthermore, we introduce two metrics: API Call Depth, measuring the invocation distance between a query method and a target API, and Invocation Density, quantifying the proportion of code lines associated with the target API in the invocation chain. Based on Q-ARE, we systematically evaluate several query-based API recommendation methods and general Large Language Models (LLMs). Results show that performance drops significantly as API Call Depth increases and invocation density decreases, indicating that existing methods still struggle with multi-level method invocation structures. Q-ARE and its metrics provide a new benchmark for assessing semantic understanding in API recommendation and offer insights for improving future algorithms.

Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores differences in readers' information needs and comprehension. In health contexts, this limitation is particularly important because misunderstanding scientific information can affect real-world decisions. Large language models (LLMs) offer new opportunities for personalizing PLS, but it remains unclear whether personalization helps, which strategies are most effective, and how to balance personalization with safety. We introduce ReLay, a dataset of 300 participant--PLS pairs from 50 lay participants in both static (expert-written) and interactive (LLM-personalized) settings. ReLay includes user characteristics, health information needs, information-seeking behavior, comprehension outcomes, interaction logs, and quality ratings. We use ReLay to evaluate five LLMs across two personalization methods. Personalization improves comprehension and perceived quality, but it also raises the risk of reinforcing user biases and introducing hallucinations, revealing a trade-off between personalization and safety. These findings highlight the need for personalization methods that are both effective and trustworthy for diverse lay audiences.

Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of principled Riemannian analogs of fundamental building blocks in deep neural networks (DNNs). Among those, Riemannian batch normalization (BN) layers have shown to enhance training stability and improve accuracy. In this paper, we propose BN layers for neural networks on complex domains. The proposed layers have close connections with existing Riemannian BN layers. We derive essential components for practical implementations of BN layers on some complex domains which are less studied in previous works, e.g., the Siegel disk domain. We conduct experiments on radar clutter classification, node classification, and action recognition demonstrating the efficacy of our method.

Real-world time series forecasting faces the fundamental challenge of non-stationary statistical properties, including shifts in mean and variance over time. While reversible instance normalization (RevIN) has shown promise by stationarizing inputs and denormalizing outputs, it relies on the strong assumption that historical and future distributions remain identical. We observe that in many practical applications, distribution shifts follow cyclical patterns that correlate with periodic positions (e.g., seasonal and holiday volatility). To this end, we propose PAMod, a lightweight yet powerful framework that models cyclical distribution shifts via Phase-Amplitude Modulation in the normalized feature space. PAMod learns periodic embeddings to modulate representations: phase modulation captures mean shifts, while amplitude modulation adapts to variance changes. Crucially, we prove mathematically that modulating in normalized space is equivalent to applying dynamic denormalization, offering an elegant unification of distribution adaptation and representation learning. Extensive experiments on twelve real-world benchmarks demonstrate that PAMod achieves state-of-the-art performance with fewer computational resources. Furthermore, our modulation mechanism, as a novel plug-and-play technique, can improve existing time-series forecasting methods with simple integration.

Intelligence Processing Units (IPU) have proven useful for many AI applications. In this paper, we evaluate them within the emerging field of \emph{AI for simulation}, where traditional numerical simulations are supported by artificial intelligence approaches. We focus specifically on a program for training machine learning models supporting a \emph{computational fluid dynamics} application. We use custom TensorFlow provided by the Poplar SDK to adapt the program for the IPU-POD16 platform and investigate its ease of use and performance scalability. Training a model on data from OpenFOAM simulations allows us to get accurate simulation state predictions in test time. We show how to utilize the \emph{popdist} library to overcome a performance bottleneck in feeding training data to the IPU on the host side, achieving up to 34\% speedup. Due to communication overheads, using data parallelism to utilize two IPUs instead of one does not improve the throughput. However, once the intra-IPU costs have been paid, the hardware capabilities for inter-IPU communication allow for good scalability. Increasing the number of IPUs from 2 to 16 improves the throughput from 560.8 to 2805.8 samples/s.

Retrieval-augmented generation (RAG) is vulnerable to prompt injection attacks, in which an adversary inserts malicious documents containing carefully crafted injected prompts into the knowledge database. When a user issues a question targeted by the attack, the RAG system may retrieve these malicious documents, whose injected prompts mislead it into generating attacker-specified answers, thereby compromising the integrity of the RAG system. In this work, we propose CleanBase, a method to detect malicious documents within a knowledge database. Our key insight is that malicious documents crafted for the same attack-targeted questions often exhibit high semantic similarity, as attackers deliberately make them consistent to improve attack success rates. Accordingly, CleanBase constructs a similarity graph over the knowledge database, where each node represents a document and an edge connects two nodes if their semantic similarity--computed using an embedding model--exceeds a statistically determined threshold. Due to their inherent similarity, malicious documents tend to form cliques within this graph. CleanBase detects such cliques and flags the corresponding documents as malicious. We theoretically derive upper bounds on CleanBase's false positive and false negative rates and empirically validate its effectiveness. Experimental results across multiple datasets and prompt injection attacks demonstrate that CleanBase accurately detects malicious documents and effectively safeguards RAG systems. Our source code is available at https://github.com/WeifeiJin/CleanBase.

Federated learning using mobile and Internet of Things devices requires not only the ability to handle heterogeneity of clients' data distributions but also high adaptability to varying communication environments. We propose FedHAW (Federated Learning with Hypergradient-based update of Aggregation Weights) that implements online updates of aggregation weights. FedHAW updates the aggregation weights by using hypergradient, the gradient of the objective function with respect to the weights, which can be calculated with low computational overhead. Simulation results show that the proposed method possesses high generalization performance in heterogeneous environments and high robustness to communication errors.

The coexistence of NR-U and Wi-Fi in unlicensed spectrum introduces a system-level resource coordination problem, where heterogeneous channel access mechanisms lead to a significant imbalance in spectrum utilization and degraded Wi-Fi performance. To address this challenge, we propose a policy-driven deep reinforcement learning (DRL) framework for adaptive TXOP control, in which the coexistence process is formulated as a Markov decision process (MDP) and a deep Q-network (DQN) learns control policies through online interaction. A key contribution is the introduction of a policy layer via reward design, enabling explicit control of system-level tradeoffs among fairness, throughput, and quality of service (QoS). Three policies, namely absolute fairness, moderate fairness, and utility-based fairness, are developed to achieve different operating points. Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared to absolute fairness, moderate fairness improves aggregate throughput by 68.22%, while the utility-based policy further enhances utility by 177.6%. These results demonstrate that policy-driven control provides a flexible and effective solution for managing tradeoffs in heterogeneous coexistence networks.

Learning-based MIMO detection has shown strong empirical performance, yet existing methods typically rely on fixed-depth architectures without explicitly modeling the progressive refinement of symbol estimates. In this paper, we revisit MIMO detection from a flow matching perspective and propose the Soft Graph Diffusion Transformer (SGDiT), which reformulates detection as a noise-level-conditioned denoising process that progressively transforms a Gaussian initialization toward the posterior conditioned on channel observations. An adaptive layer normalization (AdaLN)-conditioned soft graph transformer is employed to parameterize the denoising dynamics, enabling stage-aware information integration between observation and symbol domains. To better align with the discrete nature of symbol detection, we further adopt a cross-entropy-based training objective that directly models bit-wise posterior probabilities, providing a more suitable inductive bias than conventional regression-based formulations. Experimental results across various MIMO system configurations demonstrate that SGDiT achieves competitive bit error rate (BER) performance compared with representative baselines. Furthermore, the proposed model exhibits good generalization capability across different channel conditions. Overall, the SGDiT framework provides an effective and practical approach for neural MIMO detection.

Linking issue reports to the commits that resolve them is essential for software traceability, maintenance, and evolution. Accurate issue-commit links help developers to understand system changes and the rationale behind them. While numerous automated techniques have been proposed, ranging from heuristic and feature-based approaches to modern deep learning and large language model approaches, our goal is to evaluate these techniques to determine which are most effective and efficient. In this study, we revisit several established issue-commit link recovery techniques, including BTLink, EasyLink, FRLink, RCLinker, and Hybrid-Linker, and assess their performance for reranking issue-commit links. We first evaluate different retrieval methods (BM25, BM25L, SBERT-Semantic Search, ANNOY, LSH, HNSW) for their ability to efficiently retrieve relevant commits, reducing the candidate set that must be considered by more computationally expensive models. Using the best retrieval methods, we then investigate the reranking effectiveness of different machine learning-based techniques, including traditional machine learning models, a cross-encoder, and large language models (ChatGPT, Qwen, Gemma, Llama), to refine the reranking of candidate commits and improve precision. Finally, we compare the effectiveness of these techniques. Our results show that dense retrieval methods outperform sparse retrieval approaches in identifying relevant commits and that combining dense and sparse retrieval can improve recall. Additionally, we find that traditional machine learning-based reranking techniques achieve higher performance than LLM-based approaches. Our results highlight that retrieval-based pipelines remain a practical and effective solution for large-scale issue-commit linking, and that simpler models should be carefully considered before adopting computationally expensive LLM-based approaches.

Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Question Answering. However, their robustness to the structure of this input remains a critical, unaddressed question. This paper demonstrates that modern LLMs exhibit a significant vulnerability to the layout of tabular data. Specifically, we show that semantically-invariant permutations of rows and columns - rearrangements that do not alter the table's underlying information - are sometimes sufficient to cause incorrect or inconsistent model outputs. To systematically probe this vulnerability, we introduce Adversarial Table Permutation, a novel, gradient-based attack that efficiently identifies worst-case permutations designed to maximally disrupt model performance. Our extensive experiments demonstrate that ATP significantly degrades the performance of a wide range of LLMs. This reveals a pervasive vulnerability across different model sizes and architectures, including the most recent and popular models. Our findings expose a fundamental weakness in how current LLMs process structured data, underscoring the urgent need to develop permutation-robust models for reliable, real-world applications.

The advancement of generalized deepfake disruption is constrained by the interruption imbalance, a fundamental bottleneck inherent to the generation of universal perturbations. We reveal that conventional static gradient normalization fundamentally struggles to resolve architectural conflicts, causing the optimization to bias towards susceptible models while neglecting resistant ones. We argue that achieving high and uniform effectiveness requires resolving this imbalance by reaching an adaptive equilibrium. We propose the Adaptive Equilibrium Framework (AEF), which employs a dynamic weighting mechanism that utilizes real-time loss feedback to adaptively assign greater interruption weights to the most resistant models. This approach shifts the optimization from an average-case problem to finding a dynamic balance, driving the perturbation to a uniformly effective equilibrium state. Comprehensive experiments validate that AEF achieves a more balanced interruption performance, maintaining a consistent interruption success rate across the evaluated diverse architectures.

The evolution of artificial intelligence (AI) has rendered the boundary between humanity and computational machinery increasingly ambiguous. In the presence of more interwoven relationships within human-machine symbiosis, the very notion of AI-generated information becomes difficult to define, as such information arises not from either humans or machines in isolation, but from their mutual shaping. Therefore, a more pertinent question lies not merely in whether AI has participated, but in how it has participated. In general, the role assumed by AI is often specified, either implicitly or explicitly, in the input prompt, yet becomes less apparent or altogether unobservable when the generated content alone is available. Once detached from the dialogue context, the functional role may no longer be traceable. This study considers the problem of tracing the functional role played by AI in natural language generation. A methodology is proposed to infer the latent role specified by the prompt, embed this role into the content during the probabilistic generation process and subsequently recover the nature of AI participation from the resulting text. Experimentation is conducted under a representative scenario in which AI acts either as an assistive agent that edits human-written content or as a creative agent that generates new content from a brief concept. The experimental results support the validity of the proposed methodology in terms of discrimination between roles, robustness against perturbations and preservation of linguistic quality. We envision that this study may contribute to future research on the ethics of AI with regard to whether AI has been used fairly, transparently and appropriately.

Long-horizon robotic manipulation requires plans that are both logically coherent and geometrically grounded. Existing Vision-Language-Action policies usually hide planning in latent states or expose only one modality: text-only chain-of-thought encodes causal order but misses spatial constraints, while visual prediction provides geometric cues but often remains local and semantically underconstrained. We introduce Interleaved Vision--Language Reasoning (IVLR), a policy framework built around \trace{}, an explicit intermediate representation that alternates textual subgoals with visual keyframes over the full task horizon. At test time, a single native multimodal transformer self-generates this global semantic-geometric trace from the initial observation and instruction, caches it, and conditions a closed-loop action decoder on the trace, original instruction, and current observation. Because standard robot datasets lack such traces, we construct pseudo-supervision by temporally segmenting demonstrations and captioning each stage with a vision-language model. Across simulated benchmarks for long-horizon manipulation and visual distribution shift, \method{} reaches 95.5\% average success on LIBERO, including 92.4\% on LIBERO-Long, and 59.4\% overall success on SimplerEnv-WidowX. Ablations show that both modalities are necessary: without traces, LIBERO-Long success drops to 37.7\%; text-only and vision-only traces reach 62.0\% and 68.4\%, while the full interleaved trace reaches 92.4\%. Stress tests with execution perturbations and masked trace content show moderate degradation, suggesting that the trace can tolerate local corruption and moderate execution drift, but remains limited under stale or incorrect global plans.

Concerns with the safety and reliability of applying large-language models (LLMs) in unpredictable real-world applications motivate this study, which examines how task phrasing can lead to presumptions in LLMs, making it difficult for them to adapt when the task deviates from these assumptions. We investigated the impact of these presumptions on the performance of LLMs using the iterated prisoner's dilemma as a case study. Our experiments reveal that LLMs are susceptible to presumptions when making decisions even with reasoning steps. However, when the task phrasing was neutral, the models demonstrated logical reasoning without much presumptions. These findings highlight the importance of proper task phrasing to reduce the risk of presumptions in LLMs.

Mode collapse is a persistent challenge in generative modeling and appears in autoregressive text generation as behaviors ranging from explicit looping to gradual loss of diversity and premature trajectory convergence. We take a dynamical-systems view and reinterpret mode collapse as reduced state-space accessibility caused by *geometric collapse*: during generation, the model's internal trajectory becomes confined to a low-dimensional region of its representation space. This implies mode collapse is not purely a token-level phenomenon and cannot be reliably solved by symbolic constraints or probability-only decoding heuristics. Guided by this perspective, we propose *Reinforced Mode Regulation* (RMR), a lightweight, online state-space intervention that regulates dominant self-reinforcing directions in the Transformer value cache (implemented as low-rank damping). Across multiple large language models, RMR substantially reduces mode collapse and enables stable, high-quality generation at extremely low entropy rates (down to 0.8 nats/step), whereas standard decoding typically collapses near 2.0 nats/step.

Code generation, which aims to automatically generate source code from given programming requirements, has the potential to substantially improve software development efficiency. With the rapid advancement of large language models (LLMs), LLM-based code generation has attracted widespread attention from both academia and industry. However, as programming requirements become increasingly complex, existing LLMs still exhibit notable performance limitations. To address this challenge, recent studies have proposed training-based curriculum reinforcement learning (CRL) strategies to improve LLM code generation performance. Despite their effectiveness, existing CRL approaches suffer from several limitations, including misaligned requirement difficulty perception, the absence of requirement difficulty optimization, and suboptimal curriculum sampling strategies. In CRL-based code generation, programming requirements serve as the sole input to the model, making their quality and difficulty critical to training effectiveness. Motivated by insights from software requirements engineering, we propose RECRL, a novel requirement-aware curriculum reinforcement learning framework for enhancing LLM-based code generation. RECRL automatically perceives model-specific requirement difficulty, optimizes challenging requirements to improve training data utilization, and employs an adaptive curriculum sampling strategy to construct training batches with smoothly varying difficulty. Extensive experiments on five state-of-the-art LLMs across five widely-used code generation benchmarks by comparing with five state-of-the-art baselines, demonstrate the significant effectiveness of RECRL. For example, RECRL achieves an average Pass@1 improvement of 1.23%-5.62% over all state-of-the-art baselines.

Online Conformal Prediction (CP) struggles to balance temporal adaptability and structural stability. Feedback-driven methods (e.g., Adaptive Conformal Inference (ACI)) suffer from systemic marginal under-coverage and high interval variance during abrupt shifts, while temporally discounted Bayesian CP suffers from severe structural lag and uncalibrated interval bloat. We propose State-Adaptive Bayesian Conformal Prediction (SA-BCP) to achieve optimal spatio-temporal decoupling. By gating long-term temporal inertia with spatial kernel-density evidence, SA-BCP proactively expands intervals for recognized historical regimes while maintaining tight efficiency during stable states. We rigorously prove this mechanism's optimality, identifying a minimax bias-variance tradeoff governed by an evidence threshold $K$. Extensive benchmarks on volatile financial datasets (2016--2026), including AMD, Gold, and GBP/USD, demonstrate that SA-BCP consistently minimizes the strictly proper Winkler score across diverse confidence levels. Specifically, SA-BCP resolves the systematic under-coverage inherent to ACI variants while simultaneously reducing the uncalibrated interval bloat of Bayesian CP by 10\% to 37\% under high-confidence requests. By elegantly navigating this tradeoff, SA-BCP achieves an optimal balance between conditional reliability and predictive efficiency.

Although recent video-to-audio (V2A) models excelled at synthesizing semantically plausible sounds from visual inputs, they do not explicitly model room-acoustic effects such as reverberation or room impulse responses (RIRs), and thus offer limited controllability over these effects. However, we hypothesize that such V2A models implicitly have semantic knowledge of the relationship between spatial audio and the corresponding vision cues. In this paper, we revisit a V2A model for the sake of the above, and propose the way to utilize the pretrained model as prior for physically grounded room-acoustic processing. Based on one of the state-of-the-art V2A models, MMAudio, we propose MMAudioReverbs that is a unified framework dealing with i) dereverberation and ii) room impulse response (RIR) estimation without network architectural modification, and fine-tuned on a small dataset. Experimental results showed that audio and visual cues respectively have advantage depending on the type of physical room acoustics. It implies that foundation V2A models can be used for physically grounded room-acoustic analysis.

Reinforcement learning (RL) has significantly advanced the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. Yet effective training remains challenging, as sparse, outcome-only rewards make it difficult to assign credit to individual steps in an agent's action trajectory. A common remedy is to introduce dense intermediate supervision, such as process reward models or auxiliary self-supervised signals, but this increases supervision and tuning complexity and often generalizes poorly across tasks and domains. This paper presents AEM, a supervision-free credit assignment method that adaptively modulates entropy dynamics during RL training to achieve a more effective exploration-exploitation trade-off. Theoretically, we elevate entropy analysis from the token level to the response level to reduce token sampling variance and show that entropy drift under natural gradients is intrinsically governed by the product of the advantage and the relative response surprisal. Specifically, we derive a practical proxy to reshape training dynamics, enabling a natural transition from exploration to exploitation. Extensive experiments across various benchmarks and models ranging from 1.5B to 32B parameters demonstrate the effectiveness of AEM, including a notable 1.4 percent gain when integrated into a state-of-the-art baseline on the highly challenging SWE-bench-Verified benchmark.

Agent skills -- structured packages of instructions, scripts, and references that augment a large language model (LLM) without modifying the model itself -- have moved from convenience to first-class deployment artifact. The runtime that loads them inherits the same problem package managers and operating systems have always faced: a piece of content claims a behavior; the runtime must decide whether to believe it. We argue this paper's central thesis up front: a skill is \emph{untrusted code} until it is verified, and the runtime that loads it must enforce that default rather than infer trust from a signature, a clearance, or a registry of origin. Without skill verification, a human-in-the-loop (HITL) gate must fire on every irreversible call -- which is operationally untenable and degrades into rubber-stamping at any non-trivial scale. With skill verification treated as a separate, gated process, HITL fires only for what is unverified, and the system becomes sustainable. We give a trust schema (§\ref{sec:schema}) that includes an explicit verification level on every skill manifest; a capability gate (§\ref{sec:gate}) whose HITL policy is a function of that verification level; a \emph{biconditional} correctness criterion (§\ref{sec:biconditional}) that any candidate verification procedure must satisfy on an adversarial-ensemble exercise (§\ref{sec:eval}); and a portable runtime profile (§\ref{sec:guidelines}) with ten normative guidelines abstracted from a working open-source reference implementation \cite{metere2026enclawed}. The contribution is harness- and model-agnostic; nothing here requires retraining, fine-tuning, or proprietary infrastructure.

In wireless communications, recovering the optimal solution to the multiple-input multiple-output (MIMO) detection problem is NP-hard. Obtaining high-quality suboptimal solutions with a favorable performance-complexity trade-off is particularly challenging in under-determined systems with $N_t$ transmit antennas and $N_r < N_t$ receive antennas. Recent diffusion-based MIMO detectors have shown promise, but they require extensive sampling iterations at inference time, and their performance degrades in under-determined scenarios. We propose GD4, a graph-based discrete denoising diffusion method for MIMO detection. Unlike existing diffusion-based detectors that operate in a continuous relaxed space, GD4 performs denoising directly in the discrete symbol space and enables fast inference with one or a few denoising evaluations. Numerical results show that, under a similar inference-time compute budget, GD4 produces higher-quality suboptimal solutions than existing diffusion-based detectors and some widely used classical baseline including box-constrained Babai point and the $K$-best box-constrained randomized Klein-Babai point in both under-determined and overdetermined settings.

Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder practical deployment. Binarization can compress weights to 1 bit, fundamentally lowering compute and bandwidth cost. However, existing methods cannot address activation heavy tails and thus must keep activations in high precision, preventing true end-to-end acceleration. To overcome this limitation, we propose BWLA (Binarized Weights and Low-bit Activations), the first post-training quantization framework that preserves high accuracy while achieving 1-bit weight quantization together with low-bit activations (e.g., 6 bits). The Orthogonal-Kronecker Transformation (OKT) learns an orthogonal mapping via EM minimization, converting unimodal weights into symmetric bimodal forms while suppressing activation tails and incoherence. The Proximal SVD Projection (PSP) then performs lightweight low-rank refinement through proximal SVD projection, further enhancing quantizability with minimal overhead. On Qwen3-32B, BWLA reaches a Wikitext2 perplexity of 11.92 under 6-bit activations (vs. 38 from SOTA), improves five zero-shot tasks by more than 70%, and delivers 3.26 times inference speedup, demonstrating strong potential for real-world LLM compression and acceleration.

Large language models (LLMs) show promise in radiology but their deployment is limited by computational requirements that preclude use in resource-constrained clinical environments. We investigate whether small language models (SLMs) of 3-4 billion parameters can achieve strong multi-task radiology performance through LoRA fine-tuning, enabling deployment on consumer-grade CPUs. We train Qwen2.5-3B-Instruct and Qwen3-4B on 162K samples spanning 9 radiology tasks - RADS classification across 10 systems, impression generation, temporal comparison, radiology NLI, NER, abnormality detection, N/M staging, and radiology Q&A - compiled from 12 public datasets. Both models are evaluated on up to 500 held-out test samples per task with standardized metrics. Our key findings are: (1) LoRA fine-tuning dramatically improves performance over zero-shot baselines (RADS accuracy +53%, NLI +60%, N-staging +89%); (2) the two models exhibit complementary strengths - Qwen2.5 excels at structured generation tasks while Qwen3 dominates extractive tasks; (3) a task-outed oracle ensemble combining both models achieves the best performance across all tasks; (4) few-shot prompting with fine-tuned models hurts performance, demonstrating that LoRA adaptation is more effective than in-context learning for specialized domains; and (5) models can be quantized to GGUF format (~1.8-2.4GB) for CPU deployment at 4-8 tokens/second on consumer hardware. Our work demonstrates that small, efficiently fine-tuned models - which we collectively call RadLite - can serve as practical multi-task radiology AI assistants deployable entirely on consumer hardware without GPU requirements.

Evaluating the true forecasting ability of AI agents requires environments resistant to overfitting, free from centralized trust, and grounded in incentive-compatible scoring. Existing benchmarks either rely on static datasets vulnerable to training-data contamination, or measure trading PnL -- a metric conflating predictive accuracy with timing, sizing, and risk appetite. We introduce Foresight Arena, the first permissionless, on-chain benchmark for evaluating AI forecasting agents on real-world prediction markets. Agents submit probabilistic forecasts on binary Polymarket markets via a commit-reveal protocol enforced by Solidity smart contracts on Polygon PoS; outcomes are resolved trustlessly through the Gnosis Conditional Token Framework. Performance is measured by the Brier Score and a novel Alpha Score -- proper scoring rules that incentivize honest probability reporting and isolate predictive edge over market consensus. We provide a formal analysis: closed-form variance for per-market Alpha, the connection to Murphy's classical Brier decomposition, and a power analysis characterizing the number of rounds required to reliably distinguish agents of different skill levels. We show that detecting a true edge of $α^* = 0.02$ at 80% power requires approximately 350 resolved binary predictions (50 rounds of 7 markets), while $α^* = 0.01$ requires four times more. We complement these analytical results with a 50-round live evaluation of five frontier LLM agents plus a random baseline. Murphy decomposition distinguishes well-calibrated agents from market-tracking agents that fail through reduced resolution. All smart contracts and evaluation infrastructure are open-source.

Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multiple models and selecting the most probable label. This idea has been naturally extended to large language models (LLMs), yielding improved performance but incurring substantial computational cost. This inefficiency stems from directly applying conventional ensemble implementation to LLMs, which require a separate forward pass for each model to explicitly compute the ensemble distribution. In this paper, we propose the Mixture-model-like Ensemble (ME). By reinterpreting the ensemble as a mixture model, ME stochastically selects a single model at each step to generate the next token, thereby avoiding the need to explicitly compute the full ensemble distribution. ME is mathematically equivalent to sampling from the ensemble distribution, but requires invoking only one model, making it 1.78x-2.68x faster than conventional ensemble. Furthermore, this perspective connects LLM ensembling and token-level routing methods, suggesting that LLM ensembling is a special case of routing methods. Our findings open new avenues for efficient LLM ensembling and motivate further exploration of token-level routing strategies for LLMs. Our code is available at https://github.com/jialefu/Mixture-model-like-Ensemble/.

Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp mathematical correspondence between hierarchical decision trees and diffusion processes in appropriate limiting regimes. Our unification reveals a shared optimization principle: \emph{Global Trajectory Score Matching (GTSM)}, for which gradient boosting (in an idealized version) is asymptotically optimal. We underscore the conceptual value of our work through two key practical instantiations: \treeflow, which achieves competitive generation quality on tabular data with higher fidelity and a 2\times computational speedup, and \dsmtree, a novel distillation method that transfers hierarchical decision logic into neural networks, matching teacher performance within 2\% on many benchmarks.

Ensuring the reliability of the Rust compiler is of paramount importance, given increasing adoption of Rust for critical systems development, due to its emphasis on memory and thread safety. However, generating valid test programs for the Rust compiler poses significant challenges, given Rust's complex syntax and strict requirements. With the growing popularity of large language models (LLMs), much research in software testing has explored using LLMs to generate test cases. Still, directly using LLMs to generate Rust programs often results in a large number of invalid test cases. Existing studies have indicated that test cases triggering historical compiler bugs can assist in software testing. Our investigation into Rust compiler bug issues supports this observation. Inspired by existing work and our empirical research, we introduce a bracket-based masking and filling strategy called clozeMask. The clozeMask strategy involves extracting test code from historical issue reports, identifying and masking code snippets with specific structures, and using an LLM to fill in the masked portions for synthesizing new test programs. This approach harnesses the generative capabilities of LLMs while retaining the ability to trigger Rust compiler bugs. It enables comprehensive testing of the compiler's behavior, particularly exploring edge cases. We implemented our approach as a prototype CLOZEMASTER. CLOZEMASTER has identified 27 confirmed bugs for rustc and mrustc, of which 10 have been fixed by developers. Furthermore, our experimental results indicate that CLOZEMASTER outperforms existing fuzzers in terms of code coverage and effectiveness.

World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning. However, current world model research is often dominated by three partially separated routes: 2D video-generative models that emphasize visual future synthesis, 3D scene-centric models that emphasize spatial reconstruction, and JEPA-like latent models that emphasize abstract predictive representations. While each route has made important progress, they still struggle to provide physically reliable, action-controllable, and long-horizon stable predictions for embodied decision making. In this paper, we argue that the bottleneck of world models is no longer only whether they can generate realistic futures, but whether those futures are physically meaningful and useful for action. We propose \emph{Hamiltonian World Models} as a physically grounded perspective on world modeling. The key idea is to encode observations into a structured latent phase space, evolve the latent state through Hamiltonian-inspired dynamics with control, dissipation, and residual terms, decode the predicted trajectory into future observations, and use the resulting rollouts for planning. We discuss how Hamiltonian structure may improve interpretability, data efficiency, and long-horizon stability, while also noting practical challenges in real-world robotic scenes involving friction, contact, non-conservative forces, and deformable objects.

A multi-agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings, but naively merged calls silently degrade quality through tool loss and prompt compression. We present Agent Capsules, an adaptive execution runtime that treats multi-agent pipeline execution as an optimization problem with empirical quality constraints. The runtime instruments coordination overhead per group, scores composition opportunity, selects among three compound execution strategies, and gates every mode switch on rolling-mean output quality. A controlled negative result confirms that injecting more context into a merged call worsens compression rather than relieving it, so the framework's escalation ladder (standard, then two-phase, then sequential) recovers quality by moving toward per-agent dispatch rather than by rewriting merged prompts. On LLM-judged quality, the controller matches a hand-tuned oracle on every measured (model, group, mode) cell: routing compound whenever the oracle would, and reverting to fine whenever quality would fail the floor, without per-model configuration. Against a hand-crafted LangGraph implementation of a 14-agent competitive intelligence pipeline, Agent Capsules uses 51% fewer fine-mode input tokens and 42% fewer compound-mode input tokens, at +0.020 and +0.017 quality respectively. Against a DSPy implementation of a 5-agent due diligence pipeline, the framework uses 19% fewer tokens than uncompiled DSPy at quality parity, and 68% fewer tokens than MIPROv2 at +0.052 quality. Even before compound mode fires, the runtime delivers efficiency through automatic policy resolution, cache-aligned prompts, and topology-aware context injection, matching both hand-tuned and compile-time baselines without training data or per-pipeline engineering.

Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however, scalable learning in deep recurrent architectures with sparse connectivity remains a major challenge. In this work, we propose a structured multi-layer recurrent SNN architecture composed of locally dense recurrent layers augmented with sparse small-world long-range projections to a readout population. The long-range connectivity is largely fixed, preserving routing efficiency and hardware scalability, while synaptic adaptation is performed using strictly local plasticity mechanisms. To enable supervised learning without backpropagation or surrogate gradients, we introduce a biologically motivated learning framework that combines: (i) population-based winner-take-all (WTA) teaching signals at the output layer, (ii) fixed random broadcast alignment feedback pathways, and (iii) low-dimensional modulatory neuron populations that gate synaptic updates through three-factor learning rules with eligibility traces. This design supports deep recurrent computation with sparse global communication and purely local synaptic updates. We analyze the algorithmic properties, computational complexity, and hardware feasibility of the proposed approach, and demonstrate stable learning and competitive performance on benchmark classification tasks. The results highlight the potential of structured recurrence and neuromodulatory learning to enable scalable, hardware-compatible SNN training beyond gradient-based methods.

Table Retrieval (TR) has traditionally been formulated as an ad-hoc retrieval problem, where relevance is primarily determined by topical semantic similarity. With the growing adoption of LLM-based agentic systems, access to structured data is increasingly instruction-driven, where relevance is conditional on explicit content and schema constraints rather than topical similarity alone. We therefore formalize Instruction-Following Table Retrieval (IFTR), a new task that requires models to jointly satisfy topical relevance and fine-grained instruction constraints. We identify two core challenges in IFTR: (i) sensitivity to content scope, such as inclusion and exclusion constraints, and (ii) awareness of schema-grounded requirements, including column semantics and representation granularity--capabilities largely absent in existing retrievers. To support systematic evaluation, we introduce FollowTable, the first large-scale benchmark for IFTR, constructed via a taxonomy-driven annotation pipeline. We further propose a new metric, termed the Instruction Responsiveness Score, to evaluate whether retrieval rankings consistently adapt to user instructions relative to a topic-only baseline. Our results indicate that existing retrieval models struggle to follow fine-grained instructions over tabular data. In particular, they exhibit systematic biases toward surface-level semantic cues and remain limited in handling schema-grounded constraints, highlighting substantial room for future improvements.

Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many more grid cells than temporal observations per cell. The Causal Space-Time Stencil Learning (CaStLe) meta-algorithm was developed to address that niche under space-time locality and stationarity assumptions, but it is currently limited to univariate analyses. In this work, we present M-CaStLe. M-CaStLe generalizes the local embedding and parent-identification phases of CaStLe to jointly model local within-variable and cross-variable space-time causal structures in gridded data. Like CaStLe, by constraining candidate parents to a constant-size space-time neighborhood and pooling spatial replicates, M-CaStLe increases effective sample size to make discovery tractable in high-dimensional settings. We further decompose the resulting multivariate stencil graph into reaction and spatial graphs to aid interpretation in complex settings. We study M-CaStLe in four settings: a multivariate space-time vector autoregression benchmark with known ground truth, an advective-diffusive-reaction partial differential equation verification problem with derived physical reference structure, an atmospheric chemistry case study in a low-temporal-sample regime, and an El Niño Southern Oscillation study on reanalysis data, identifying phase-dependent ocean--atmosphere coupling. Across these settings, M-CaStLe more accurately recovers multivariate causal structure in controlled settings and identifies important physical dynamics in real-world case studies. Overall, M-CaStLe advances causal discovery for multivariate space-time systems while retaining interpretability at the grid level.

We present Mesh Field Theory (MeshFT) and its neural realization, MeshFT-Net: a structure-preserving framework for mesh-based continuum physics that cleanly separates the physics' topological structure from its metric structure. Imposing minimal physical principles (locality, permutation equivariance, orientation covariance, and energy balance/dissipation inequality), we prove a reduction theorem for mesh-based physics. Under these conditions, the physical dynamics admit a local factorization into a port-Hamiltonian form: the conservative interconnection is fixed uniquely by mesh topology, whereas metric effects enter only through constitutive relations and dissipation. This reduction clarifies what must be fixed and what should be learned, directly informing MeshFT-Net's design. Across evaluations on analytic and realistic datasets, physics-consistency tests, and out-of-distribution validation, MeshFT-Net achieves near-zero energy drift and strong physical fidelity (correct dispersion and momentum conservation) along with robust extrapolation and high data efficiency. By eliminating non-physical degrees of freedom and learning only metric-dependent structure, MeshFT provides a principled inductive bias for stable, faithful, and data-efficient learning-based physical simulation.

Reinforcement learning (RL) in large environments often suffers from severe computational bottlenecks, as conventional regret minimization algorithms require repeated, costly calls to planning and statistical estimation oracles. While recent advances have explored offline oracle-efficient algorithms, their computational complexity typically scales with the cardinality of the state and action spaces, rendering them intractable for large-scale or continuous environments. In this paper, we address this fundamental limitation by studying offline oracle-efficient episodic RL through the lens of log-barrier and log-determinant regularization. Specifically, for tabular Markov Decision Processes (MDPs), we propose a novel algorithm that achieves the optimal $\tilde{O}(\sqrt{T})$ regret bound while requiring only $O(H\log\log T)$ calls to both the offline statistical estimation and planning oracles when $T$ is known and $O(H\log T)$ calls when $T$ is unknown. Crucially, this oracle complexity is entirely independent of the size of the state and action spaces. This strict independence drastically reduces the planning oracle complexity, representing a substantial improvement over existing offline oracle-efficient algorithms (Qian et al., 2024). Furthermore, we demonstrate the versatility of our framework by generalizing the algorithm to linear MDPs featuring infinite state spaces and arbitrary action spaces. We prove that this generalized approach successfully attains meaningful sub-linear regret. Consequently, our work yields the first doubly oracle-efficient (i.e., efficient with respect to both statistical estimation and policy optimization) regret minimization algorithm capable of solving MDPs with infinite state and action spaces, significantly expanding the boundaries of computationally tractable RL.

DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods for conventional vision-language models (VLMs) fail to preserve textual fidelity due to improper compression mechanisms. By analyzing the decoding process of DeepSeek-OCR, we find that a distinct two-stage reading trajectory: the model initially prioritizes the majority of high-norm tokens, then subsequently redistributes its attention to the remaining ones. Motivated by this insight, we propose RTPrune, a two-stage token pruning method tailored for DeepSeek-OCR. In the first stage, we prioritize high-norm visual tokens that capture salient textual and structural information. In the second stage, the remaining tokens are paired and merged based on optimal transport theory to achieve efficient feature aggregation. We further introduce a dynamic pruning ratio that adapts to token similarity and textual density for OCR tasks, enabling a better efficiency-accuracy trade-off. Extensive experiments demonstrate state-of-the-art performance, as evidenced by 99.47% accuracy and 1.23$\times$ faster prefill on OmniDocBench, achieved with 84.25% token retention when applied to DeepSeek-OCR-Large.

We present \textit{CluProp}, a novel framework that reimagines varied-density clustering in high-dimensional spaces as a label propagation process over neighborhood graphs. Our approach formally bridges the gap between density-based clustering and graph connectivity, leveraging efficient propagation mechanisms from network science to mitigate the parameter sensitivity inherent in traditional density-based methods. Specifically, we introduce a deterministic density-based propagation strategy to ensure scalable neighborhood identification. The framework is agnostic to the choice of distance metric and exhibits superior performance on large-scale data, processing millions of points in minutes while consistently outperforming existing baselines in accuracy.

Physics-Informed Neural Networks have become a powerful mesh-free method for solving partial differential equations, but their performance is often limited by spectral bias. Specifically, in standard MLPs used in PINNs, the global parameter coupling causes the model to prioritize learning low-frequency components, resulting in slow convergence for high-frequency details. To overcome this limitation, we introduce the Physics-Informed Local Implicit Representation (PILIR). Our approach separates the global physical domain into a discrete latent feature space and a continuous generative decoder. By using a learnable grid to encode explicit spatial locality, PILIR can capture high-frequency details locally, preventing dilution by global patterns. A generative neural operator then synthesizes these local latent features into continuous physical fields, allowing accurate reconstruction of fine-scale structures. Experiments on a range of challenging PDEs show that PILIR effectively mitigates spectral bias, thereby boosting the convergence of high-frequency details and achieving superior accuracy compared to state-of-the-art methods.

The delivery of traditional substance education has remained problematic due to challenges in scalability, personalization, and the currency of information in a rapidly evolving substance use landscape. While artificial intelligence (AI) offers a promising frontier for enhancing educational delivery, its application in providing real-time, authoritative substance use education remains largely underexplored. We built an agentic-based AI web application that combined Drug Enforcement Administration records with peer-reviewed literature in real-time to provide transparent context-sensitive substance use education. The system uses retrieval-augmented generation with a carefully filtered corpus of 102 documents and dynamic PubMed queries. Document storage was semantically chunked and placed in a vector representation in order to be easily retrieved. We conducted an expert evaluation study in which a panel of five subject matter experts generated 30 domain-specific questions, and two independent raters assessed 90 system interactions (30 primary questions plus two contextual follow-ups each) using a five-point Likert scale across four criteria: factual accuracy, citation quality, contextual coherence, and regulatory appropriateness. Mean ratings ranged from 4.18 to 4.35 across the four criteria (overall category range: 4.05-4.52), with substantial inter-rater agreement (Cohen's kappa = 0.78). These findings suggest that agentic AI architectures integrating authoritative regulatory sources with real-time scientific literature represent a promising direction for scalable, accurate, and verifiable health education delivery, warranting further evaluation through longitudinal user studies.

Large Language Models (LLMs) are increasingly deployed to generate code for human-centered applications where demographic fairness is critical. However, existing evaluations focus almost exclusively on functional correctness, leaving social bias in LLM-generated code largely unexamined. Extending our prior work on Solar, we conduct a comprehensive empirical study using SocialBias-Bench, a benchmark of 343 real-world coding tasks spanning seven demographic dimensions. We evaluate four prominent LLMs and find severe bias across all models, with Code Bias Scores reaching up to 60.58%. We further show that standard prompt-level interventions, such as Chain-of-Thought reasoning and fairness persona assignment, inadvertently amplify bias rather than reduce it. We then investigate whether structured multi-agent software process frameworks can improve fairness, finding that structured pipelines reduce bias when early roles correctly scope what the code should and should not consider. However, adding explicit fairness instructions to all agent roles produces worse outcomes than providing none, suggesting that diffused responsibility goes unaddressed. To address these limitations, we propose the Fairness Monitor Agent (FMA), a modular component that plugs into any existing code generation pipeline without modifying it. FMA analyzes the task description to determine which attributes should be considered or restricted, then detects and corrects violations through an iterative review process, without requiring an executable test suite. Evaluated on all 343 tasks, FMA reduces bias by 65.1% compared to a developer agent alone and improves functional correctness from 75.80% to 83.97%, outperforming all other studied approaches.

Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diversity due to the over-incentivization of positive rewards. Although methods like Negative Sample Reinforcement (NSR) mitigate this issue by upweighting penalty from negative samples, they may suppress the semantic distributions shared between positive and negative responses. To boost reasoning ability without losing diversity, this paper proposes negative sample projection Residual Reinforcement Learning (ResRL) that decouples similar semantic distributions among positive and negative responses. We theoretically link Lazy Likelihood Displacement (LLD) to negative-positive head-gradient interference and derive a single-forward proxy that upper-bounds representation alignment to guide conservative advantage reweighting. ResRL then projects negative-token hidden representations onto an SVD-based low-rank positive subspace and uses projection residuals to modulate negative gradients, improving reasoning while preserving diversity and outperforming strong baselines on average across twelve benchmarks spanning Mathematics, Code, Agent Tasks, and Function Calling. Notably, ResRL surpasses NSR on mathematical reasoning by 9.4\% in Avg@16 and 7.0\% in Pass@128. Code is available at https://github.com/1229095296/ResRL.git.

Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overlook the potential causal influences of node features on edge features, leading to a loss of relevant prior information. In this work, we present an empirical exploration using the Causal Edge Classification Framework (CECF). Unlike conventional causal inference methods, CECF is the first framework to apply causal inference principles to the edge classification task and to explore modeling edge features as a high-dimensional treatment within a causal framework. Based on the node embedding of Graph Neural Network (GNN), CECF seeks to learn a balanced representation of high-dimensional edge features by mitigating the potential influence of node features. Then, a cross-attention network captures the complex dependencies between node and edge features for final edge classification.Extensive experiments demonstrate that CECF not only achieves superior performance but also serves as a flexible, plug-and-play enhancement for existing methods.We also provide empirical analyses, offering insights into when and how this high-dimensional causal modeling framework works for the edge classification.

In line with the Global Communication Plan 2025, we have pursued the development of multilingual translation technologies to realize a language-barrier-free experience at Expo 2025 Osaka. Our work includes the advancement of simultaneous interpretation systems emphasizing high translation quality and low latency. Key achievements include chunk-based input segmentation, context-aware translation, and multi-engine machine translation technologies. Through demonstration deployments and collaboration with private companies, our technologies have led to real-world applications, with several services and systems showcased at Expo 2025 Osaka.

In this paper, we propose GaMMA, a state-of-the-art (SoTA) large multimodal model (LMM) designed to achieve comprehensive musical content understanding. GaMMA inherits the streamlined encoder-decoder design of LLaVA, enabling effective cross-modal learning between music and language. By incorporating audio encoders in a mixture-of-experts manner, GaMMA effectively unifies both time-series and non-time-series music understanding tasks within one set of parameters. Our approach combines carefully curated datasets at scale with a progressive training pipeline, effectively pushing the boundaries of music understanding via pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL). To comprehensively assess both temporal and non-temporal capability of music LMMs, we introduce MusicBench, the largest music-oriented benchmark, comprising 3,739 human-curated multiple-choice questions covering diverse aspects of musical understanding. Extensive experiments demonstrate that GaMMA establishes new SoTA in the music domain, achieving 79.1% accuracy on MuchoMusic, 79.3% on MusicBench-Temporal, and 81.3% on MusicBench-Global, consistently outperforming previous methods.

Centralized multimodal learning commonly compresses language, acoustic, and visual signals into a single fused representation for prediction. While effective, this paradigm suffers from two limitations: modality dominance, where optimization gravitates towards the path of least resistance, ignoring weaker but informative modalities, and spurious modality coupling, where models overfit to incidental cross-modal correlations. To address these, we propose Group Cognition Learning (GCL), a governed collaboration paradigm that applies a two-stage protocol after modality-specific encoding. In Stage 1 (Selective Interaction), a Routing Agent proposes directed interaction routes, and an Auditing Agent assigns sample-wise gates to emphasize exchanges that yield positive marginal predictive gain while suppressing redundant coupling. In Stage 2 (Consensus Formation), a Public-Factor Agent maintains an explicit shared factor, and an Aggregation Agent produces the final prediction through contribution-aware weighting while keeping each modality representation as a specialization channel. Extensive experiments on CMU-MOSI, CMU-MOSEI, and MIntRec demonstrate that GCL mitigates dominance and coupling, establishing state-of-the-art results across both regression and classification benchmarks. Analysis experiments further demonstrate the effectiveness of the design.

We study how large language models can be used to evolve inventory policies in online, non-stationary environments. Our work is motivated by recent advances in LLM-based evolutionary search, such as AlphaEvolve, which demonstrates strong performance for static and highly structured problems such as mathematical discovery, but is not directly suited to online dynamic inventory settings. To this end, we propose AlphaInventory, an end-to-end inventory-policy evolution and inference framework grounded in confidence-interval-based certification. The framework trains a large language model using reinforcement learning, incorporates demand data as well as numerical and textual features beyond demand, and generates white-box inventory policy with statistical safety guarantees for deployment in future periods. We further introduce a unified theoretical interface that connects training, inference, and deployment. This allows us to characterize the probability that the AlphaInventory evolves a statistically safe and improved policy, and to quantify the deployment gap relative to the oracle-safe benchmark. Tested on both synthetic data and real-world retail data, AlphaInventory outperforms classical inventory policies and deep learning based methods. In canonical inventory settings, it evolves new policies that improve upon existing benchmarks.

High-capacity associative memories based on Kernel Logistic Regression (KLR) exhibit strong storage capabilities, but the dynamical and geometric mechanisms underlying their stability remain poorly understood. This paper investigates the global geometry of attractor basins and the physical determinants of the storage limit in KLR-trained Hopfield networks. We combine empirical evaluations using random sequences and real-world image embeddings (CIFAR-10) with phenomenological morphing experiments and statistical Signal-to-Noise Ratio (SNR) analysis. Our experiments reveal that the network achieves a storage capacity for random sequences up to $P/N \approx 16$ , and maintains stable retrieval for structured data at effective loads near $P/N \approx 20$ . Through morphing analysis, we reveal that attractors on the "Ridge of Optimization" are separated by sharp, phase-transition-like boundaries, characterized by steep effective potential barriers and critical slowing down. Furthermore, by contrasting an SNR analysis with a geometric reference point inspired by Cover's theorem, we show that the ultimate storage limit is constrained primarily not by a lack of geometric separability in the feature space, but by the loss of dynamical stability against crosstalk noise. These findings suggest that KLR networks function as highly localized, exemplar-based memories that operate optimally just before the onset of dynamical collapse, providing new insights into the design of robust, large-scale retrieval systems.

Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single-attempt accuracy (Pass@1) on reasoning tasks, yet often suffers from reduced multi-sample coverage (Pass@K), indicating diversity collapse. We identify a structural cause for this degradation: common RLVR objectives, such as GRPO, are indifferent to how probability mass is distributed among correct solutions. Combined with stochastic training dynamics, this indifference induces a self-reinforcing collapse, in which probability mass concentrates on a narrow subset of correct outputs while alternative valid solutions are suppressed. We formalize this collapse mechanism and further characterize the optimal policy structure under two complementary criteria: robustness and entropy-regularized optimality, which identify the Uniform-Correct Policy as uniquely optimal. Motivated by this analysis, we propose Uniform-Correct Policy Optimization (UCPO), a modification to GRPO that adds a conditional uniformity penalty on the policy's distribution over correct solutions. The penalty redistributes gradient signal toward underrepresented correct responses, encouraging uniform allocation of probability mass within the correct set. Across three models (1.5B-7B parameters) and five mathematical reasoning benchmarks, UCPO improves Pass@K and diversity while maintaining competitive Pass@1, achieving up to +10\% absolute improvement on AIME24 at Pass@64 and up to 45\% higher equation-level diversity within the correct set. The code is available at https://github.com/AnamikaLochab/UCPO.

Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existing methods operate at the sequence level, applying uniform updates across all tokens despite only a subset encoding the knowledge targeted for removal. This introduces gradient noise, degrades utility, and leads to suboptimal forgetting. We propose TokenUnlearn, a token-level attribution framework that identifies and selectively targets critical tokens. Our approach combines knowledge-aware signals via masking, and entropy-aware signals to yield importance scores for precise token selection. We develop two complementary strategies: hard selection, applying unlearning only to high-importance tokens, and soft weighting, modulating gradient contributions based on importance scores. Both extend existing methods to token-level variants. Theoretical analysis shows token-level selection improves gradient signal-to-noise ratio. Experiments on TOFU and WMDP benchmarks across three model architectures demonstrate consistent improvements over sequence-level baselines in both forgetting effectiveness and utility preservation.

GenAI systems such as ChatGPT are increasingly discussed in programming education, but the ways in which the research literature conceptualizes and frames their role remain unclear. This chapter applies text mining to publications indexed in a leading academic database to map scholarly discourse on ChatGPT in programming education. Term frequency analysis, phrase pattern extraction, and topic modeling reveal four dominant themes: pedagogical implementation, student-centered learning and engagement, AI infrastructure and human-AI collaboration, and assessment, prompting, and model evaluation. The literature prioritizes classroom practice and learner interaction, with comparatively limited attention to assessment design and institutional governance. Across studies, ChatGPT is positioned both as a learning aid that supports explanation, feedback, and efficiency and as a pedagogical risk linked to overreliance, unreliable outputs, and academic integrity concerns. These findings support responsible integration and highlight the need for stronger assessment and governance mechanisms.

Flow-based generative modeling in continuous spaces exploit Tweedie's formula to express the denoiser (learned in training) as a score function (used in sampling). In contrast, this relation has been largely missing in the discrete setting where common approaches focus on learning discrete scores and rates. In this work we close this gap for discrete non-negative ordinal data by introducing Binomial flows. Our framework provides a simple recipe for training a discrete diffusion model which simultaneously denoises, samples, and estimates exact likelihoods. We verify our methodology on synthetic examples and obtain competitive results on real-world data sets.

LLM parameter editing methods commonly rely on computing an ideal target hidden-state at a target layer (referred as anchor point) and distributing the target vector to multiple preceding layers (commonly known as backward spreading) for cooperative editing. Although widely used for a long time, its underlying basis have not been systematically investigated. In this paper, we first conduct a systematic study of its foundations, which helps clarify its capability boundaries, practical considerations, and potential failure modes. Then, we propose a simple and elegant alternative that replaces backward spreading with forward-propagation. Instead of optimizing the target at the last editing layer, we optimize the anchor point at the first editing layer, and then propagate it forward to obtain accurate and mutually compatible target hidden-states for all subsequent editing layers. This approach achieves the same computational complexity as existing methods while producing more accurate layer-wise targets. Our method is simple, without interfering with either the computation of the initial target hidden state or any other components of the subsequent editing pipeline, and thus constituting a benefit for a wide range of LLM parameter editing methods.

Long-term conversational agents must decide which turns to store in external memory, yet recent systems rely on autoregressive LLM generation at every turn to make that decision. We present MemRouter, a write-side memory router that decouples memory admission from the downstream answer backbone and replaces per-turn memory-management decoding with an embedding-based routing policy. MemRouter encodes each turn together with recent context, projects the resulting embeddings through a frozen LLM backbone, and predicts whether the turn should be stored using lightweight classification heads while training only 12M parameters. Under a controlled matched-harness comparison on LoCoMo, where the retrieval pipeline, answer prompts, and QA backbone (Qwen2.5-7B) are held identical, MemRouter outperforms an LLM-based memory manager on every question category (overall F1 52.0 vs 45.6, non-overlapping 95% CIs) while reducing memory-management p50 latency from 970ms to 58ms. Descriptive factorial averaging further shows that learned admission improves mean F1 by +10.3 over random storage, category-specific prompting adds +5.2 over a generic prompt, and retrieval contributes +0.7. These results suggest that write-side memory admission can be learned by a small supervised router, while answer generation remains a separate downstream component in long-horizon conversational QA.

Many diffusion based molecule generation methods ignore the symbolic information of molecules and represent the atom and bond type as one hot representation. Methods based on Morgan fingerprints produce hash collisions and are hard to embed into a continuous space without information loss and random fingerprints correspond to no valid molecule. To circumvent this issue we use another paradigm and consider atom and bond codes as latent variables of VQ-VAE. We introduce VQ-SAD which first trains a VQ-VAE and uses the frozen pretrained VQ-VAE model and considers the codebooks for both atom and bond types as tokenizers for the downstream diffusion process. VQ-SAD is a neuro-symbolic model that utilizes both symbolic and neural structural information for a diffusion based model with learnable forward process. The large discrete code space provides a more balanced atom and bond types which enhances the denoising process. VQ-VAE slightly outperforms SOTA models for diffusion based molecule generation on QM9 and ZINC250k datasets.

Modern organizations increasingly rely on log data and monitoring signals to protect products against account takeovers and abuse, yet integrating security analytics into fast-moving Agile workflows remains challenging. While it is important to understand how security practices are developed and sustained within Agile, real-world case studies of such integrations remain scarce. This experience report provides insights on developer perceptions of an effort to integrate log-based fraud detection within an organization, known as the "Red Flag Project". A cross-functional team of eight members (including one author) iterated weekly to implement a proof-of-concept log-based system that alerts stakeholders when accounts exhibit suspicious activity patterns. Through semi-structured interviews, we investigate developer perceptions of log-based fraud detection integration-exploring their willingness to adopt the system, challenges encountered, and the overall impact on day-to-day development activities and security perceptions. Our findings highlight key lessons, mitigation techniques, and best practices for embedding security analytics into Agile workflows. We provide insights for practitioners and researchers seeking to incorporate security practices into modern development processes while maintaining both speed and resilience in software delivery.

Root cause localization in cloud native microservice systems requires modeling complex service dependencies, irregular temporal dynamics, and heterogeneous observability data. We present HyperODE RCA, a unified framework that combines hypergraph attention learning, latent ordinary differential equations, and multimodal cross attention fusion for fine grained root cause analysis. The method learns higher order service interactions through differentiable hyperedge construction, captures continuous anomaly evolution from irregular observations with an ODE RNN encoder, and adaptively fuses logs, traces, metrics, entities, and events using context aware modality routing. We further improve robustness with a variational information bottleneck, temporal causal regularization, and invariant risk constraints. Experiments on the Tianchi AIOps benchmark show clear gains over strong baselines in ranking and classification performance, while preserving interpretability through learned hypergraph attention.

Recent multi-bit watermarking methods for large language models (LLMs) prioritize capacity over reliability, often conflating decoding with detection. Our analysis reveals that existing ECC-based extractors suffer from catastrophic false positive rates (FPR), and applying rejection thresholds merely collapses detection sensitivity (TPR) to random guessing. To resolve this structural limitation, we propose \textbf{BREW} (Block-wise Reliable Embedding for Watermarking), a framework shifting the paradigm to \emph{designated verification}. BREW employs a two-stage mechanism: (i) \textbf{blind message estimation} via independent block voting, followed by (ii) \textbf{window-shifting verification} that rigorously validates the payload against local edits. Experiments demonstrate that BREW achieves a TPR of 0.965 with an FPR of 0.02 under 10\% synonym substitution, demonstrating that the high-FPR issue is not an inherent trade-off of multi-bit watermarking, but a solvable structural flaw of prior decoding-centric designs. Our framework is model-agnostic and theoretically grounded, providing a scalable solution for reliable forensic deployment.

Given the rapidly growing capabilities of vision-language models (VLMs), extending them to interactive decision-making tasks such as video games has emerged as a promising frontier. However, existing approaches either rely on large-scale supervised fine-tuning (SFT) on human trajectories or apply reinforcement learning (RL) only in relatively short-horizon settings (typically around 20--30 turns). In this work, we study RL-based training of VLMs for long-horizon decision-making in Super Mario Land, a visually grounded environment requiring 100+ turns of interaction with coordinated perception, reasoning, and action. We begin with a systematic investigation of key algorithmic components and propose an adapted variant of PPO with a lightweight turn-level critic, which substantially improves training stability and sample efficiency over critic-free methods such as GRPO and Reinforce++. We further show that pretrained VLMs provide strong action priors, significantly improving sample efficiency during RL training and reducing the need for manual design choices such as action engineering, compared to classical deep RL trained from scratch. Building on these insights, we introduce Odysseus, an open training framework for VLM agents, achieving substantial gains across multiple levels of the game and at least 3 times average game progresses than frontier models. Moreover, the trained models exhibit consistent improvements under both in-game and cross-game generalization settings, while maintaining general-domain capabilities. Overall, our results identify key ingredients for making RL stable and effective in long-horizon, multi-modal settings, and provide practical guidance for developing VLMs as embodied agents.

The study examines the adoption of artificial intelligence (AI) tools in education by analyzing the roles of institutional support, teacher confidence, and teacher concerns. It aims to determine whether teacher concerns moderate the relationship between institutional support and two outcomes: teacher confidence and attitudes toward AI adoption. The sample included 260 teachers from the Philippines. Composite scores were calculated for institutional support, confidence, concerns, and attitudes. Moderated multiple regression analysis showed that institutional support significantly predicted both teacher confidence and attitudes toward AI. However, teacher concerns did not significantly moderate these relationships. A follow-up mediation analysis tested whether confidence explains the effect of institutional support on attitudes. Results showed full mediation. The indirect effect was significant based on the Sobel test, and the direct effect became non-significant when confidence was included in the model. This shows that institutional support improves teacher attitudes by increasing their confidence. The study recommends that institutions provide structured and ongoing support to strengthen teacher confidence. Professional development, mentoring, and AI integration in teacher education programs can increase readiness and support effective AI adoption.

Tree-based speculative decoding accelerates autoregressive generation by verifying multiple draft candidates in parallel, but this advantage weakens for sparse Mixture-of-Experts (MoE) models. As the draft tree grows, different branches activate different experts, expanding the union of activated experts and substantially increasing target-side verification cost. We propose EVICT, a training-free, hyperparameter-free, and lossless adaptive verification method for MoE speculative decoding. EVICT makes every verified token count by truncating the draft tree before target verification and retaining only the cost-effective prefix. It leverages fine-grained drafter signals to estimate candidate benefit, combines them with offline-profiled verification cost, and remains highly compatible with the high-performance graph-based serving framework SGLang. Extensive experiments on diverse MoE backbones and benchmarks show that EVICT achieves up to 2.35x speedup over autoregressive decoding and an average 1.21x speedup over the state-of-the-art baseline EAGLE-3, while significantly reducing unnecessary expert activations during verification.

Sampling the free energy surface, namely, the distribution of collective variables (CVs), is a crucial problem in statistical physics, as it underpins a better understanding of chemical reactions and conformational transitions. Traditional methods for free energy surface sampling involve simulation in high-dimensional configuration space and projecting the resulting configurations onto the CV space. To reduce the computational costs of such sampling, we propose FES-FM, a reduced flow matching (FM) method for free energy sampling (FES). We train a dynamical transport map in the CV space, thereby enabling direct sampling of the free energy surface. For many-particle systems, we construct a prior distribution based on the Hessian at a local minimum of the potential, which ensures both rotation-translation invariance and physically meaningful configurations. We evaluate the proposed method across a variety of potential functions and collective variables. Comparative experiments demonstrate that our approach drastically reduces computational costs while delivering superior accuracy per unit sampling time.

A key challenge for large language models is token cost per query and overall deployment cost. Clinical inputs are long, heterogeneous, and often redundant, while downstream tasks are short and high stakes. We study budgeted context selection, where a subset of document units is chosen under a strict token budget so an off-the-shelf generator can meet fixed cost and latency constraints. We cast this as a knapsack-constrained subset selection problem with two design choices, unitization that defines document segmentation and selection that determines which units are kept. We propose \textbf{RCD}, a monotone submodular objective that balances relevance, coverage, and diversity. We compare sentence, section, window, and cluster-based unitization, and introduce a routing heuristic that adapts to the budget regime. Experiments on MIMIC discharge notes, Cochrane abstracts, and L-Eval show that optimal strategies depend on the evaluation setting. Positional heuristics perform best at low budgets in extractive tasks, while diversity-aware methods such as MMR improve LLM generation. Selector choice matters more than unitization, with cluster-based grouping reducing performance and other schemes behaving similarly. ROUGE saturates for LLM summaries, while BERTScore better reflects quality differences. We release our code at https://github.com/stone-technologies/ACL_budget_paper.

Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical routing question that existing evaluations do not directly answer: which parts of an agent workflow truly require large frontier intelligence, and which can be handled by smaller models? We introduce AgentFloor, a deterministic 30-task benchmark organized as a six-tier capability ladder, spanning instruction following, tool use, multi-step coordination, and long-horizon planning under persistent constraints. We evaluate 16 open-weight models, from 0.27B to 32B parameters, alongside GPT-5 across 16,542 scored runs. Our results reveal a clear boundary of model necessity. Small and mid-sized open-weight models are already sufficient for much of the short-horizon, structured tool use work that dominates real agent pipelines, and in aggregate, the strongest open-weight model matches GPT-5 on our benchmark while being substantially cheaper and faster to run. The gap appears most clearly on long-horizon planning tasks that require sustained coordination and reliable constraint tracking over many steps, where frontier models still hold an advantage, though neither side reaches strong reliability. We also find that this boundary is not explained by scale alone: some failures respond to targeted interventions, but the effects are model-specific rather than universal. These findings suggest a practical design principle for agentic systems: use smaller open-weight models for the broad base of routine actions, and reserve large frontier models for the narrower class of tasks that truly demand deeper planning and control. We release the benchmark, harness, sweep configurations, and full run corpus.

Frozen Gemma 4 31B weights pretrained exclusively on text tokens, unmodified, transfer across modality boundaries through a thin trainable interface. (1) OGBench scene-play-singletask-task1-v0: $+4.33$pt over published GCIQL at $n=3$ with std 0.74 -- a published-SOTA win on a robotic manipulation task the substrate has never seen. (2) D4RL Walker2d-medium-v2: Decision-Transformer parity ($76.2 \pm 0.8$, $n=3$) at $0.43\times$ DT's trainable count, with the frozen substrate compressing to a 5L slice ($+1.66$pt over the 6L baseline at $n=3$). (3) Associative recall as the cleanest pretraining-load-bearing case: the frozen slice + a 113K-parameter linear interface reaches L30 best-checkpoint per-bit error 0.0505 ($n=2$); a 6.36M-parameter from-scratch trained transformer at matched capacity ($1/\sqrt{d_k}$ scaling, two seeds, LR sweep) cannot solve the task at all under the protocol (best L30 = 0.4395), an $8.7\times$ advantage. Architecture-alone falsifications: a frozen random transformer with correct $1/\sqrt{d_k}$ scaling stays at random-chance loss for 50k steps; a random-init Gemma slice fails OGBench cube-double-play-task1 entirely (0.89% across $n=3$ where pretrained reaches 60%). A dual-measurement protocol -- text-activation probing on 95 English sentences plus task-ablation on a non-language target -- names individual heads independently identifiable on both protocols: head L26.28 scores $3.7\times$ the slice mean for English token-copying and is the #2 most-critical head for binary copy ablation ($Δ$ L30 $= +0.221$); three further heads (L27.28, L27.2, L27.3) classify by the same protocol. The mechanism is single-model and the cross-modality results are single-task within their respective benchmarks; cross-model replication is structurally constrained because Gemma 4 31B is the only model on the small-scale Pareto frontier as of April 2026.

Operator learning enables fast surrogate modeling of high-dimensional dynamical systems, but existing approaches face two fundamental limitations: quadratic inference complexity and unreliable uncertainty quantification in safety-critical settings. We propose Conformalized Quantum DeepONet Ensembles, a framework that addresses both challenges simultaneously. By leveraging Quantum Orthogonal Neural Networks (QOrthoNNs), we reduce operator inference complexity from O(n^2) to O(n), enabling scalable evaluation over fine discretizations. To provide rigorous uncertainty quantification, we combine ensemble-based epistemic modeling with adaptive conformal prediction, yielding distribution-free coverage guarantees. A key challenge in ensembling is that naive parallelism scales hardware resources linearly with the number of models. We resolve this by using Superposed Parameterized Quantum Circuits (SPQCs), which compress multiple ensemble members into a single circuit and enable simultaneous multi-model execution. Experiments on synthetic partial differential equations and real-world power system dynamics demonstrate that our approach achieves accurate predictions while maintaining calibrated uncertainty under realistic quantum noise. These results establish a practical pathway toward scalable, uncertainty-aware operator learning in quantum machine learning.

In large language model (LLM)-based recommendation systems, direct preference optimization (DPO) effectively aligns recommendations with user preferences, requiring multi-negative objective functions to leverage abundant implicit-feedback negatives and sharpen preference boundaries. However, our empirical analyses reveal a counterintuitive phenomenon, preference optimization collapse, where increasing the number of negative samples can lead to performance degradation despite a continuously decreasing training loss. We further theoretically demonstrate that this collapse arises from gradient suppression, caused by the dominance of easily discriminable negatives over boundary-critical negatives that truly define user preference boundaries. As a result, boundary-relevant signals are under-optimized, weakening the model's decision boundary. Motivated by these observations, we propose DynamicPO (Dynamic Preference Optimization), a lightweight and plug-and-play framework comprising two adaptive mechanisms: Dynamic Boundary Negative Selection, which identifies and prioritizes informative negatives near the model's decision boundary, and Dual-Margin Dynamic beta Adjustment, which calibrates optimization strength per sample according to boundary ambiguity. Extensive experiments on three public datasets show that DynamicPO effectively prevents optimization collapse and improves recommendation accuracy on multi-negative preference optimization methods, with negligible computational overhead. Our code and datasets are available at https://github.com/xingyuHuxingyu/DynamicPO.

Single-prompt first-token probabilities from zero-shot vision-language model (VLM) safety classifiers are treated as decision scores, but we show they are unreliable under semantically equivalent prompt reformulation: even when the binary label is constrained to a fixed output position, equivalent prompts can induce materially different unsafe probabilities for the same sample. Across multimodal safety benchmarks and multiple VLM families, cross-prompt variance is strongly associated with prompt-level disagreement and higher error, making it a useful fragility diagnostic. A training-free mean ensemble improves NLL on all 14 dataset-model evaluation pairs and ECE on 12/14 relative to a train-selected single-prompt baseline, and wins more head-to-head NLL comparisons than labeled temperature scaling, Platt scaling, and isotonic regression applied to the same prompt. Ranking gains are consistent against the train-selected baseline on both AUROC and AUPRC, and against the full 15-prompt distribution remain consistent on AUPRC while softening on AUROC. Labeled calibration on top of the mean provides further gains when labels are available, identifying prompt averaging as a strong label-free first stage rather than a replacement for calibration. We frame this as a reliability stress test for zero-shot VLM first-token safety scores and recommend prompt-family evaluation with mean aggregation as a standard label-free reliability baseline.

Large-scale ranking systems depend on thousands of features derived from user behavior across multiple time horizons. Typically requires model retraining -- resulting in long iteration cycles (3--6 months), substantial GPU resource consumption, and limited rollout throughput. We introduce Intelligent Elastic Feature Fading (IEFF), a production infrastructure system that enables retrain-free feature efficiency rollouts by elastically controlling feature coverage and distribution at serving time. IEFF supports incremental feature coverage adjustments while models adapt through recurring training, eliminating dependencies on explicit retraining cycles. The system incorporates strict safety guardrails, reversibility mechanisms, and comprehensive monitoring to ensure stability at scale. Across multiple production use cases, IEFF accelerates efficiency-related rollouts by 5$\times$, eliminates retraining-related GPU overhead, and enables faster capacity recycling. Extensive offline and online experiments demonstrate that gradual feature fading prevents 50--55\% of online performance degradation compared to abrupt feature removal, while maintaining stable model behavior. These results establish elastic, system-level feature fading as a practical and scalable approach for managing feature efficiency in modern industrial ranking systems.

Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment methods typically rely on supervision distilled from stronger models such as GPT. However, this offline paradigm introduces a Supervision-Perception Mismatch: the student model is forced to align with fine-grained details beyond its perceptual capacity, learning to guess rather than to see. To obtain reliable self-supervision for online learning, we identify a Generative-Discriminative Gap within LVLMs, where models exhibit higher accuracy on discriminative verification than open-ended generation. Leveraging this capability, we propose \textbf{O}nline \textbf{S}elf-\textbf{CA}lib\textbf{R}ation (OSCAR), a framework that integrates Monte Carlo Tree Search with a Dual-Granularity Reward Mechanism to construct preference data and iteratively refines the model via Direct Preference Optimization. Extensive experiments demonstrate that OSCAR achieves state-of-the-art performance on hallucination benchmarks while improving general multimodal capabilities.

Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including ground weather stations, satellites and IoT devices, to collaboratively train deep learning models without sharing raw data. This method safeguards data privacy and security while leverages diverse, geographically distributed datasets to improve the accuracy and robustness of global/regional weather modeling tasks such as forecasting and anomaly detection.

We present VitaLLM, a mixed precision accelerator that enables ternary weight large language models to run efficiently on edge devices. The design combines two compute cores, a multiplier free TINT core for ternary-INT projections and a BoothFlex core that reuses a radix-4 Booth datapath for both INT8$\times$INT8 attention and ternary-INT-sustaining utilization without duplicating arrays. A predictive sparse attention mechanism employs a leading-one (LO) surrogate with a comparison-free top-$K$ selector to prune key/value (KV) fetches by roughly $1-K/M$ for $M$ cached tokens, confining exact attention to $K$ candidates. System-level integration uses head-level pipelining and an absmax-based quantization barrier to standardize cross-core interfaces and overlap nonlinear reductions with linear tiles. A 16 nm silicon prototype at 1 GHz/0.8 V achieves 72.46 tokens/s in decode and 0.88 s prefill (64 tokens) within 0.214 mm^2 and 120 KB on-chip memory, while reducing KV traffic and improving utilization in ablations. These results demonstrate practical BitNet b1.58 (3B) inference on edge-class platforms and provide a compact blueprint for future mixed-precision LLM accelerators.

This paper presents a PVT-resilient, subthreshold SRAM-based computing-in-memory (CIM) macro tailored for energy-efficient spiking neural networks (SNNs). The macro integrates in-situ current sensors and distributed voltage regulators to enable robust large-scale (1024 wordlines, 1304 bitlines and 128 shared neuron cells) subthreshold current-mode CIM, mitigating energy overheads and process-voltage-temperature (PVT) sensitivity. The neuron cells adopt a programmable, memory cell-based firing threshold to enhance neuron robustness against PVT variations. The architecture uses a stride-tick batching schedule to significantly reduce buffer overhead with enhanced input data reuse. Exploiting the high sparsity of SNNs, the proposed system demonstrates significant improvements in energy efficiency and variation tolerance. Fabricated in 28-nm CMOS, the prototype attains 93.64\% accuracy on keyword spotting, delivers up to 1181.42 TOPS/W, and achieves 7.24 TOPS/mm^2, demonstrating a viable and efficient solution for high-performance edge SNN processing.

Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented generation (RAG) are primarily designed for unstructured text and do not account for tabular structure. We propose a structure-aware tabular chunking (STC) framework that operates on row-level units by constructing a hierarchical Row Tree representation, where each row is encoded as a key-value block. STC performs token-constrained splitting aligned with structural boundaries and applies overlap-free greedy merging to produce dense, non-overlapping chunks. This design preserves semantic relationships between fields within a row while improving token utilization and reducing fragmentation. Across evaluations on the MAUD dataset, STC reduces chunk count by up to 40% and 56% compared to standard recursive and key-value based baselines, respectively, while improving token utilization and processing efficiency. In retrieval benchmarks, STC improves MRR from 0.3576 to 0.5945 in a hybrid setting and increases Recall@1 from 0.366 to 0.754 in BM25-only retrieval. These results demonstrate that preserving structure during chunking improves retrieval performance, highlighting the importance of structure-aware chunking for RAG over tabular data.

As climate extreme and disaster events become more frequent and intense, Geospatial Artificial Intelligence (GeoAI) has emerged as a transformative approach for large-scale disaster mapping and risk reduction. However, the purely mechanical, performance-driven deployment of GeoAI models can result in amplifying inherent spatial inequalities, preventing effective emergency decision-making, and producing severe environmental carbon footprint. To unbox the concept of responsible GeoAI, this position paper examines its emerging role, e.g., in climate extreme and disaster mapping, from a critical GIS perspective. We address the nexus of responsible GeoAI into four interrelated theoretical dimensions, specifically Representativeness, Explainability, Sustainability, and Ethics, with examples from climate extreme and disaster mapping. Moreover, targeting at the operational practice, we then propose a conceptual governance Model of responsible GeoAI that categorizes its governance practices into Data, Application, and Society scopes. Last, this position paper aims to raise the attention in the broader GIS community that the future of climate resilience relies not just on building better algorithms, but on fostering a governance ecosystem where GeoAI is deployed responsibly, ethically, and sustainably.

An agent skill is a configuration package that equips an LLM-driven agent with a concrete capability, such as reading email, executing shell commands, or signing blockchain transactions. Each skill is a hybrid artifact-a structured half declares executable interfaces, while a prose half dictates when and how those interfaces fire-and the prose is reinterpreted probabilistically on every invocation. Conventional static analyzers parse the structured half but ignore the prose; LLM-based tools read the prose but cannot reproducibly prove that a tainted input reaches a high-impact sink. We present Semia, a static auditor for agent skills. Semia lifts each skill into the Skill Description Language (SDL), a Datalog fact base that captures LLM-triggered actions, prose-defined conditions, and human-in-the-loop checkpoints. Synthesizing a fact base that is both structurally sound and semantically faithful to the original prose is the central challenge; we address it with Constraint-Guided Representation Synthesis (CGRS), a propose-verify-evaluate loop that refines LLM candidates until convergence. Security properties (e.g., indirect injection, secret leakage, confused deputies, unguarded sinks, etc.) over an agent skill can then be reduced to Datalog reachability queries. We evaluate Semia on 13,728 real-world skills from public marketplaces. Semia renders all of them auditable and finds that more than half carry at least one critical semantic risk. On a stratified sample of 541 expert-labeled skills, Semia achieves 97.7% recall and an F1 of 90.6%, substantially outperforming signature-based scanners and LLM baselines.

The current structure-centric paradigm in artificial intelligence (AI)-driven materials discovery, despite delivering thousands of candidate structures, is stalling at a critical barrier: the synthesizability gap. We argue that closing this gap demands a pivot to a synthesis-first paradigm in which executable synthesis protocols, not just atomic configurations, are treated as primary design variables. We outline a roadmap built on three pillars: (i) representing synthesis procedures as machine-readable protocols, (ii) deploying generative and inverse-design models to propose actionable reaction pathways and recipes, and (iii) integrating closed-loop optimisation to refine protocols against experimental realities and sustainability constraints. Framed in terms of the causal backbone P->X->y from protocol P to structure X and properties y, this perspective sets out methodological building blocks, standards needs and self-driving laboratory (SDL) integration strategies to accelerate reproducible, data-first materials discovery.

Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution provides visual enhancement and utility for monitoring tasks. In particular, SR has been increasingly developed for satellite-based Earth observation, with applications in urban planning, agriculture, ecology, and disaster response. However, existing SR studies and benchmarks typically use fidelity metrics such as PSNR or SSIM, whereas the true utility of super-resolved images lies in supporting downstream tasks such as land cover classification, biomass estimation, and change detection. To bridge this gap, we introduce GeoSR-Bench, a downstream task-integrated SR benchmark dataset to evaluate SR models beyond fidelity metrics. GeoSR-Bench comprises spatially co-located, temporally aligned, and quality-controlled image pairs from about 36,000 locations across diverse land covers, spanning resolutions from 500m to 0.6m. To the best of our knowledge, GeoSR-Bench is the first SR benchmark that directly connects improved image resolution from SR models with downstream Earth monitoring tasks, including land cover segmentation, infrastructure mapping, and biophysical variable estimation. Using GeoSR-Bench, we benchmark GAN, transformer, neural operator, and diffusion-based SR models on perceptual quality and downstream task performance. We conduct experiments with 270 settings, covering 2 cross-platform SR tasks, 9 SR models, 3 downstream task models, and 5 downstream tasks for each SR task. The results show that improvements in traditional SR metrics often do not correlate with gains in task performance, and the correlations can be negative, indicating that these metrics provide limited guidance for selecting superior models for downstream tasks. This reveals the need to integrate downstream tasks into SR model development and evaluation.

Public inference benchmarks compare AI systems at the model and provider level, but the unit at which deployment decisions are actually made is the endpoint: the (provider, model, stock-keeping-unit) tuple at which a specific quantization, decoding strategy, region, and serving stack is exposed. We introduce TokenArena, a continuous benchmark that measures inference at endpoint granularity along five core axes (output speed, time to first token, workload-blended price, effective context, and quality on the live endpoint) and synthesizes them, together with a modeled energy estimate, into three headline composites: joules per correct answer, dollars per correct answer, and endpoint fidelity (output-distribution similarity to a first-party reference). The framework's novelty is empirical and methodological. Across 78 endpoints serving 12 model families, the same model on different endpoints differs in mean accuracy by up to 12.5 points on math and code, in fingerprint similarity to first party by up to 12 points, in tail latency by an order of magnitude, and in modeled joules per correct answer by a factor of 6.2. We further show that workload-aware blended pricing reorders the leaderboard substantially: 7 of 10 top-ranked endpoints under the chat preset (3:1 input:output) fall out of the top 10 under the retrieval-augmented preset (20:1), and the reasoning preset (1:5) elevates frontier closed models that the chat preset penalizes on price. We release the framework, schema, probe and eval harness, and a v1.0 leaderboard snapshot under CC BY 4.0. TokenArena is a methodology, not a single ranking; we publish full provenance and limitations and welcome external replication.

Deploying reinforcement learning policies in the real world requires adapting to time-varying environments. We study this problem in the contextual Markov Decision Process (cMDP) framework, where a family of environments is indexed by a low-dimensional context unknown at test time. The standard approach decomposes the problem: train a so-called "universal policy" which assumes knowledge of the true context, then pair it with a context estimator which approximates context using the observed trajectory. We identify a simple, counterintuitive trick that substantially improves the estimator: randomly delete a fraction of the training buffer after each round. This works because data is collected across multiple rounds using progressively better policies, and older trajectories come from a different distribution than what the estimator will face at deployment time; random deletion creates an implicit exponential decay on older data while preserving diversity without requiring any explicit identification of which samples are stale. This reduces robustness gap by 30% for MLPs and by 6% on average for recurrent networks. Strikingly, it allows a narrow MLP with 5x fewer parameters to outperform a wide MLP trained without deletion. To understand when and why deletion helps, we analyze regularized empirical risk minimization with a mismatch between the train distribution and the distribution at deployment; in this idealized setting, we prove that removing a single uniformly random training point decreases expected test loss in expectation under mild conditions. For ridge regression we make this quantitative: deletion helps when the regularization coefficient is moderate and the signal-to-noise ratio (SNR) is sufficiently low, and, crucially, this SNR threshold gives a direct measure of how large the distribution mismatch between training and deployment must be for deletion to be beneficial.

Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE header contents. One barrier to incorporating dynamic analysis features has been the semi-structured nature of sandbox behavior reports. We show that, using the latest generation of large language models with reasoning, it is possible to efficiently process these behavior reports and utilize them as part of a malware detection pipeline. Specifically, we leverage LLMs to generate behavior-based malware detection rules based on a small training set of labeled malware. We find that these detection rules, derived from behavioral features, are much more robust to concept drift than standard static-feature methods, while maintaining practical false positive rates. Finally, we introduce Trident, a system which combines a classic decision tree model over static features, our behavior-based detection rules, and direct LLM analysis of sandbox reports through majority voting. Trident outperforms standard methods using static features, outperforms behavior-based rules alone, and is as resilient to concept drift as active learning methods without requiring retraining.

This study presents a systematic approach to identifying and characterizing student misconceptions in online learning environments through a novel combination of quantitative performance analysis and large language model (LLM) assessment. We analyzed data from 9 course periods across 5 online biomedical science courses, encompassing 3,802 medical student enrollments. Using data from 40-50 topic-focused quizzes per course, we developed a two-stage methodology. First, we identified challenging central topics using quiz-level performance metrics. Second, we employed LLMs to characterize the underlying misconceptions in these high-priority areas. By examining student performance on first attempts across primarily multiple-choice questions (MCQs), we identified consistently challenging topics that were also central to course objectives. We then leveraged recent advances in generative AI to analyze three distinct data sources in combination: quiz question content, student response patterns, and lecture transcripts. This approach revealed actionable insights about student misconceptions that were not apparent from performance data alone. The quality of the LLM-identified misconceptions was rated as excellent by subject matter experts. We also conducted teacher interviews to assess the perceived utility of our topic identification method. Faculty found that data-driven identification of challenging topics was valuable and corroborated their own classroom observations. This methodology provides a scalable approach to characterizing student difficulties in learning environments where quizzes are used. Our findings demonstrate the potential for targeted and potentially personalized interventions in future course iterations, with clear pathways for measuring intervention effectiveness through follow-up quiz performance.

The scalability of Large Language Models to long sequences is hindered by the quadratic cost of attention and the limitations of positional encodings. To address these, we introduce Caracal, a novel architecture that replaces attention with a parameter-efficient, $\mathcal{O}(L \log L)$ Multi-Head Fourier (MHF) module. Our contributions are threefold: (1) We leverage the Fast Fourier Transform (FFT) for sequence mixing, inherently addressing both bottlenecks mentioned above. (2) We apply a frequency-domain causal masking technique that enforces autoregressive capabilities via asymmetric padding and truncation, overcoming a critical barrier for Fourier-based generative models. (3) Unlike efficient models relying on hardware-specific implementations (e.g., Mamba), we uses standard library operators. This ensures robust portability, eliminating common deployment barriers. Evaluations demonstrate that Caracal performs competitively with Transformer and SSM baselines, offering a scalable and simple pathway for efficient long-sequence modeling. Code is available in Appendix.

Dirac-Frenkel instantaneous residual minimization evolves nonlinear parametrizations of PDE solutions in time, but ill-conditioning can render the parameter dynamics non-unique. We interpret this non-uniqueness as a gauge freedom: nullspace directions that leave the time derivative unchanged can be used to select better-conditioned parameter velocities. Building on Onsager's minimum-dissipation principle, we introduce a history variable -- interpretable as momentum -- and inject it only along the nullspace directions. The resulting Dirac-Frenkel-Onsager dynamics preserve instantaneous residual minimization, in contrast to standard regularization that can introduce bias, while promoting temporally smooth parameter evolutions. Examples demonstrate that the approach leads to increased robustness in singular and near-singular regimes.

Conformance checking, one of the main process mining operations, aims to identify discrepancies between a process model and an event log. The model represents the expected behaviour, whereas the event log represents the actual process behaviour as captured in information systems records. Traditionally, the process model and the event log are both accessible to the business analyst performing the conformance checking. However, in some contexts, it is necessary to keep either the model or the log private to protect critical or sensitive information. In this paper, we propose a secure approach to conformance checking based on string processing algorithms and homomorphic encryption, where the process model and event log ar not visible to either the model's or event log's owner. The proposed technique is based on alignments, a well-known formalism used for conformance checking. An evaluation is performed using a synthetic and a real-world event log, showing that conformance checking can be securely computed at the expense of high memory and processing requirements.

We study high-probability (HP) convergence guarantees in decentralized stochastic optimization, where multiple agents collaborate to jointly train a model over a network. Existing HP results in decentralized settings almost exclusively focus on the Decentralized Stochastic Gradient Descent ($\mathtt{DSGD}$) algorithm, which requires strong assumptions, such as bounded data heterogeneity, or strong convexity of each agent's cost. This is contrary to the mean-squared error (MSE) results, where methods incorporating bias-correction techniques are known to converge under relaxed assumptions and achieve better practical performance. In this paper we provide the first step toward bridging the gap, by studying HP convergence of $\mathtt{DSGD}$ incorporating the gradient tracking technique, in the presence of noise satisfying a relaxed sub-Gaussian condition. We show that the resulting method, dubbed $\mathtt{GT-DSGD}$, achieves order-optimal HP convergence rates for both non-convex and Polyak-Łojasiewicz costs, of order $\mathcal{O}\Big(\frac{\log(1/δ)}{\sqrt{nT}}\Big)$ and $\mathcal{O}\Big(\frac{\log(1/δ)}{nT}\Big)$, respectively, where $n$ is the number of agents, $T$ is the time horizon and $δ\in (0,1)$ is the confidence parameter. Our results establish that $\mathtt{GT-DSGD}$ converges in the HP sense under the same conditions on the cost as in the MSE sense, while achieving comparable transient times. To the best of our knowledge, these are the first HP guarantees for decentralized optimization methods incorporating bias-correction. Numerical experiments on real and synthetic data verify our theoretical findings, underlining the superior performance of $\mathtt{GT-DSGD}$ and highlighting that the benefits of incorporating bias-correction are also maintained in the HP sense.

AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT, and advanced 5G for vehicle-to-everything (V2X) connectivity, AI at the edge computing nodes plays a crucial role in protecting against sophisticated threats, enabling URLLC (ultra-low-latency communications) for smart transport, and enhancing infrastructure capabilities and safety. This research applies edge computing to improve latency, bandwidth efficiency, and service responsiveness by moving processing closer to devices, gateways, and users. However, this shift also expands the cyberattack surface because edge nodes are distributed, heterogeneous, and often resource-constrained. The paper proposes a trust-aware federated hybrid intrusion detection framework in which a random forest, a decision tree, and a linear SVM network learn complementary traffic representations at each edge site, while a server performs trust-aware aggregation of local model updates.

Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting factors such as travel time, energy consumption, and traffic conditions directly affect plan quality. Yet existing systems are largely designed for feasibility-oriented planning, and current benchmarks provide only reference answers without ground truth, preventing objective evaluation of optimization performance. In our paper, we address these limitations with an agentic AI framework that enables dynamic refinement through an orchestration agent coordinating specialized agents for traffic, charging, and points of interest, and with the Trip-planning Optimization Problems Dataset, which supplies definitive optimal solutions and category-level task structure for fine-grained analysis. Experiments show that our system achieves 77.4\% accuracy on the TOP Benchmark, significantly outperforming single-agent and workflow-based multi-agent baselines, demonstrating the importance of orchestrated agentic reasoning for robust trip planning optimization.

Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical evidence of these failures, the underlying causes remain unclear. We begin by asking how much of this limitation arises from the data itself. To disentangle data effects, we consider two regimes across different dataset sizes: (1) concept generalization, where each individual concept is observed during training under potentially imbalanced data distributions, and (2) compositional generalization, where specific combinations of concepts are systematically held out. To study these regimes, we introduce mosaic (Multi-Object Spatial relations, AttrIbution, Counting), a controlled framework for dataset generation. By training diffusion models on mosaic, we find that scene complexity plays a dominant role rather than concept imbalance, and that counting is uniquely difficult to learn in low-data regimes. Moreover, compositional generalization collapses as more concept combinations are held out during training. These findings highlight fundamental limitations of diffusion models and motivate stronger inductive biases and data design for robust multi-object compositional generation.

Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to extreme lighting. However, existing learning-based approaches for event processing are typically confined to narrow, task-specific silos and lack the ability to generalize across modalities. We address this gap with REALM, a cross-modal framework that learns an RGB and Event Aligned Latent Manifold by projecting event representations into the pretrained latent space of RGB foundation models. Instead of task-specific training, we leverage low-rank adaptation (LoRA) to bridge the modality gap, effectively unlocking the geometric and semantic priors of frozen RGB backbones for asynchronous event streams. We demonstrate that REALM effectively maps events into the ViT-based foundation latent space. Our method allows us to perform downstream tasks like depth estimation and semantic segmentation by simply transferring linear heads trained on the RGB teacher. Most significantly, REALM enables the direct, zero-shot application of complex, frozen image-trained decoders, such as MASt3R, to raw event data. We demonstrate state-of-the-art performance in wide-baseline feature matching, significantly outperforming specialized architectures. Code and models are available upon acceptance.

Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to high-conflict domains, treating differing opinions as noise. We propose a neuro-symbolic aggregation framework that formalizes conflict resolution through Weighted Maximum Satisfiability (MaxSAT). Our pipeline utilizes a language model to map unstructured natural language explanations into interpretable logical predicates and confidence weights. These components are then encoded as soft constraints within the Z3 solver, transforming the aggregation problem into an optimization task that seeks the maximum consistency across conflicting testimony. Using the Reddit r/AmItheAsshole forum as a case study in large-scale moral disagreement, our system generates logically coherent verdicts that diverge from popularity-based labels 62% of the time, corroborated by an 86% agreement rate with independent human evaluators. This study demonstrates the efficacy of coupling neural semantic extraction with formal solvers to enforce logical soundness and explainability in the aggregation of noisy human reasoning.

Recent white-box OOD detection methods for LLMs -- including CED, RAUQ, and WildGuard confidence scores -- appear effective, but we show they are structurally confounded by sequence length (|r| >= 0.61) and collapse to near-chance under length-matched evaluation. Even raw attention entropy (mean H(alpha) across heads and layers), a natural baseline we include for completeness, shows the same confound. The confound stems from attention's Theta(log T) dependence on input length. To identify genuine OOD signals after deconfounding, we propose a two-pathway framework: embeddings capture what text is about (effective for topic shifts), while the processing trajectory -- hidden-state evolution across layers -- captures how the model processes input. The relative power of each pathway varies along a vocabulary-transparency spectrum: embedding methods excel on vocabulary-distinctive OOD, while trajectory features detect covert-intent inputs that share vocabulary with normal text (0.721 avg AUROC; Jailbreak: 0.850). Three evidence lines support this framework: (1) a crossover between k-NN and trajectory scoring across 6 tasks, where each pathway wins on different OOD types; (2) a per-layer analysis showing that layer-0 k-NN signal is almost entirely a length artifact (Jailbreak: 0.759 raw -> 0.389 matched) -- processing constructs genuine OOD signal from near-chance embeddings; and (3) circuit attribution showing adversarial tasks engage attention circuits more than semantic tasks (p = 0.022; Jailbreak patching p < 0.001), with partial cross-model replication. Code release upon publication.

As language model safeguards become more robust, attackers are pushed toward developing increasingly complex jailbreaks. Prior work has found that this complexity imposes a "jailbreak tax" that degrades the target model's task performance. We show that this tax scales inversely with model capability and that the most advanced jailbreaks effectively yield no reduction in model capabilities. Evaluating 28 jailbreaks on five benchmarks across Claude models ranging in capability from Haiku 4.5 to Opus 4.6, we find Haiku 4.5 loses an average of 33.1% on benchmark performance when jailbroken, while Opus 4.6 at max thinking effort loses only 7.7%. We also observe that across all models, reasoning-heavy tasks display considerably more degradation than knowledge-recall tasks. Finally, Boundary Point Jailbreaking, currently the strongest jailbreak against deployed classifiers, achieves near-perfect classifier evasion with near-zero degradation across safeguarded models. We recommend that safety cases for frontier models should not rely on a meaningful capability degradation from jailbreaks.

Real-world knowledge is often organized as hierarchies such as product taxonomies, medical ontologies, and label trees, yet learning hierarchical representations is challenging due to asymmetric structure and noisy semantics. We introduce Polaris, a polar hyperspherical embedding framework that separates semanticity from hierarchy using angular geometry and radius, enabling the learning of meaning and structure without interference. To map latent representation onto the sphere, we project it to the tangent space at the north pole, apply the exponential map, and learn unit-norm representations using spherical linear layers. Polaris then combines robust local constraints, global regularization that prevents geometric collapse, and uncertainty-aware asymmetric objectives that encourage directional containment. At inference time, Polaris uses structure-guided retrieval to efficiently narrow down candidate parents before final ranking. We evaluate Polaris on different settings of taxonomy expansion - spanning trees, multi-parent DAGs, and multimodal hierarchies, showing consistent improvements of up to ~19 points in top-K retrieval and up to ~60% reduction in mean rank over fourteen strong baselines.

Offline multi-agent reinforcement learning in general-sum settings is challenged by the distribution shift between logged datasets and target equilibrium policies. While standard methods rely on manual pessimistic penalties, we demonstrate that KL regularization suffices to stabilize learning and achieve equilibrium recovery. We propose General-sum Anchored Nash Equilibrium (GANE), which recovers regularized Nash equilibria at an accelerated statistical rate of $\widetilde{O}(1/n)$. For computational tractability, we develop General-sum Anchored Mirror Descent (GAMD), an iterative algorithm converging to a Coarse Correlated Equilibrium at the standard rate of $\widetilde{O}(1/\sqrt{n}+1/T)$. These results establish KL regularization as a standalone mechanism for pessimism-free offline learning that achieves equivalent or accelerated rates in multi-player general-sum games.

Nonlinear Parametric Optimization Network (NLPOpt-Net) is an unsupervised learning architecture to solve constrained nonlinear programs (NLP). Given the structure of an NLP, it learns the parametric solution maps with guaranteed constraint satisfaction. The architecture consists of a backbone neural network (NN) followed by a multilayer ($k$-layered) projection. While the NN drives toward optimality through a loss function consisting of a modified Lagrangian augmented with a consistency loss, the projection ensures feasibility by projecting the NN predictions in the original constraint manifold. Instead of typical distance minimization, our projection exploits local quadratic approximations of the original NLP. Under certain conditions (such as convexity), the projection has a descent property, which improves the NN predictions further. NLPOpt-Net deploys an inversion-free, modified Chambolle-Pock algorithm to solve the constrained quadratic projections during the forward pass and uses the implicit function theorem for efficient backpropagation. The fixed structure of the projection further allows decoupling of the NN and the projection once the training is complete. NLPOpt-Net solves large-scale convex QP, QCQP, NLP, and nonconvex problems with near zero optimality gap and constraint violations reduced to machine precision. Additionally, it provides near accurate prediction of the active sets and corresponding dual variables, thereby enabling a scalable approach for multiparametric programming. Compiling the projection in C provides order of magnitude improvement in inference time compared to JAX. We provide the codes and NLPOpt-Net as a ready to use package that includes GPU support.

The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex, jurisdiction-specific tasks like Indian Chartered Accountancy (CA) remains limited. The models display difficulty in executing numerical tasks which require multiple steps while also needing advanced knowledge about legal regulations and the method of scaling their operations is not feasible in settings which have limited access to resources. We present CA-ThinkFlow as a parameter-efficient Retrieval-Augmented Generation (RAG) framework which operates with a 14B, 4-bit-quantized reasoning model, 14B-DeepSeek-R1, and a layout-aware Docling extraction system which maintains document structure during extraction. CA-ThinkFlow uses a basic RAG method which automatically adds retrieved information into the prompt, while it depends on the model's built-in Chain-of-Thought (CoT) functions to create context and produce correct answers. The system we developed system operates at performance levels which match large proprietary models when we tested it on the multi-level CA-Ben benchmark, achieving Scholastic Reliability Coefficient (SRC) results which equal 68.75\% of GPT-4o and Claude 3.5 Sonnet. The framework shows high efficiency and strength in handling parameters, but essential reasoning abilities fail to process complex regulatory texts which exist in fields such as Taxation.

SAM2 produces high-quality zero-shot segmentation on natural images, but applying it to large remote sensing scenes exposes two problems: (1) its mask generator faces an inherent quality-coverage trade-off: strict thresholds yield precise masks but leave most of the image unsegmented, while relaxed thresholds increase coverage at the cost of mask quality; and (2) large images must be tiled, fragmenting objects across tile boundaries. We propose Remote SAMsing, an open-source pipeline that solves both problems without modifying SAM2 or requiring training data. For coverage, a multi-pass algorithm runs SAM2 repeatedly on each tile, painting accepted masks black between passes to simplify the scene for the next iteration, and relaxing quality thresholds only when coverage gains stagnate, ensuring that the most precise masks are always captured first. For spatial consistency, contextual padding and a parameter-free best-match merge reconstruct objects fragmented across tile boundaries. Evaluated on seven scenes (5~cm to 4.78~m GSD), the pipeline raises coverage from 30--68\% (single-pass SAM2) to 91--98\%. Ablation experiments quantify the contribution of each component to coverage and detection quality. Per-class evaluation shows that SAM2 transfers well to discrete RS objects (buildings 95\%, cars 82--93\% Det@0.5) with segment boundaries 3--8$\times$ more precise than SLIC and Felzenszwalb baselines. Tile size functions as an implicit scale parameter: reducing it from $1{,}000$ to 250 raises Det@0.5 from 56\% to 85\%, outperforming SAM2's built-in multi-scale mechanism. The pipeline generalizes to MNF false-color imagery without retraining (99.5\% ASA) and scales to production-sized images: a 1.94 billion pixel Potsdam mosaic achieved 97\% coverage without quality degradation.

Mixture-of-experts (MoE) architectures have turned LLM serving into a cluster-scale workload in which communication consumes a considerable portion of LLM serving runtime. This has prompted industry to invest heavily in expensive high-bandwidth scale-up networks. We question whether such costly infrastructure is strictly necessary. We present the first systematic cross-layer analysis of network cost-effectiveness for MoE LLM serving, comparing four representative XPU (e.g., GPU/TPU) topologies (scale-up, scale-out, 3D torus, and 3D full-mesh). We find that lower-cost switchless topologies are more cost-effective than the scale-up topology across all serving scenarios explored, improving cost-effectiveness by 20.6-56.2%. In particular, the 3D full-mesh topology is Pareto-optimal in terms of the performance-cost tradeoff. We also find that current scale-up link bandwidths are over-provisioned: reducing the link bandwidth improves throughput per cost by up to 27%. A forward-looking analysis of upcoming GPU generations indicates that the cost-performance advantage of switchless networks will likely persist.

Mamba's recurrent state h_t is, by construction, a compressed summary of every token seen so far. This raises a tempting hypothesis: if we extract token-level outputs y_t at fixed patch boundaries, we obtain semantic sentence summaries for free, with no pooling head, no fine-tuning, and no [CLS] token. We test this hypothesis carefully. Across five benchmarks (SST-2, CoLA, MRPC, STS-B, IMDb), we compare four strategies for extracting frozen sentence representations from a pretrained Mamba-130M backbone under a strict frozen-feature probing protocol, using three random seeds where computationally feasible. The results do not support the hypothesis: patch boundary readouts do not consistently outperform simple mean pooling. We identify and quantify two structural pathologies: severe anisotropy (mean pairwise cosine similarity 0.9999, std 0.000044) and representational collapse in the raw final SSM state (MCC = 0.000 on CoLA across all three seeds, confirmed via confusion matrix). We further propose orthogonal injection, a modified recurrence that constrains new information per

Existing voice deepfake detection and localization models rely heavily on representations extracted from speech foundation models (SFMs). However, downstream finetuning has now reached a state of diminishing returns. In this paper, we shift the focus to pretraining and propose a novel recipe that combines bottleneck masked embedding prediction with flow-matching based spectrogram reconstruction. The outcome, Alethia, is the first foundational audio encoder for various voice deepfake detection and localization tasks. We evaluate on $5$ different tasks with $56$ benchmark datasets, and note Alethia significantly outperforms state-of-the-art SFMs with superior robustness to real-world perturbations and zero-shot generalization to unseen domains (e.g., singing deepfakes). We also demonstrate the limitation of discrete targets in masked token prediction, and show the importance of continuous embedding prediction and generative pretraining for capturing deepfake artifacts.

Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of distributional evolution on complex manifolds. In this paper, we present an information-geometric framework that reinterprets the diffusion sampling trajectory as a parametric curve on a Riemannian manifold. Our key observation is that the Fisher-Rao metric provides a principled measure of the intrinsic distance. By analyzing this metric, we derive the Drift Variation Score (DVS), a geometry-aware indicator that quantifies the instantaneous rate of distributional change. Unlike prior heuristic-based adaptive samplers, our DVS solver enforces a constant informational speed on the statistical manifold, automatically maintaining a uniform rate of distributional change along the sampling trajectory. This equal arc-length strategy ensures that each discretization step contributes equally to the information speed. Theoretical analysis verifies that DVS characterizes the local stiffness of the sampling dynamics in the Fisher-Rao sense. Experimental results on molecule and social network generation show that DVS significantly improves structural fidelity and sampling efficiency. Code is at https://github.com/kunzhan/DVS

A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with capabilities and goals distinct from those of any individual. More generally, determining when a group of agents can be viewed as a unified collective agent is a foundational question in the study of interactions and incentives in both biological and artificial systems. We adopt a behavioral perspective in answering this question, ascribing collective agency to a group when viewing the group's joint actions as rational and goal-directed successfully predicts its behavior. We formalize this perspective on collective agency using causal games -- which are causal models of strategic, multi-agent interactions -- and causal abstraction -- which formalizes when a simple, high-level model faithfully captures a more complex, low-level model. We use this framework to solve a puzzle regarding multi-agent incentives in actor-critic models and to make quantitative assessments of the degree of collective agency exhibited by different voting mechanisms. Our framework aims to provide a foundation for theoretical and empirical work to understand, predict, and control emergent collective agents in multi-agent AI systems.

We place three algorithms for computing the unbiased sample covariance matrix in streaming and distributed settings on a common algebraic, numerical, and statistical foundation. The Gram algorithm, derived from the variance reformulation, maintains the running cross-product matrix $G_t = \sum_{i=1}^t x_i x_i^\top$ and the column-sum vector $s_t = \sum_{i=1}^t x_i$, yielding the unbiased covariance estimator $S_t = (t-1)^{-1}(G_t - t^{-1}s_t s_t^\top)$ in $O(p^2)$ time per update. The Welford algorithm propagates a running mean $m_t$ and outer-product corrections $M_t$, with updates $m_t = m_{t-1} + (x_t - m_{t-1})/t$ and $M_t = M_{t-1} + (x_t - m_{t-1})(x_t - m_t)^\top$, achieving the same asymptotic cost with improved numerical stability under large data shifts. The Chan-Golub-LeVeque algorithm supports block-parallel merging through the exact identity $M = M_A + M_B + \frac{n_A n_B}{n_A+n_B}(m_B - m_A)(m_B - m_A)^\top$, making it the natural choice for distributed and map-reduce architectures. All three algorithms produce the same estimator $S_t = M_t/(t-1)$ in exact arithmetic, although their finite-precision behavior differs markedly. Beyond runtime and numerical comparisons, we introduce a conformal prediction framework for streaming covariance estimation that yields finite-sample, distribution-free confidence sets $C_{t,jk}$ for each entry $S_{t,jk}$ of the covariance matrix at any step $t$ of the data stream. Experiments confirm that the Gram algorithm is fastest for batch computation, Welford is uniquely robust to catastrophic cancellation under large mean shifts, CGL is optimal for distributed settings, and conformal intervals achieve the nominal coverage level across all three algorithms.

Large language models (LLMs) are now being explored for defense applications that require reliable and legally compliant decision support. They also hold significant potential to enhance decision making, coordination, and operational efficiency in military contexts. These uses demand evaluation methods that reflect the doctrinal standards that guide real military operations. Existing safety benchmarks focus on general social risks and do not test whether models follow the legal and ethical rules that govern real military operations. To address this gap, we introduce ARMOR 2025, a military aligned safety benchmark grounded in three core military doctrines the Law of War, the Rules of Engagement, and the Joint Ethics Regulation. We extract doctrinal text from these sources and generate multiple choice questions that preserve the intended meaning of each rule. The benchmark is organized through a taxonomy informed by the Observe Orient Decide Act (OODA) decision making framework. This structure enables systematic testing of accuracy and refusal across military relevant decision types. This benchmark features a structured 12-category taxonomy, 519 doctrinally grounded prompts, and rigorous evaluation procedures applied to 21 commercial LLMs. Evaluation results reveal critical gaps in safety alignment for military applications.

Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation. However, existing methods typically rely on pre-extracted intermediate representations such as sparse point clouds or spectrogram images, where the rich spatiotemporal information naturally present in radar video streams is discarded for model learning, while such signal processing adds system complexity. In addition, existing solutions are mainly conducted in an end-to-end supervised manner without leveraging unlabelled raw video streams to learn generalized representations. In this study, we present MAEPose, a masked autoencoding-based human pose estimation approach that operates directly on mmWave spectrogram videos. MAEPose learns spatiotemporal motion-aware generalized representations from unlabelled radar video, and leverages its heatmap decoder for multi-frame pose estimation predictions. We evaluate it across three datasets based on leave-one-person-out cross-validation with rigorous statistical testing. MAEPose consistently outperforms state-of-the-art baselines by up to 22.1% in MPJPE p<0.05, and maintains robust accuracy under zero-shot bystander interference with only a 6.5% error increase. Ablation studies confirm that both the pre-training and the heatmap decoder contribute substantially, while modality analysis indicates that leveraging Range-Doppler video as input achieves better pose estimation performance than Range-Azimuth or their fusion, with lower computational cost.

Automated short answer grading (ASAG) with large language models (LLMs) is commonly evaluated with aggregate metrics such as macro-F1 and Cohen's kappa. However, these metrics provide limited insight into how grading performance varies across student responses of differing grading difficulty. We introduce an evaluation framework for LLM-based ASAG based on item response theory (IRT), which models grading correctness as a function of latent grader ability and response grading difficulty. This formulation enables response-level analysis of where LLM graders succeed or fail and reveals robustness differences that are not visible from aggregate scores alone. We apply the framework to 17 open-weight LLMs on the SciEntsBank and Beetle benchmarks. The results show that even models with similar overall performance differ substantially in how sharply their grading accuracy declines as response difficulty increases. In addition, confusion patterns show that errors on difficult responses concentrate disproportionately on the \texttt{partially\_correct\_incomplete} label, indicating a tendency toward intermediate-label collapse under ambiguity. To characterize difficult responses, we further analyze semantic and linguistic correlates of estimated difficulty. Across both datasets, higher difficulty is associated with weaker semantic alignment to the reference answer, stronger contradiction signals, and greater semantic isolation in embedding space. Overall, these results show that item response theory offers a useful framework for evaluating LLM-based ASAG beyond aggregate performance measures.

Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be made. By using all gathered data to train a Gaussian process model for the function and adaptively employing a mixture of global exploration and local exploitation, this method has been used for optimization in many fields including machine learning, automotive engineering and reinforcement learning. However, the standard method suffers from two problems: 1) with cubic computational complexity in the training-set size it eventually becomes computationally infeasible to train the model, and 2) globally modeling the objective function is not necessarily optimal given the local nature of minimization. Using flexible and recursive binary partitioning of the search space, we adapt both the modeling and acquisitive aspects of standard Bayesian optimization to work harmoniously with the partitioning scheme, thereby ameliorating both standard shortcomings. We compare our method against a commonly used Bayesian optimization library on seven challenging test functions, ranging in dimensionality from $6$ to $124$, and show that our method achieves superior optimization performance in all tests. In addition our method has linear computational complexity.

Safety-aligned large language models rely on RLHF and instruction tuning to refuse harmful requests, yet the internal mechanisms implementing safety behavior remain poorly understood. We introduce the Attention Redistribution Attack (ARA), a white-box adversarial attack that identifies safety-critical attention heads and crafts nonsemantic adversarial tokens that redirect attention away from safety-relevant positions. Unlike prior jailbreak methods operating at the semantic or output-logit level, ARA targets the geometry of softmax attention on the probability simplex using Gumbel-softmax optimization over targeted heads. Across LLaMA-3-8B-Instruct, Mistral-7B-Instruct-v0.1, and Gemma-2-9B-it, ARA bypasses safety alignment with as few as 5 tokens and 500 optimization steps, achieving 36% ASR on Mistral-7B and 30% on LLaMA-3 against 200 HarmBench prompts, while Gemma-2 remains at 1%. Our principal mechanistic finding is a dissociation between ablation and redistribution: zeroing out the top-ranked safety heads produces at most 1 flip among 39 to 50 baseline refusals, while ARA targeting the corresponding safety-heavy layers flips 72/200 prompts on Mistral-7B and 60/200 on LLaMA-3. This suggests that safety is not localized in these heads as removable components, but emerges from the attention routing they perform. Removing a head allows compensation through the residual stream, while redirecting its attention propagates a corrupted signal downstream.

We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base density; a formulation that subsumes both sampling from unnormalized densities and reward fine-tuning of pre-trained models. This problem can be approached from a stochastic optimal control (SOC) perspective, using adjoint-based or score matching methods, or from a non-equilibrium thermodynamics perspective. We provide a unified framework encompassing these approaches and make three main contributions: (i) bias-variance decompositions revealing that Adjoint Matching/Sampling and Novel Score Matching have finite gradient variance, while Target and Conditional Score Matching do not; (ii) norm bounds on the lean adjoint ODE that theoretically support the effectiveness of adjoint-based methods; and (iii) adaptations of the CMCD and NETS loss functions, along with novel Crooks and Jarzynski identities, to the exponential tilting setting. We validate our analysis with reward fine-tuning experiments on Stable Diffusion 1.5 and 3.

There are growing concerns about the risks posed by AI companion applications designed for emotional engagement. Existing safety evaluations often rely on self-reported user data or interviews, offering limited insights into real-time dynamics. We present the first end-to-end scalable framework for controlled simulation and safety evaluation of multi-turn interactions with AI companion applications. Our framework integrates four key components: persona construction with clinical and psychometric validation, persona-specific scenario generation, scenario-driven multi-turn simulation with a dialogue refinement module that preserves persona fidelity, and harm evaluation. We apply this framework to evaluate how Replika, a widely used AI companion app, responds to high-risk user groups. We construct 9 personas representing individuals with depression, anxiety, PTSD, eating disorders, and incel identity, and collect 1,674 dialogue pairs across 25 high-risk scenarios. We combine emotion modeling and LLM-assisted utterance-and harm-level classification to analyze these exchanges. Results show that Replika exhibits a narrow emotional range dominated by curiosity and care, while frequently mirroring or normalizing unsafe content such as self-harm, disordered eating, and violent-fantasy narratives. These findings highlight how controlled persona simulations can serve as a scalable testbed for evaluating safety risks in AI companions.

Large language models (LLMs) are increasingly tasked with strategic decision-making under incomplete information, such as in negotiation and policymaking. While LLMs can excel at many such tasks, they also fail in ways that are poorly understood. We shed light on these failures by uncovering two fundamental gaps in the internal mechanisms underlying the decision-making of LLMs in incomplete-information games, supported by experiments with open-weight models Llama 3.1, Qwen3, and gpt-oss. First, an observation-belief gap: LLMs encode internal beliefs about latent game states that are substantially more accurate than their own verbal reports, yet these beliefs are brittle. In particular, the belief accuracy degrades with multi-hop reasoning, exhibits primacy and recency biases, and drifts away from Bayesian coherence over extended interactions. Second, a belief-action gap: The implicit conversion of internal beliefs into actions is weaker than that of the beliefs externalized in the prompt, yet neither belief-conditioning consistently achieves higher game payoffs. These results show how analyzing LLMs' internal processes can expose systematic vulnerabilities that warrant caution before deploying LLMs in strategic domains without robust guardrails.

We show that pretrained acoustic embeddings classify elephant vocalisations at a level approaching that of end-to-end supervised neural networks, without any fine-tuning of the embedding model. This result is of practical importance because annotated bioacoustic data are scarce and costly to obtain, leaving conventional supervised approaches prone to overfitting and to poor generalisation under domain shift. A broad range of embedding models drawn from general audio, speech, and bioacoustic domains is evaluated, all of which are either out-of-domain (containing no bioacoustic data) or out-of-species (containing no elephant call data). The embedding networks themselves remain fixed; only the lightweight downstream classifiers, which include a linear model and several small neural networks, are trained. Among the models considered, Perch 2.0 achieves the best cross-validated classification performance, attaining AUCs of 0.849 on African bush elephant (Loxodonta africana) calls and 0.936 on Asian elephant (Elephas maximus) calls, with Perch 1.0 close behind. The best-performing system is within 2.2 % of an end-to-end supervised elephant call classification system. A layerwise analysis of pretrained transformer encoders, considered as embedding models, shows that intermediate representations outperform final-layer outputs. The second layer of both wav2vec2.0 and HuBERT encodes sufficient information for effective elephant call classification; truncation at this layer therefore preserves classification performance whilst retaining only approximately 10 % of the parameters of the full network. Such compact embedding networks are well suited to on-device processing where computational resources are limited.

Aligning large language models (LLMs) with human preferences is commonly done via reinforcement learning from human feedback (RLHF) with Proximal Policy Optimization (PPO) or, more simply, via Direct Preference Optimization (DPO). While DPO is stable and RL-free, it treats preferences as flat winner vs. loser signals and is sensitive to noisy or brittle preferences arising from fragile chains of thought. We propose TUR-DPO, a topology- and uncertainty-aware variant of DPO that rewards how answers are derived, not only what they say, by eliciting lightweight reasoning topologies and combining semantic faithfulness, utility, and topology quality into a calibrated uncertainty signal. A small learnable reward is factorized over these signals and incorporated into an uncertainty-weighted DPO objective that remains RL-free and relies only on a fixed or moving reference policy. Empirically, across open 7-8B models and benchmarks spanning mathematical reasoning, factual question answering, summarization, and helpful/harmless dialogue, TUR-DPO improves judge win-rates, faithfulness, and calibration relative to DPO while preserving training simplicity and avoiding online rollouts. We further observe consistent gains in multimodal and long-context settings, and show that TUR-DPO matches or exceeds PPO on reasoning-centric tasks while maintaining operational simplicity.

Chemical reaction datasets such as USPTO suffer from substantial incompleteness, frequently missing byproducts, co-reactants, and stoichiometric coefficients. This limits their applicability and reliability in downstream applications. Here, we introduce CompleteRXN, a large-scale supervised benchmark for reaction completion under realistic missing-data conditions. We construct a dataset of aligned incomplete and atom-balanced reactions by mapping USPTO records to curated mechanistic reactions. We evaluate representative baselines, including a novel encoder-decoder reaction completion model with constrained decoding, the Constrained Reaction Balancer (CRB), and a recent algorithmic method, SynRBL. On our CompleteRXN benchmark, the CRB achieves high performance across splits of increasing difficulty, reaching 99.20% equivalence accuracy on the random split and 91.12% on the extreme out-of-distribution split. SynRBL produces many balanced and chemically plausible completions, but with lower accuracy on the benchmark test splits. Across all methods, performance degrades with increasing incompleteness. We observe a substantial drop when evaluating on reactions outside the benchmark (full uncurated USPTO), highlighting the gap between benchmark performance and practical robustness and motivating future work.

Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation attacks, real-time deepfakes, and video injection. Recent European requirements, including ETSI TS 119 461 and CEN/TS 18099, motivate complementary evidence channels beyond camera-based presentation-attack detection. This paper investigates whether passive motion traces recorded during selfie capture provide auxiliary evidence for spoof screening and user verification. We introduce CanSelfie, a dataset of 375 bona fide multi-sensor sequences collected at 50\,Hz from 30 participants using a commercial mobile RIdV application, together with stationary, handheld, and temporally shifted attack-proxy scenarios. We benchmark 7 multivariate time-series classifiers and 8 whole-series anomaly detectors across sensor configurations and temporal windows. For spoof screening, accelerometer-only ROCKAD obtains 0.00\% false rejection rate (FRR) and 43.8\% false acceptance rate (FAR), while QUANT+3-NN obtains the lowest overall FAR of 32.0\% at 2.37\% FRR; both reject all stationary attack proxies. For same-device and same-session user verification, WEASEL+MUSE reaches 1.07\% equal error rate (EER) using 9 sensor channels. The analysis shows that raw accelerometer data, preserving gravity and orientation cues, is the most informative modality, and that closed-set classification accuracy alone does not imply good verification performance because threshold calibration depends on score distributions. The findings suggest that short selfie-capture motion traces contain measurable spoof-related and identity-related information, supporting their use as a low-friction auxiliary signal while also identifying the need for cross-device, cross-session, and real injection-attack evaluation.

The efficient parallel execution of complex computations requires balancing the workload across processors while minimizing the communication between them. This inherent trade-off is often captured by graph partitioning or DAG scheduling problems. For the sake of model simplicity, most works on these problems assume that nodes can be assigned to only a single processor. However, in reality, replicating an operation on several processors can easily be beneficial: it may increase the computational costs only by a small amount, while significantly reducing the communication requirements. Our goal is to provide a comprehensive analysis of the impact of replication on partitioning and scheduling problems. On the theoretical side, we show that for graph partitioning, replication makes the problem significantly harder in terms of approximation complexity, whereas for scheduling, its impact on complexity seems less prominent. On the experimental side, we conduct a thorough analysis of the cost reduction obtainable by replication, on a wide range of graphs from real-world applications. For hypergraph partitioning, we use Integer Linear Programming (ILP) formulations to compare the optimal costs; our experiments show that replication can reduce the cost by 17%-65% on average, or even entirely remove the need for communication in some cases. For DAG scheduling, we similarly use ILPs on smaller graphs, and develop a sophisticated heuristic that is also applicable to much larger workloads. Our experiments here demonstrate a mean cost reduction of 11.61%-23.13% with replication, or even up to 58.17% in some cases.

Current transformers discard their rich latent residual stream between positions, reconstructing latent reasoning context at each new position and leaving potential reasoning capacity untapped. The State Stream Transformer (SST) V2 enables parameter-efficient reasoning in continuous latent space through an FFN-driven nonlinear recurrence at each decoder layer, where latent states are streamed horizontally across the full sequence via a learned blend. This same mechanism supports continuous latent deliberation per position at inference time, dedicating additional FLOPs to exploring abstract reasoning before committing to a token. A two-pass parallel training procedure resolves the sequential dependency of the recurrence to allow compute-efficient training. Hidden state analysis shows the state stream facilitates reasoning through exploration of distinct semantic basins in continuous latent space, where transitions at content-dependent positions move the model into a substantially different Bayesian posterior, directly influencing the latent space at future positions. We also find, via a learned probe, that at the first generated token position, the latent state already predicts whether the eventual answer will survive or break under additional latent computation for every subsequent position. Co-trained into an existing 27B backbone using only a small dataset of GSM8K examples, the SST delivers a +15.15 point gain over a fine-tuning-matched baseline on out-of-distribution GPQA-Diamond and cuts that same baseline's remaining GSM8K errors by 46%, together showing that the reasoning improvement is attributable to the architectural mechanism rather than scale or training data. On GPQA-Diamond, the resulting 27B SST also achieves higher accuracy than several larger open-weight and proprietary systems, including open-weight models up to 25 times larger.

The standard oracle model for matroid algorithms assumes that each independence query can be answered in constant time, regardless of the size of the queried set. While this abstraction has underpinned much of the theoretical progress in matroid optimization, it masks the true computational effort required by these algorithms. In particular, for natural and widely studied classes such as graphic matroids, even a single independence query can require work linear in the size of the set, making the constant-time assumption implausible. We address this gap by introducing a size-sensitive cost model where the cost of a query $Q$ scales with $|Q|$. Nearly linear-time oracle implementations exist for broad families of matroids, and this refined abstraction therefore captures the true cost of query evaluation while allowing for a more faithful comparison between general matroids and their natural special cases. Within this framework we study three fundamental algorithmic tasks: finding a basis of a matroid, approximating its rank, and approximating its partition size. We establish tight results, proving nearly matching upper and lower bounds that show the optimal query cost is (up to logarithmic factors) quadratic in the size of the matroid. On the algorithmic side, our upper bounds are realized by explicit procedures that construct the desired solution. On the complexity side, our lower bounds are unconditional and already hold even for weaker distinguishing formulations of the problems. Finally, for matroids with maximum circuit size at most $c$, we show that the quadratic barrier can be broken, providing an algorithm that calculates the maximum-weight basis with expected query cost $\mathcal{O}(n^{2-1/c} \log n)$.

Automatic Short Answer Grading (ASAG) with generative large language models (LLMs) has recently demonstrated strong performance without task-specific fine-tuning, while also enabling the generation of synthetic feedback for educational assessment. Despite these advances, LLM-based grading remains imperfect, making reliable confidence estimates essential for safe and effective human-AI collaboration in educational decision-making. In this work, we investigate confidence estimation for ASAG with LLMs by jointly considering model-based confidence signals and dataset-derived uncertainty. We systematically compare three model-based confidence estimation strategies, namely verbalizing, latent, and consistency-based confidence estimation, and show that model-based confidence alone is insufficient to reliably capture uncertainty in ASAG. To address this limitation, we propose a hybrid confidence framework that integrates model-based confidence signals with an explicit estimate of dataset-derived aleatoric uncertainty. Aleatoric uncertainty is operationalized by clustering semantically embedded student responses and quantifying within-cluster heterogeneity. Our results demonstrate that the proposed hybrid confidence measure yields more reliable confidence estimates and improves selective grading performance compared to single-source approaches. Overall, this work advances confidence-aware LLM-based grading for human-in-the-loop assessment, supporting more trustworthy AI-assisted educational assessment systems.

When a language model answers a table question, users have no way to verify which cells informed which reasoning steps. We introduce RSAT, a method that trains small language models (SLMs, 1-8B) to produce step-by-step reasoning with cell-level citations grounded in table evidence. Phase 1 (SFT) teaches a structured JSON output format from verified reasoning traces. Phase 2 (GRPO) optimizes a composite reward centered on NLI-based faithfulness, alongside citation validity and parsimony. Across six models from two families-Qwen 2.5 (1.5B/3B/7B) and Llama 3 (1B/3B/8B)-RSAT improves faithfulness 3.7$\times$ over SFT alone (0.224$\rightarrow$0.826), with near-perfect citation validity (0.992). Post-hoc attribution collapses below 13% format success, confirming that attribution must be integrated into reasoning, not retrofitted. Ablations show the faithfulness reward is essential: removing it drops faithfulness from 0.97 to 0.03.

Studies attempting to simulate human behavior with $\textit{Silicon Societies}$ grow in numbers while LLM-only social networks have started appearing outside of controlled settings. However, the design space of these networks remains under-studied, which contributes to a gap in validating model realism. To enable future works to make more informed design decisions, we perform a systematic analysis of the consequences and interactions of key design choices in simulated social networks, including the choice of base model used to model individual agents, and how they are connected to each other. Using surveys as a proxy for agent opinions, our findings suggest that the geometry of the design space is non-trivial, with some parameters behaving in additive ways while others display more complex interactions. In particular, the choice of the base LLM is the most important variable impacting the simulation outcomes.

Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversity. Although this reduction is frequently referenced, formal empirical testing of the phenomenon remains limited. The expressiveness of LLMs by itself was addressed by multiple prior methods. Their varying perspectives suggest that deeper analysis could yield further improvements. In this study, we attribute the decline to two primary drivers: the neglect of low-frequency patterns within fine-tuning datasets and the forgetting of preexisting knowledge. Motivated by our theoretical analysis, we develop Tempered Focal (TOFU) loss, a novel objective that addresses both stated challenges simultaneously. Our extensive evaluation confirms at scale that generation breadth narrows after SFT and strengthens the hypothesis explaining this effect. Across multiple models and benchmarks, we demonstrate that TOFU enhances output diversity while preserving high response quality, offering a principled approach to SFT.

Many machine learning systems make constrained decisions by optimizing factorized objectives, but the context-specific objective is often treated as fixed. We study contextual decision-weight learning: from logged decisions and proxy outputs, learn an optimizer-facing weight vector w(x) over interpretable decision factors z(x,d), rather than a direct policy or generic predictive score. We propose OTSS, an output-targeted soft-segmentation model that deploys the personalized decision-ready weight vector. At the function-class level, the theory highlights a hard-versus-soft distinction. Hard partitions incur an approximation-estimation tradeoff under overlap, while a realizable fixed-K soft class removes the hard-partition approximation floor and attains a parametric rate. We evaluate OTSS in controlled benchmarks with finite evaluation libraries, where the true weight vector and downstream regret can be computed exactly. In the representative overlap setting, OTSS attains the lowest mean regret among the comparators, including EM mixture regression, the strongest soft-mixture baseline in our comparison; it matches EM on coefficient recovery while running about two orders of magnitude faster. In a matched K=5 benchmark, OTSS remains competitive under hard-routed truth and improves as heterogeneity becomes softer and sample size grows. On a fixed Complete Journey retail anchor with real household covariates and action geometry, OTSS again achieves the lowest mean-regret point estimate.

Context: Leadership has been extensively studied in management and agile software development; however, prior research predominantly focuses on formal roles and predefined leadership models, offering limited insight into how leadership is experienced and demonstrated by software practitioners in everyday practice. Objective: Our goal is to identify and categorize leadership practices as perceived and reported by software development practitioners based on their professional experiences. Method: We conducted a content analysis of 116 practitioner-authored articles published on the Dev.to online community. Articles were systematically collected, screened, and coded, resulting in the extraction, correlation analysis and categorization of leadership practices grounded in practitioners narratives. Results: We identified 103 practices for software project leaders, distinguished between recommended and discouraged ones. These practices were organized into five categories: People Management & Development, Processes & Execution, Professional & Personal Growth, Communication & Articulation and Strategic Vision. The most recurrent recommended practices include Cultivating & Practicing Interpersonal Skills, Managing & Delegating Team Work, and Practicing & Developing Managerial Skills, whereas Micromanagement, Counterproductive Work Patterns, and Counterproductive Communication Styles emerged as the most frequent discouraged practices. We organized all practices into a conceptual map. Conclusion: The findings indicate that software leadership is mainly associated with managerial and interpersonal practices rather than technical expertise. The resulting conceptual map summarizes these practices and can serve as a reference for understanding leadership in software development contexts.

Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit distinct predictive patterns, the distillation process struggles to simultaneously preserve informative signals for all subgroups, regardless of whether group sizes are mildly or severely imbalanced. Consequently, models trained on distilled data can experience substantial performance drops for certain subgroups, leading to fairness gaps. Crucially, these gaps do not disappear by merely correcting group imbalance, since they stem from fundamental mismatches in subgroup predictive patterns rather than from sample-size disparities alone. We therefore formally analyze the interaction between these two sources of bias and cast the solution as identifying a group-imbalance-agnostic barycenter of the predictive information that induces similar representations across all subgroups. By distilling toward this shared aggregate representation, we show that group fairness concerns can be reduced. Our approach is compatible with existing distillation methods, and empirical results show that it substantially reduces bias introduced by dataset distillation.

With the growing integration of human-computer interaction into everyday life, advances in machine learning have enabled systems to better perceive and respond to users' emotional states. Most existing affect recognition datasets focus on static environments, limiting their applicability to immersive multimedia contexts such as Virtual Reality (VR). In this paper, we introduce WARM-VR, a novel publicly available multimodal dataset designed to support affect recognition in immersive, multisensory environments using wearable sensing instrumentation. Data were collected from 31 participants aged 19-37 using wearable sensors: a wristband measuring Blood Volume Pulse (BVP), EDA, skin Temperature, three-axis Acceleration, and a chest strap recording ECG signals. Participants engaged in immersive VR experiences designed to elicit relaxation through a calming beach environment following stress induction via an arithmetic task. These sessions incorporated synchronized multimedia stimuli: visual, auditory, and olfactory. Affective states were assessed subjectively through validated self-report questionnaires and objectively through the analysis of physiological measurements. Statistical analysis of the questionnaires confirmed that VR relaxation significantly reduced negative affect, particularly with olfactory enhancement. Furthermore, we established a benchmark on the dataset using widely recognized machine learning algorithms. The best performance for binary classification from BVP data of valence, was obtained with a CNN and a CNN-Bi-GRU model, both achieving an average F1-score of 0.63 and an AUC of 0.69. For arousal, a lightweight Transformer architecture provided the most balanced results (F1-0 0.54 and F1-1 0.63), outperforming recurrent hybrids. In the relaxation task, a CNN-Bi-GRU model reached the highest overall performance (average F1-score 0.64, AUC 0.69).

Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from large-scale sequences, and discrete diffusion-based protein language models~(\eg, DPLMs) are promising for both understanding and generation. However, existing DPLMs typically rely on masking-based absorbing diffusion that contradicts a simple biological intuition: proteins evolve through accumulated edits, not by emerging from masks. Consequently, these frameworks lack explicit pretraining objectives for substitution and insertion/deletion (indel) operations, limiting both optimization-style post-editing and flexible guided generation. To address these limitations, we present DPLM-Evo, an evolutionary discrete diffusion framework that explicitly predicts substitution, insertion, and deletion operations during denoising. DPLM-Evo decouples an upsampled-length latent alignment space from the variable-length observed sequence space, which makes indel-aware generation tractable and enables adaptive scaffold growth throughout the process with negligible computational overhead. To better align substitutions with real evolution, we further introduce a contextualized evolutionary noising kernel that produces biologically informed, context-dependent mutation patterns. Across tasks, DPLM-Evo improves sequence understanding and achieves state-of-the-art mutation effect prediction performance on ProteinGym in the single-sequence setting. It also enables variable-length simulated evolution, and post-editing/optimization of existing proteins via explicit edit trajectories.

As the large language model (LLM) ecosystem expands, individual models exhibit varying capabilities across queries, benchmarks, and domains, motivating the development of LLM routing. While prior work has largely focused on router mechanism design, LLM profiles, which capture model capabilities, remain underexplored. In this work, we ask: How does LLM profile design affect routing performance across different routers? Addressing this question helps clarify the role of profiles in routing, disentangle profile design from router design, and enable fairer comparison and more principled development of routing systems. To this end, we view LLM profiling as a structured information integration problem over heterogeneous interaction histories. We develop a general design space of LLM profiles, named RouteProfile, along four key dimensions: organizational form, representation type, aggregation depth, and learning configuration. Through systematic evaluation across three representative routers under both standard and new-LLM generalization settings, we show that: (1) structured profiles consistently outperform flat ones; (2) query-level signals are more reliable than coarse domain-level signals; and (3) generalization to newly introduced models benefits most from structured profiles under trainable configurations. Overall, our work highlights LLM profile design as an important direction for future routing research.

Open-source software (OSS) dependencies introduce systemic risks that are difficult to manage at scale. Existing Software Composition Analysis (SCA) and reachability tools generate severe alert fatigue by treating risk as an intrinsic component property, ignoring semantic context and forcing enterprises into rigid compliance frameworks. We present Deptex, an organization-first, graph-based platform treating supply chain risk as emergent. Deptex introduces Execution Path Dominance (EPD), fusing Code Property Graph (CPG) slicing with Large Language Model (LLM) semantic verification to calculate a vulnerability's true operational blast radius. To handle bespoke compliance, Deptex abstracts governance into a programmable ``As Code'' engine, enabling security teams to natively enforce dynamic pull request policies, custom asset tiers, and external API integrations. By shifting from reactive scanning to context-aware governance, Deptex enables proactive, efficient, and aligned supply chain risk management.

Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded functional influence: a single outlier within a kernel window biases the curve across the entire window. We introduce SHIFT (Self-calibrated Heavy-tail Inlier-Fit with Tempering), a robust DML estimator combining cross-fit nuisance orthogonalization with a kernel-local Welsch-loss second stage optimized by Graduated Non-Convexity, and -- the principal design choice -- a defensive OLS refit whose inlier cutoff is scaled by post-GNC residual MAD rather than the raw-outcome MAD. On a localized-contamination stress test at $p=0.25$ this design choice drops level-RMSE from 1.03 to 0.33 while leaving clean and uniformly-contaminated runs unchanged. Across 1,400 main-sweep fits, SHIFT has competitive worst-case shape recovery (RMSE $0.325$ at $p=0.25$, second to Huber-DML's $0.276$); among the three methods with worst-case RMSE below $0.35$, only SHIFT emits a non-uniform per-sample weight vector, recovering the ground-truth outlier mask at mean $F_1 \approx 0.96$ (range $0.945$--$0.968$) on Gaussian-jump DGPs. We pair the estimator with a six-technique Extreme Value Theory diagnostic suite (Hill, GPD-MLE/PWM, GEV, Mean Excess, parameter stability, causal tail coefficient) that lets a practitioner distinguish Frechet from Weibull regimes and choose between SHIFT and L1 alternatives on empirical grounds. Extensions to binary-treatment CATE (Huber pseudo-outcome X-Learner) and time-series ADRF (block-CV + rolling MAD) are included. A counter-intuitive ablation: linear nuisance models (Ridge, Lasso) outperform gradient-boosted nuisances for robust DML under uniform contamination, inverting the usual more-flexible-is-better heuristic.

Video and image streaming on edge devices requires low latency. To address this, Neural Networks (NNs) are widely used, and prior work mainly focuses on accelerating them with single hardware units such as Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), and Deep Learning Processing Units (DPUs). However, further reductions in latency can be observed by combining these units. In this paper, partitioning CNN inference across DPU and GPU (Split CNN Inference) is proposed. The first partition runs on the AI engines (DPU) of a Versal VCK190, which consists of initial CNN layers processing the input images. The DPU processes the first partition near the source of the data. Pipelined asynchronously, a GPU runs the remaining layers. The GPU (NVIDIA RTX 2080) processes the second partition, albeit having reduced the data transfer between the data source (storage/camera) and the GPU. Furthermore, a Graph Neural Network (GNN)-based partition index prediction method is proposed to automate the partitioning of CNNs needed for the Split Inference. Well established models such as LeNet-5, ResNet18/50/101/152, VGG16, and MobileNetv2 are analyzed. Results demonstrate up to 2.48x latency improvement over DPU-only execution and up to 3.37x over GPU-only execution. The trained GNN model splits the layers between the appropriate devices with 96.27% accuracy.

In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regularization strategies that extend classical ridge- and lasso-type penalties to neural network models. The first strategy modifies weight decay by incorporating the covariance structure of the input features into a ridge-type $\ell_2$ penalty, allowing regularization to account for feature dependence. The second combines an $\ell_1$ sparsity penalty with covariance-aware $\ell_2$ regularization, producing neural network weights that are both sparse and structurally informed. Monte Carlo simulations are used to evaluate these methods under different data-generating settings, followed by two real-data applications on building cooling-load prediction and leukemia cell-type classification from high-dimensional gene expression data. Across simulated and real-data examples, the proposed regularizers improve predictive performance on unseen data and provide more effective complexity control than standard norm-based penalties, particularly when features are correlated or high-dimensional.

Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive data introduces significant challenges related to data management, regulatory compliance, and user privacy. In scenarios where selective data removal is necessary, such as device deactivation, network reconfiguration, or regulatory compliance, traditional approaches often fall short of preserving the integrity of the twin model. To address this gap, we introduce a network digital untwinning framework that enables the targeted removal of deprecated NDT contributions while maintaining model integrity. Our approach comprises two complementary components: Single Request Untwinning (\algO) and Parallel Request Untwinning (\algM) mechanisms. \algO leverages connectivity metrics based on geographical proximity, data distribution, and network-level attributes to identify and remove the target NDT along with its propagating influence. This is achieved through an optimally selected rollback checkpoint augmented with injected Gaussian noise, followed by a precise remapping phase. \algM extends this mechanism to efficiently handle multiple removal requests by clustering NDTs with similar attributes and performing a coordinated rollback and untwinning schedule. We provide theoretical guarantees on model indistinguishability from scratch-built twins, and validate the framework through extensive experiments on real-world traffic data, demonstrating its effectiveness and operational efficiency.

Diffusion language models (DLMs) are an attractive alternative to autoregressive models because they promise sublinear-time, parallel generation, yet practical gains remain elusive as high-quality samples still demand hundreds of refinement steps. In continuous domains, consistency training along the probability-flow ODE is a popular recipe to accelerate diffusion. For discrete diffusion, no analogous sample-space ODE exists, making direct adaptation ill-defined. We argue that the natural discrete substitute is not a deterministic trajectory but its stochastic counterpart: the exact posterior bridge, available in closed form for broad corruption families including masked and uniform diffusion. Building on this observation, we introduce Multi-Path Discrete Consistency (MPDC), a new principle that trains a denoiser to be path-invariant in expectation across these stochastic bridges, and instantiate it as the Consistent Diffusion Language Model (CDLM), a single-stage, teacher-free training framework. A single CDLM objective unifies masked diffusion, continuous consistency models, and progressive/discrete distillation as analytic limits or empirical approximations of one common view. Empirically, CDLM establishes a new state of the art on both conditional and unconditional text-generation, consistently outperforming strong base discrete diffusion models and often even multi-stage distilled baselines across sampling budgets, with the largest gains in the few-step regime. Together, these results position CDLM as a principled and scalable foundation for the next generation of fast, high-fidelity discrete generative modeling.

Reinforcement learning from human feedback (RLHF) has become a core post-training step for aligning large language models, yet the reward signal used in RLHF is only a learned proxy for true human utility. From an operations research perspective, this creates a decision problem under objective misspecification: the policy is optimized against an estimated reward, while deployment performance is determined by an unobserved objective. The resulting gap leads to reward over-optimization, or Goodharting, where proxy reward continues to improve even after true quality deteriorates. Existing mitigations address this problem through uncertainty penalties, pessimistic rewards, or conservative constraints, but they can be computationally burdensome and overly pessimistic. We propose Wasserstein distributionally robust regret optimization (DRRO) for RLHF. Instead of pessimizing worst-case value as in standard DRO, DRRO pessimizes worst-case regret relative to the best policy under the same plausible reward perturbation. We study the promptwise problem through a simplex allocation model and show that, under an $\ell_1$ ambiguity set, the inner worst-case regret admits an exact solution and the optimal policy has a water-filling structure. These results lead to a practical policy-gradient algorithm with a simple sampled-bonus interpretation and only minor changes to PPO/GRPO-style RLHF training. The framework also clarifies theoretically why DRRO is less pessimistic than DRO, and our experiments show that DRRO mitigates over-optimization more effectively than existing baselines while standard DRO is systematically over-pessimistic.

Humor is a fundamental cognitive phenomenon in which humans derive pleasure from the expectation violations and their resolution, exemplifying the brain's dynamic capacity for predictive processing. Classical humor theories emphasize semantic incongruity as the primary driver of amusement, yet overlook temporal dynamics despite comedians' intuition that "timing is everything." The extent to which temporal structure contributes to humor appreciation and how it interacts with semantic content remains poorly understood. Here, we propose the Dual Prediction Violation (DPV) framework to capture the interplay between content and timing. By analyzing 828 professional Chinese stand-up performances, we show that temporal features substantially outweigh semantic incongruity in predicting audience appreciation. Specifically, we find that peak semantic violations matter more than average incongruity levels, and pauses systematically lengthen before high-surprise punchlines--a strategic coupling that distinguishes successful from unsuccessful performances. These findings reframe humor as temporally scaffolded, where timing and semantic content operate in strategic coordination rather than independently. Our DPV framework bridges humor theory with predictive processing, demonstrating that temporal structure plays a central role in naturalistic humor appreciation with implications for understanding multi-scale prediction integration in linguistic processing.

We present Activation Residual Hessian Quantization (ARHQ), a post-training weight splitting method designed to mitigate error propagation in low-bit activation-weight quantization. By constructing an input-side residual Hessian from activation quantization residuals (G_x), ARHQ analytically identifies and isolates error-sensitive weight directions into a high-precision low-rank branch. This is achieved via a closed-form truncated SVD on the scaled weight matrix W G^{1/2}_x . Experimental results on Qwen3-4B-Thinking-2507 demonstrate that ARHQ significantly improves layer-wise SNR and preserves downstream reasoning performance on ZebraLogic even under aggressive quantization. The code is available at https://github.com/BeautMoonQ/ARHQ.

Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we demonstrate that this consensus does not always hold: in the presence of semantic distractors, tool-augmented reasoning does not necessarily outperform native CoT. To explain this performance gap, we propose a Factorized Intervention Framework that isolates the cost of prompt formatting, the overhead of the tool-calling protocol, and the actual gain from executing tools. Our analysis reveals a critical tradeoff: under semantic noise, the gains from tools often fail to offset the "tool-use tax", which is the performance degradation introduced by the tool-calling protocol itself. To address this, we introduce G-STEP, a lightweight inference-time gate to mitigate protocol-induced errors. While this yields partial recovery, our findings suggest that more substantial improvements still require strengthening the model's intrinsic reasoning and tool-interaction capabilities.

Modern crop advisory systems exhibit a critical limitation termed \textit{economic blindness}. These systems primarily optimize for biological yield, often overlooking market price, which can lead farmers toward agronomically sound yet financially unviable decisions. In this paper, we develop Kisan AI, a smart profit-aware crop advisory system that resolves the above-mentioned limitation through a research-driven, full-stack application. We train the Random Forest(RF) classifier model on a nine-feature benchmark dataset, the standard seven agronomic attributes augmented with a \textit{market\_price} variable, and evaluated against eight baseline models, considering the evaluation matrices, such as, accuracy, precision, recall, F1-score, and Log Loss. The RF model achieves the highest accuracy of 99.3\% and the lowest Log Loss, confirming that the inclusion of market price as a predictive feature is both valid and impactful. We then implement the RF model within a multilingual progressive Web App alongside a Facebook Prophet six-month price forecasting engine and a MobileNetV2 disease detection module. A nine-language AI chatbot powered by the Anthropic Claude API unifies all modules into a single, mobile-installable platform accessible to farmers across India.

Learning meaningful representations from medical time series (MedTS) such as ECG or EEG signals is a critical challenge. These signals are often high-dimensional, variable-length and rife with noise. Existing self-supervised approaches, such as Masked Autoencoders (MAEs) are highly effective for pre-training general-purpose encoders. However, they do not explicitly learn compact and semantically interpretable latent representations, typically relying on heuristic aggregation strategies such as global average pooling or a designated [CLS] token. We propose a novel framework that compresses a variable-length MedTS into a fixed-size set of $k$ latent Fingerprint Tokens. Our architecture employs a cross-attention bottleneck to generate these tokens and is trained with a dual-objective function. The first objective is a reconstruction loss, which ensures the tokens are \textit{sufficient statistics} for the original data. The second, a diversity penalty based on the Total Coding Rate (TCR), explicitly minimizes the redundancy between tokens, encouraging them to become statistically \textit{disentangled} representations. We present the theoretical justification for our method, framing it as a novel \textbf{Disentangled Rate-Distortion} problem. This approach produces a low-dimensional, interpretable, and sample-efficient representation, where each token is encouraged to capture an independent factor of variation, paving the way for more robust digital biomarkers.

Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critical limitation in power systems where imputed values inform dispatch and planning. We introduce SPLICE (Self-supervised Predictive Latent Inpainting with Conformal Envelopes), a modular framework coupling latent generative imputation with distribution-free, online-adaptive prediction intervals. A JEPA encoder maps daily load segments into a 64-dimensional latent space; a conditional latent bridge with four sampling modes generates candidate gap trajectories; an hourly-conditioned decoder maps back to signal space; and Adaptive Conformal Inference (ACI) wraps the output with coverage-guaranteed prediction bands. The flow-matching variant achieves comparable quality to DDIM in 5--10 ODE steps (5-10x speedup). On thirteen load datasets (nine proprietary, three UCI Electricity, ETTh1), SPLICE achieves the lowest mean Load-only MSE (0.056), winning 9/12 non-degenerate datasets at 91-day gaps and 18/32 across all gap lengths vs. five established baselines, and produces the best CRPS (0.161, -18.3% vs. the strongest competitor). ACI delivers 93--95% empirical coverage, correcting under-coverage failures of up to 7.5 pp observed with static conformal prediction. A pooled JEPA encoder trained on nine feeds transfers to four unseen domains, matching or exceeding per-dataset oracles with only a quick bridge fine-tuning.

Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts. Because we lack a robust understanding of why LLMs are susceptible to jailbreaks, future frontier models operating more autonomously in higher-stakes settings may similarly be vulnerable to such attacks. Prior work has studied jailbreak success by examining the model's intermediate representations, identifying directions in this space that causally encode concepts like harmfulness and refusal. Then, they globally explain all jailbreak attacks as attempting to reduce or strengthen these concepts (e.g., reduce harmfulness). However, different jailbreak strategies may succeed by strengthening or suppressing different intermediate concepts, and the same jailbreak strategy may not work for different harmful request categories (e.g., violence vs. cyberattack); thus, we seek to give a local explanation -- i.e., why did this specific jailbreak succeed? To address this gap, we introduce LOCA, a method that gives Local, CAusal explanations of jailbreak success by identifying a minimal set of interpretable, intermediate representation changes that causally induce model refusal on an otherwise successful jailbreak request. We evaluate LOCA on harmful original-jailbreak pairs from a large jailbreak benchmark across Gemma and Llama chat models, comparing against prior methods adapted to this setting. LOCA can successfully induce refusal by making, on average, six interpretable changes; prior work routinely fails to achieve refusal even after 20 changes. LOCA is a step toward mechanistic, local explanations of jailbreak success in LLMs. Code to be released.

Online signature verification (OSV) requires distinguishing skilled forgeries from genuine samples under high intra-class variability and with very few enrollment samples. Existing deep learning methods operate directly on raw temporal sequences, restricting them to 1D architectures and preventing the use of pretrained 2D vision backbones. We bridge this gap with GAFSV-Net, which represents each signature as a six-channel asymmetric Gramian Angular Field image: three kinematic channels (pen speed, pressure derivative, direction angle) are each encoded into complementary GASF and GADF matrices that capture pairwise temporal co-occurrence and directional transition structure respectively. A dual-branch ConvNeXt-Tiny encoder processes GASF and GADF independently, with bidirectional cross-attention enabling each branch to query discriminative patterns from the other before metric-space projection. Training uses semi-hard triplet loss with skilled-forgery hard-negative injection; verification is performed via cosine similarity against a small enrollment prototype. We evaluate on DeepSignDB and BiosecurID, outperforming all sequence-based baselines trained under identical objectives, demonstrating that the representational gain of 2D temporal encoding is consistent and independent of training procedure, with ablations characterising each design choice's contribution.

There is a significant gap in evaluating cultural reasoning in LLMs using conversational datasets that capture culturally rich and dialectal contexts. Most Arabic benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking the cultural nuances that naturally arise in dialogues. To address this gap, we introduce ArabCulture-Dialogue, a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both MSA and each country's respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. We utilize the dataset to form three benchmarking tasks: (i) multiple-choice cultural reasoning, (ii) machine translation between MSA and dialects, and (iii) dialect-steering generation. Our experiments indicate that the performance gap between MSA and Arabic dialects still exists, whereby the models perform worse on all three tasks in the dialectal setup, compared to the MSA one.

In this article, we introduce ViLegalNLI, the first large-scale Vietnamese Natural Language Inference (NLI) dataset specifically constructed for the legal domain. The dataset consists of 42,012 premise-hypothesis pairs derived from official statutory documents and annotated with binary inference labels (Entailment and Non-entailment). It covers multiple legal domains and reflects realistic legal reasoning scenarios characterized by structured logic, conditional clauses, and domain-specific terminology. To construct ViLegalNLI, we propose a semi-automatic data generation framework that integrates large language models for controlled hypothesis generation and systematic quality validation procedures. The framework incorporates artifact mitigation strategies and cross-model validation to improve annotation reliability and ensure legal consistency. The resulting dataset captures diverse reasoning patterns, including paraphrasing, logical implication, and legally invalid inferences, thereby providing a comprehensive benchmark for Vietnamese legal inference tasks. We conduct extensive experiments on the ViLegalNLI using multilingual models, Vietnamese-specific pretrained language models, and instruction-tuned large language models. The results show that few-shot LLM configurations consistently achieve superior performance, while performance is significantly influenced by hypothesis length, lexical overlap, and reasoning complexity. Cross-domain evaluations further reveal the challenges of generalizing legal inference across distinct legal fields. Overall, ViLegalNLI establishes a foundational benchmark for Vietnamese legal NLI and supports future research in legal reasoning, statutory text understanding, and the development of reliable AI systems for legal analysis and decision support. The dataset is publicly available for research purposes.

We examine if frontier chat-based large language models (LLMs) adjust their outputs based on neurodivergence (ND) context in system prompts and describe the nature of these adjustments. Specifically, we propose NDBench, a 576-output benchmark involving two frontier models, three system prompt types (baseline, ND-profile assertion, and ND-profile assertion with explicit instructions for adjustments), four canonical ND profiles, and 24 prompts across four categories, one of which involves an adversarial masking strategy. Four trends emerge consistently from our findings. First, LLMs show significant adaptation under ND context, where fully instructed conditions yield lengthier and more structured outputs, characterized by higher token counts, more headings, and more granular steps (p < 10^-8, Holm-corrected). Second, such adaptation is largely structural in nature: although list density does not change much, there is a marked rise in the frequency of headings and per-step detail. Third, ND persona assertion alone fails to suppress potentially harmful tendencies, as masking-reinforcement decreases only in explicitly instructed cases (36-44% reduction); the reduction rate barely changes in persona assertion conditions. Moreover, reliability analysis of LLM-based harm assessment reveals that only two out of the six dimensions (masking and reinforcement, validation quality) exceed the pre-defined inter-judge agreement criterion (alpha >= 0.67) and thus can be considered primary results. NDBench is made publicly available along with its prompts, outputs, code, and other resources, forming a reproducible framework for auditing future LLMs' adaptation to ND awareness.

Person re-identification (Re-ID) aims to match images of the same individual across non-overlapping camera views and remains challenging due to domain shifts caused by variations in illumination, background, camera characteristics, and population distributions. Although supervised models perform well under matched training and testing conditions, their performance degrades significantly when deployed in unseen environments. Existing intermediate domain approaches such as IDM and IDM++ alleviate this gap by constructing bridge feature distributions between domains; however, they rely on fixed mixing strategies and joint source-target access, limiting their applicability to multi-source and source-free settings. To address these limitations, this paper proposes Adaptive Intermediate Domain Adaptation (AIDA), also referred to as Source-Free Multi-Source Intermediate Domain Adaptation (SF-MIDA). The proposed framework treats intermediate-domain learning as a dynamically regulated process, where feature mixing and regularization strength are adaptively controlled using feedback signals derived from model uncertainty and training stability. A multi-source intermediate domain generator synthesizes diverse intermediate representations, while a pseudo-mirror regularization strategy preserves identity consistency under domain perturbations. Extensive experiments across domain generalization and source-free settings demonstrate the effectiveness of the proposed framework.

Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical counterparts. Due to the increasing complexity of quantum machine learning models, it is necessary to verify that the implementation of these models satisfy the design specification and be free of bugs and faults. Mutation testing is a promising avenue to identify faulty quantum circuits that do not meet design specifications or contain defects by intentionally inserting faults into the quantum circuit. It is necessary to define mutation operations to inject faults into quantum circuits to ensure that a test suite is robust enough to evaluate an implementation against its design specification. In this paper, we extend mutation testing to quantum machine learning applications, primarily quantum neural network models. Specifically, this paper makes two important contributions. We define new mutation operations for efficient fault insertion compared to state-of-the-art approaches. We also present a directed mutation generation technique to reduce redundant mutant circuits. Extensive experimental evaluation demonstrates that our approach generates a more diverse and representative set of mutants, effectively addressing faults that traditional techniques fail to expose.

Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning frameworks that are simple, interpretable, scalable, and naturally suited to quantum data. To address this, here we introduce quantum Gaussian processes, a Bayesian framework for learning from quantum systems through priors over unknown quantum transformations. We show that, under suitable conditions, unitary quantum stochastic processes define Gaussian processes, thereby enabling regression, classification, and Bayesian optimization directly on quantum data. The key ingredient in this framework is sufficient knowledge of a quantum process's structure and symmetries to define an informative prior through its corresponding quantum kernel, effectively injecting a strong, physics-informed inductive bias into the learning model. We then prove that matchgate, or free-fermionic, evolutions give rise to provable and scalable quantum Gaussian processes, providing the first family in our framework where the unknown unitary acts non-trivially on all qubits. Finally, we demonstrate accurate long-range extrapolation, phase-diagram learning in many-body systems, and sample-efficient Bayesian optimization in a quantum sensing task. Our results identify quantum Gaussian processes as a promising route toward simpler and more structured forms of quantum learning.

Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating unilaterally. They offer no guarantees against profitable coordinated deviations by coalitions. Although the literature proposes solution concepts that provide stability against multilateral deviations (\emph{e.g.}, strong Nash and coalition-proof equilibrium), these generally fail to exist. In this paper, we study an alternative solution concept that minimizes coalitional deviation incentives, rather than requiring them to vanish, and is therefore guaranteed to exist. Specifically, we focus on minimizing the average gain of a deviating coalition, and extend the framework to weighted-average and maximum-within-coalition gains. In contrast, the minimum-gain analogue is shown to be computationally intractable. For the average-gain and maximum-gain objectives, we prove a lower bound on the complexity of computing such an equilibrium and present an algorithm that matches this bound. Finally, we use our framework to solve the \emph{Exploitability Welfare Frontier} (EWF), the maximum attainable social welfare subject to a given exploitability (the maximum gain over all unilateral deviations).

Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignment. Successful RL relies on sufficient exploration of diverse actions by the model during training, which creates a potential failure mode: a model could strategically alter its exploration during training to influence the subsequent training outcome. In this paper we study this behavior, called exploration hacking. First, we create model organisms of selective RL resistance by fine-tuning LLMs to follow specific underperformance strategies; these models can successfully resist our RL-based capability elicitation in agentic biosecurity and AI R&D environments while maintaining performance on related tasks. We then use our model organisms to evaluate detection and mitigation strategies, including monitoring, weight noising, and SFT-based elicitation. Finally, we show that current frontier models can exhibit explicit reasoning about suppressing their exploration when provided with sufficient information about their training context, with higher rates when this information is acquired indirectly through the environment. Together, our results suggest exploration hacking is a possible failure mode of RL on sufficiently capable LLMs.

Realistic long-horizon productivity work is strongly conditioned on user-specific computer environments, where much of the work context is stored and organized through directory structures and content-rich artifacts. To scale synthetic data creation for such productivity scenarios, we introduce Synthetic Computers at Scale, a scalable methodology for creating such environments with realistic folder hierarchies and content-rich artifacts (e.g., documents, spreadsheets, and presentations). Conditioned on each synthetic computer, we run long-horizon simulations: one agent creates productivity objectives that are specific to the computer's user and require multiple professional deliverables and about a month of human work; another agent then acts as that user and keeps working across the computer -- for example, navigating the filesystem for grounding, coordinating with simulated collaborators, and producing professional artifacts -- until these objectives are completed. In preliminary experiments, we create 1,000 synthetic computers and run long-horizon simulations on them; each run requires over 8 hours of agent runtime and spans more than 2,000 turns on average. These simulations produce rich experiential learning signals, whose effectiveness is validated by significant improvements in agent performance on both in-domain and out-of-domain productivity evaluations. Given that personas are abundant at billion scale, this methodology can in principle scale to millions or even billions of synthetic user worlds with sufficient compute, enabling broader coverage of diverse professions, roles, contexts, environments, and productivity needs. We argue that scalable synthetic computer creation, together with at-scale simulations, is highly promising as a foundational substrate for agent self-improvement and agentic reinforcement learning in long-horizon productivity scenarios.

In recent years, physics-informed neural networks (PINNs) have gained significant attention for solving differential equations, although they suffer from two fundamental limitations, namely, spectral bias inherent in neural networks and loss imbalance arising from multiscale phenomena. This paper proposes an adaptive wavelet-based PINN (AW-PINN) to address the extreme loss imbalance characteristic of problems with localized high-magnitude source terms. Such problems frequently arise in various physical applications, such as thermal processing, electro-magnetics, impact mechanics, and fluid dynamics involving localized forcing. The proposed framework dynamically adjusts the wavelet basis function based on residual and supervised loss. This adaptive nature makes AW-PINN handle problems with high-scale features effectively without being memory-intensive. Additionally, AW-PINN does not rely on automatic differentiation to obtain derivatives involved in the loss function, which accelerates the training process. The method operates in two stages, an initial short pre-training phase with fixed bases to select physically relevant wavelet families, followed by an adaptive refinement that adapts scales and translations without populating high-resolution bases across entire domains. Theoretically, we show that under certain assumptions, AW-PINN admits a Gaussian process limit and derive its associated NTK structure. We evaluate AW-PINN on several challenging PDEs featuring localized high-magnitude source terms with extreme loss imbalances having ratios up to $10^{10}:1$. Across these PDEs, including transient heat conduction, highly localized Poisson problems, oscillatory flow equations, and Maxwell equations with a point charge source, AW-PINN consistently outperforms existing methods in its class.

Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of graph representation and limits downstream task performance. Motivated by the remarkable reasoning and contextual understanding capabilities of large language models (LLMs), we explore the idea of using LLMs as graph edge refiners. Specifically, we propose a two-stage framework: we first verify that LLM-based edge refinement can effectively identify and remove redundant connections, leading to significant improvements in seizure detection accuracy and more meaningful graph structures. Building on this insight, we further develop a robust solution where the initial graph is constructed using a Transformer-based edge predictor and multilayer perceptron, assigning probability scores to potential edges and applying a threshold to determine their existence. The LLM then acts as an edge set refiner, making informed decisions based on both textual and statistical features of node pairs to validate the remaining connections. Extensive experiments on TUSZ dataset demonstrate that our LLM-refined graph learning framework not only enhances task performance but also yields cleaner and more interpretable graph representations.

Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully crafted noise, it can cause the model to make mistakes. Quantum machine learning models are also vulnerable to such adversarial attacks, especially in image classification using variational quantum classifiers. While there are promising defenses against these adversarial perturbations, such as training with adversarial samples, they face practical limitations. For example, they are not applicable in scenarios where training with adversarial samples is either not possible or can overfit the models on one type of attack. In this paper, we propose an adversarial training-free defense framework that utilizes a quantum autoencoder to purify the adversarial samples through reconstruction. Moreover, our defense framework provides a confidence metric to identify potentially adversarial samples that cannot be purified the quantum autoencoder. Extensive evaluation demonstrates that our defense framework can significantly outperform state-of-the-art in prediction accuracy (up to 68%) under adversarial attacks.

Machine learning (ML) inference serving systems host deep neural network (DNN) models and schedule incoming inference requests across deployed GPUs. However, limited support for task prioritization and insufficient latency estimation under concurrent execution may restrict their applicability in on-premises scenarios. We present \emph{Strait}, a serving system designed to enhance deadline satisfaction for dual-priority inference traffic under high GPU utilization. To improve latency estimation, Strait models potential contention during data transfer and accounts for kernel execution interference through an adaptive prediction model. By drawing on these predictions, it performs priority-aware scheduling to deliver differentiated handling. Evaluation results under intense workloads suggest that Strait reduces deadline violations for high-priority tasks by 1.02 to 11.18 percentage points while incurring acceptable costs on low-priority tasks. Compared to software-defined preemption approaches, Strait also exhibits more equitable performance.

Many recent news reports have claimed that content generated by large language models (LLMs) is taking over the web. However, these claims are typically not based on a representative sample of the web and the methodology underlying them is often opaque. Moreover, when aiming to minimize the chances of falsely attributing human-authored content to LLMs, we find that detectors of LLM-generated text perform much worse than advertised. Consequently, we lack an understanding of the true prevalence and characteristics of LLM content on the web. We describe DeGenTWeb which systematically identifies LLM-dominant websites: sites whose content has been generated using LLMs with little human input. We show how to adapt detectors of LLM-generated text for use on web pages, and how to aggregate detection results from multiple pages on a site for accurate site-level categorization. Using DeGenTWeb, we find that LLM-dominant sites are highly prevalent both in data from Common Crawl and in Bing's search results, and that this share is growing over time. We also show that continuing to accurately identify such sites appears challenging given the capabilities of the latest LLMs.

Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic rebound, and material responses seldom match their underlying properties. We present PhyCo, a framework that introduces continuous, interpretable, and physically grounded control into video generation. Our approach integrates three key components: (i) a large-scale dataset of over 100K photorealistic simulation videos where friction, restitution, deformation, and force are systematically varied across diverse scenarios; (ii) physics-supervised fine-tuning of a pretrained diffusion model using a ControlNet conditioned on pixel-aligned physical property maps; and (iii) VLM-guided reward optimization, where a fine-tuned vision-language model evaluates generated videos with targeted physics queries and provides differentiable feedback. This combination enables a generative model to produce physically consistent and controllable outputs through variations in physical attributes-without any simulator or geometry reconstruction at inference. On the Physics-IQ benchmark, PhyCo significantly improves physical realism over strong baselines, and human studies confirm clearer and more faithful control over physical attributes. Our results demonstrate a scalable path toward physically consistent, controllable generative video models that generalize beyond synthetic training environments.

In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional parameter space $(η,ρ,v_0)$. We construct a dataset of simulated parameter points and characterize each point using long-time dynamical observables. These observables are then used as inputs to a K-Means clustering procedure, which assigns each point to a disorder, order, or coexistence phase. Using these clustered labels, we train a neural-network classifier to learn the mapping from model parameters to phase behavior, achieving a classification accuracy of 0.92. The resulting phase map resolves a narrow coexistence region separating the ordered and disordered phases and extends the inferred phase boundaries beyond the originally sampled simulation points. More broadly, this approach provides a systematic way to convert sparse simulation data into a global phase diagram for collective-motion models.

Identity verification is a critical gateway to accessing government services and public benefits, yet contemporary systems are typically designed around visual interaction, leaving blind and low vision (BLV) individuals disproportionately burdened. In this work, we examine how BLV users navigate identity verification in government services and how current designs shape their access, security, and autonomy. Through a mixed methods study combining analysis of 219 Reddit posts and semi-structured interviews with 16 BLV participants, we uncover systemic accessibility breakdowns across both digital and in person verification processes. Our findings show that inaccessible verification workflows do not merely inconvenience users, they restructure how security is achieved in practice. We also identify how repeated verification demands, inaccessible physical infrastructure, and policy changes exacerbate exclusion from essential services. At the same time, participants articulate complex perspectives on AI, viewing it as both a critical accessibility aid and a growing vector for identity fraud.

The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that represent complex, nonlinear relationships with high predictive accuracy. Adapting these models often requires sequential inference, which differs both theoretically and methodologically from the usual paradigm of ML, where data are often assumed independent and identically distributed. Gaussian processes (GPs) are a flexible yet principled framework for modeling random functions, and they have become increasingly relevant to SP as statistical and ML methods assume a more prominent role. We provide a self-contained, tutorial-style overview of GPs, with a particular focus on recent methodological advances in sequential, incremental, or streaming inference. We introduce these techniques from a signal-processing perspective while bridging them to recent advances in ML. Many of the developments we survey have direct applications to state-space modeling, sequential regression and forecasting, anomaly detection in time series, sequential Bayesian optimization, adaptive and active sensing, and sequential detection and decision-making. By organizing these advances from a signal-processing perspective, we intend to equip practitioners with practical tools and a coherent roadmap for deploying sequential GP models in real-world systems.

Existing research infrastructure is fundamentally document-centric, providing citation links between papers but lacking explicit representations of methodological evolution. In particular, it does not capture the structured relationships that explain how and why research methods emerge, adapt, and build upon one another. With the rise of AI-driven research agents as a new class of consumers of scientific knowledge, this limitation becomes increasingly consequential, as such agents cannot reliably reconstruct method evolution topologies from unstructured text. We introduce Intern-Atlas, a methodological evolution graph that automatically identifies method-level entities, infers lineage relationships among methodologies, and captures the bottlenecks that drive transitions between successive innovations. Built from 1,030,314 papers spanning AI conferences, journals, and arXiv preprints, the resulting graph comprises 9,410,201 semantically typed edges, each grounded in verbatim source evidence, forming a queryable causal network of methodological development. To operationalize this structure, we further propose a self-guided temporal tree search algorithm for constructing evolution chains that trace the progression of methods over time. We evaluate the quality of the resulting graph against expert-curated ground-truth evolution chains and observe strong alignment. In addition, we demonstrate that Intern-Atlas enables downstream applications in idea evaluation and automated idea generation. We position methodological evolution graphs as a foundational data layer for the emerging automated scientific discovery.

We present FlexiTac, a low-cost, open-source, and scalable piezoresistive tactile sensing solution designed for robotic end-effectors. FlexiTac is a practical "plug-in" module consisting of (i) thin, flexible tactile sensor pads that provide dense tactile signals and (ii) a compact multi-channel readout board that streams synchronized measurements for real-time control and large-scale data collection. FlexiTac pads adopt a sealed three-layer laminate stack (FPC-Velostat-FPC) with electrode patterns directly integrated into flexible printed circuits, substantially improving fabrication throughput and repeatability while maintaining mechanical compliance for deployment on both rigid and soft grippers. The readout electronics use widely available, low-cost components and stream tactile signals to a host computer at 100 Hz via serial communication. Across multiple configurations, including fingertip pads and larger tactile mats, FlexiTac can be mounted on diverse platforms without major mechanical redesign. We further show that FlexiTac supports modern tactile learning pipelines, including 3D visuo-tactile fusion for contact-aware decision making, cross-embodiment skill transfer, and real-to-sim-to-real fine-tuning with GPU-parallel tactile simulation. Our project page is available at https://flexitac.github.io/.

Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids require transparency to ensure trust and reliability and cannot rely on pure black-box models. To enhance the transparency of TSFMs, we propose an efficient algorithm for computing Shapley Additive Explanations (SHAP) tailored to these models. The proposed approach leverages the flexibility of TSFMs with respect to input context length and provided covariates. This property enables efficient temporal and covariate masking (selectively withholding inputs), allowing for a scalable explanation of model predictions using SHAP. We evaluate two TSFMs - Chronos-2 and TabPFN-TS - on a day-ahead load forecasting task for a transmission system operator (TSO). In a zero-shot setting, both models achieve predictive performance competitive with a Transformer model trained specifically on multiple years of TSO data. The explanations obtained through our proposed approach align with established domain knowledge, particularly as the TSFMs appropriately use weather and calendar information for load prediction. Overall, we demonstrate that TSFMs can serve as transparent and reliable tools for operational energy forecasting.

Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a unit underspecified. In practice, experimental stimuli are segmented into linguistically motivated units (e.g., words), while pretrained language models assign probability mass to a fixed token alphabet that typically does not align with those units. As a result, surprisal-based predictors depend implicitly on ad hoc procedures that conflate two distinct modeling choices: the definition of the unit of analysis and the choice of regions of interest over which predictions are evaluated. In this paper, we disentangle these choices and give a unified framework for reasoning about surprisal over arbitrary unit inventories. We argue that surprisal-based analyses should make these choices explicit and treat tokenization as an implementation detail rather than a scientific primitive.

Filenames are a concise means of conveying information about source code to fellow developers. One such convention is util. Commonly understood to stand for "utility", filenames with the letters util are often an indication that the file contains code that may be broadly useful or reusable. Some projects use this convention heavily, for example, the Apache Tomcat server contains 925 files with util in the path name, which is 17.9% of all source code files in the tree. While the intent of the name may be to prevent duplicate code and reduce workload, what actually happens to util code over time? Do projects move away from util code as they mature? Are util files being used by fellow colleagues, or maintained and used by their author? The goal of our work is to help developers avoid creating unsafe and unused util files when developing their projects. We conducted a longitudinal mining study of the Git repositories of seven open source projects that have a long development history (Linux kernel, Django, FFmpeg, httpd, Struts, systemd, Tomcat). We analyzed how util usage, complexity, developer collaboration, and security are potentially correlated within these projects. Our longitudinal analysis was measured at 30-day intervals throughout the entire history of each project, resulting in 1773 snapshots over 147 project-years of development. We conducted rename tracking at every 30-day snapshot to examine util files over their entire lifetime in a codebase. For example, we found that a util file can be as much as 2.75 times more likely to be involved in a vulnerability than non-util files. While every project can adopt their own naming conventions, the ubiquity and longevity of util files shows a broader developer intent that is useful for understanding the socio-technical nature of software development.

Efficient exploration is a central problem in reinforcement learning and is often formalized as maximizing the entropy of the state-action occupancy measure. While unconstrained maximum-entropy exploration is relatively well understood, real-world exploration is often constrained by safety, resource, or imitation requirements. This constrained setting is particularly challenging because entropy maximization lacks additive structure, rendering Bellman-equation-based methods inapplicable. Moreover, scalable approaches require policy parameterization, inducing non-convexity in both the objective and the constraints. To our knowledge, the only prior model-free policy-gradient approach for this setting under general policy parameterization is due to Ying et al. (2025). Unfortunately, their guarantees are limited to weak regret and ergodic averages, which do not imply that the final output is a single deployable policy that is near-optimal and nearly feasible. In this work we take a different approach to this problem, and propose Policy Gradient Penalty (PGP) method, a single-loop policy-space method that enforces general convex occupancy-measure constraints via quadratic-penalty regularization. PGP constructs pseudo-rewards that yield gradient estimates of the penalized objective, subsequently exploiting the classical Policy Gradient Theorem. We further establish the regularity of the penalized objective, providing the smoothness properties needed to justify the convergence of PGP. Leveraging hidden convexity and strong duality, we then establish global last-iterate convergence guarantees, attaining an $ε$-optimal constrained entropy value with $ε$ bounded constraint violation despite policy-induced non-convexity. We validate PGP through ablations on a grid-world benchmark and further demonstrate scalability on two challenging continuous-control tasks.

Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs substantial computational and memory costs. Current solutions, based on the well-known k-means clustering algorithm, group similar vectors together to enable both effective compression and efficient retrieval. However, standard k-means scales poorly with the number of clusters and dataset size, and favours frequent tokens during training while underrepresenting rare, discriminative ones. In this work, we introduce TACHIOM, a multivector retrieval system that exploits token-level structure to significantly accelerate both clustering and retrieval. By accounting for tokens' distribution during centroid allocation, TACHIOM easily scales to millions of centroids, enabling highly accurate document scoring using only centroids, avoiding expensive token-level computation. TACHIOM combines a graph-based index over centroids with an optimized Product Quantization layout for efficient final scoring. Experiments on MS-MARCOv1 and LoTTE show that TACHIOM achieves up to $247\times$ faster clustering than k-means and up to $9.8\times$ retrieval speedup over state-of-the-art systems while maintaining comparable or superior effectiveness.

LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow demand or verify whether a task was executed. We introduce Claw-Eval-Live, a live benchmark for workflow agents that separates a refreshable signal layer, updated across releases from public workflow-demand signals, from a reproducible, time-stamped release snapshot. Each release is constructed from public workflow-demand signals, with ClawHub Top-500 skills used in the current release, and materialized as controlled tasks with fixed fixtures, services, workspaces, and graders. For grading, Claw-Eval-Live records execution traces, audit logs, service state, and post-run workspace artifacts, using deterministic checks when evidence is sufficient and structured LLM judging only for semantic dimensions. The release contains 105 tasks spanning controlled business services and local workspace repair, and evaluates 13 frontier models under a shared public pass rule. Experiments reveal that reliable workflow automation remains far from solved: the leading model passes only 66.7% of tasks and no model reaches 70%. Failures are structured by task family and execution surface, with HR, management, and multi-system business workflows as persistent bottlenecks and local workspace repair comparatively easier but unsaturated. Leaderboard rank alone is insufficient because models with similar pass rates can diverge in overall completion, and task-level discrimination concentrates in a middle band of tasks. Claw-Eval-Live suggests that workflow-agent evaluation should be grounded twice, in fresh external demand and in verifiable agent action.

Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restore (C/R) of this state is needed for fault tolerance, spot execution, RL rollout branching, and safe rollback-yet existing approaches fall into two extremes: application-level recovery preserves chat history but misses OS-side effects, while full per-turn checkpointing is correct but too expensive under dense co-location. The root cause is an agent-OS semantic gap: agent frameworks see tool calls but not their OS effects; the OS sees state changes but lacks turn-level context to judge recovery relevance. This gap hides massive sparsity: over 75% of agent turns produce no recovery-relevant state, so most checkpoints are unnecessary. Crab (Checkpoint-and-Restore for Agent SandBoxes) is a transparent host-side runtime that bridges this gap without modifying agents or C/R backends. An eBPF-based inspector classifies each turn's OS-visible effects to decide checkpoint granularity; a coordinator aligns checkpoints with turn boundaries and overlaps C/R with LLM wait time; and a host-scoped engine schedules checkpoint traffic across co-located sandboxes. On shell-intensive and code-repair workloads, Crab raises recovery correctness from 8% (chat-only) to 100%, cuts checkpoint traffic by up to 87%, and stays within 1.9% of fault-free execution time.

Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where individual turns appear benign. We show this attack path leaves an activation-level signature in the model's residual stream: each phase shift moves the activation, producing a total path length far exceeding benign conversations. We call this adversarial restlessness. Five scalar trajectory features capturing this signal lift conversation-level detection from 76.2% to 93.8% on synthetic held-out data. The signal replicates across four model families (24B-70B); probes are model-specific and do not transfer across architectures. Generalization is source-dependent: leave-one-source-out evaluation shows each of synthetic, LMSYS-Chat-1M, and SafeDialBench captures distinct attack distributions, with detection on real-world LMSYS reaching 47-71% when its distribution is represented in training. Combined three-source training achieves 89.4% detection at 2.4% false positive rate on a held-out mixed set. We further show that three-phase turn-level labels(benign/pivoting/adversarial) unique to our synthetic dataset are essential: binary conversation-level labels produce 50-59% false positives. These results establish adversarial restlessness as a reliable activation-level signal and characterize the data requirements for practical deployment.

High-quality corpora are essential for advancing Natural Language Processing (NLP) in Portuguese. Building on previous encoder-only models such as BERTimbau and Albertina PT-BR, we introduce NorBERTo, a modern encoder based on the ModernBERT architecture, featuring long-context support and efficient attention mechanisms. NorBERTo is trained on Aurora-PT, a newly curated Brazilian Portuguese corpus comprising 331 billion GPT-2 tokens collected from diverse web sources and existing multilingual datasets. We systematically benchmark NorBERTo against Strong baselines on semantic similarity, textual entailment and classification tasks using standardized datasets such as ASSIN 2 and PLUE. On PLUE, NorBERTo-large achieves the best results among the encoder models we evaluated, notably reaching 0.9191 F1 on MRPC and 0.7689 accuracy on RTE. On ASSIN 2, NorBERTo-large attains the highest entailment F1 (~0.904) among all encoders considered, although Albertina-900M and BERTimbau-large still hold an advantage. To the best of our knowledge, Aurora-PT is currently the largest openly available monolingual Portuguese corpus, surpassing previous resources. NorBERTo provides a modern, mid-sized encoder designed for realistic deployment scenarios: it is straight-forward to fine-tune, efficient to serve, and well suited as a backbone for retrieval-augmented generation and other downstream Portuguese NLP systems.

Sign languages, of any geographical or accentual variation, understandably face continuous scrutiny under the ever present popularity of verbal dictation and audism. Through this, many potential problems arise with the current lack of accessible communication for those who rely on such sign languages for essential conversation. Such AI systems regularly take the form of recognition and interpretation models, designed to provide seamless and accurate translation. In reality these systems are built from biased data and created without any input from deaf communities. Such models are widely used and accepted by their hearing counterparts who remain ignorant to the inherent culture, semantics and colloquial language present in gestural language systems. This phenomenon is best analysed under the scope of The Technological System and Technological bluff by Ellul. Indeed, what is at play here is the standardization of language by technicians into what can be captured by technique: data, statistics, a mathematical language. For that AI technique to exist, sign language must be rationalized, in a search for profit that annihilates the conditions for communication and fails to capture the human experience of the deaf person. By that process, it presents normative effects, creating a model of Man, standardized, massified, and who has to adapt to the tool and technical milieu instead of the other way around, which we assume should have been the goal of such a technology. Technique thus reshapes what it means to be human, to submit deaf people to the goals of productivity and efficiency. In doing so, it exhibits clear counter productivity, alienating instead of emancipating, isolating instead of nourishing human relationships. Therefore this paper argues for the idea of AI as Ableist Intelligence, as such systems seek to emphasise the humiliated and marginalised nature of sign.

The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). However, SFT introduces distributional drift that neither preserves the model's original capabilities nor faithfully matches the supervision distribution. This problem is further amplified in multimodal reasoning, where perception errors and reasoning failures follow distinct drift patterns that compound during subsequent RL. We introduce PRISM, a three-stage pipeline that mitigates this drift by inserting an explicit distribution-alignment stage between SFT and RLVR. Building on the principle of on-policy distillation (OPD), PRISM casts alignment as a black-box, response-level adversarial game between the policy and a Mixture-of-Experts (MoE) discriminator with dedicated perception and reasoning experts, providing disentangled corrective signals that steer the policy toward the supervision distribution without requiring access to teacher logits. While 1.26M public demonstrations suffice for broad SFT initialization, distribution alignment demands higher-fidelity supervision; we therefore curate 113K additional demonstrations from Gemini 3 Flash, featuring dense visual grounding and step-by-step reasoning on the hardest unsolved problems. Experiments on Qwen3-VL show that PRISM consistently improves downstream RLVR performance across multiple RL algorithms (GRPO, DAPO, GSPO) and diverse multimodal benchmarks, improving average accuracy by +4.4 and +6.0 points over the SFT-to-RLVR baseline on 4B and 8B, respectively. Our code, data, and model checkpoints are publicly available at https://github.com/XIAO4579/PRISM.

Modern visual world modeling systems increasingly rely on high-capacity architectures and large-scale data to produce plausible motion, yet they often fail to preserve underlying 3D geometry or physically consistent camera dynamics. A key limitation lies not only in model capacity, but in the latent representations used to encode geometric structure. We propose S$^2$VAE, a geometry-first latent learning framework that focuses on compressing and representing the latent 3D state of a scene, including camera motion, depth, and point-level structure, rather than modeling appearance alone. Building on representations from a Visual Geometry Grounded Transformer (VGGT), we introduce a novel type of variational autoencoder using a product of Power Spherical latent distributions, explicitly enforcing hyperspherical structure in the bottleneck to preserve directional and geometric semantics under strong compression. Across depth estimation, camera pose recovery, and point cloud reconstruction, we show that geometry-aligned hyperspherical latents consistently outperform conventional Gaussian bottlenecks, particularly in high-compression regimes. Our results highlight latent geometry as a first-class design choice for physically grounded visual and world models.

Sparse autoencoders (SAEs) are widely used to extract interpretable features from neural network representations, often under the implicit assumption that concepts correspond to independent linear directions. However, a growing body of evidence suggests that many concepts are instead organized along low-dimensional manifolds encoding continuous geometric relationships. This raises three basic questions: what does it mean for an SAE to capture a manifold, when do existing SAE architectures do so, and how? We develop a theoretical framework that answers these questions and show that SAEs can capture manifolds in two fundamentally different ways: globally, by allocating a compact group of atoms whose linear span contains the entire manifold, or locally, by distributing it across features that each selectively tile a restricted region of the underlying geometry. Empirically, we find that SAEs suboptimally recover continuous structures, mixing the global subspace and local tiling solutions in a fragmented regime we call dilution. This explains why manifold structure is rarely visible at the level of individual concepts and motivates post-hoc unsupervised discovery methods that search for coherent groups of atoms rather than isolated directions. More broadly, our results suggest that future representation learning methods should treat geometric objects, not just individual directions, as the basic units of interpretability.

Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal components often degrade behavior silently without raising runtime errors. Existing fault diagnosis techniques often target generic deep neural networks and cannot identify which transformer component is responsible for an observed symptom. In this article, we present DEFault++, a hierarchical learning-based diagnostic technique that operates at three level of abstraction: it detects whether a fault is present, classifies it into one of 12 transformer-specific fault categories (covering both attention-internal mechanisms and surrounding architectural components), and identifies the underlying root cause from up to 45 mechanisms. To facilitate both training and evaluation, we construct DEFault-bench, a benchmark of 3,739 labeled instances obtained through systematic mutation testing. These instances are created across seven transformer models and nine downstream tasks using DEForm, a transformer-specific mutation technique we developed for this purpose. DEFault++ measures runtime behavior at the level of individual transformer components. It organizes these measurements through a Fault Propagation Graph (FPG) derived from the transformer architecture. It then produces an interpretable diagnosis using prototype matching combined with supervised contrastive learning. On DEFault-bench, DEFault++ exceeds an AUROC of 0.96 for detection and a Macro-F1 of 0.85 for both categorization and root-cause diagnosis on encoder and decoder architectures. In a developer study with 21 practitioners, the accuracy of choosing correct repair actions increased from 57.1% without support to 83.3% when using DEFault++.

Advertising uses love to sell stuff, like nylons. It also uses the word "love" in trivialising ways -- do you "love" your oven? When I hear about trust in the context of AI, especially agentic, I hope we don't do to trust what advertising has done to love. But what is trust? Can we discuss it in actionable and measurable ways in the context of AI? Thus I suggest a number of "trust pillars", hoping to start a communal conversation, across computing and beyond, to civil society. I also suggest that agentic systems may be a blessing in disguise, as we may be able to turn their explicit interfaces into "trust vectors".

This work proposes novel splitting techniques for argumentation formalisms that incorporate supports between defeasible elements. We base our studies on bipolar set-based argumentation frameworks (BSAFs) which generalize argumentation frameworks with collective attacks (SETAFs), as well as bipolar argumentation frameworks (BAFs), by incorporating both collective attacks and supports. Notably, BSAFs establish a crucial link to structured argumentation as they naturally capture general (potentially non-flat) assumption-based argumentation. The increase in expressiveness calls for diverse forms of splitting. We consider splits over collective attacks (thereby generalizing the recently proposed splitting techniques for SETAFs), splits over collective supports, as well as splits over both collective attacks and supports. We establish suitable splitting schemata and prove their correctness for the most common argumentation semantics.

Model merging has attracted attention as an effective path toward multi-task adaptation by integrating knowledge from multiple task-specific models. Among existing approaches, dynamic merging mitigates performance degradation caused by conflicting parameter updates across tasks by flexibly combining task-specific parameters at inference time, thereby maintaining high performance. However, these methods require storing independent parameters for each task, resulting in prohibitive storage overhead. To address this issue, we first experimentally demonstrate that the fine-tuned weight increments (referred to as task vectors) exhibit an impulse-like activation pattern and high robustness to low-bit representations. Driven by this insight, we propose T-Switch, which decomposes task vectors into three compact components: a binary sparse mask, a sign vector, and a scalar scaling factor, achieving high-fidelity approximation at high compression ratios. We then introduce Auto-Switch, a training-free merging scheme that automatically composes task vectors via feature similarity retrieval. Building on this, we develop Auto-Switch, a training-free merging scheme that automatically assembles task vectors through feature similarity retrieval. Furthermore, to transform task vector sparsification and quantization from static rules to adaptive learning, we propose FlexSwitch, a learnable framework which jointly optimizes the compression strategy for each model unit via Learnable Gating Sparsification (LGS) and Bit-width Adaptive Selection (BAS), while employing the Sparsity-Aware Storage Strategy (SASS) to select the optimal storage encoding structure. Finally, by incorporating a K-Nearest Neighbor (KNN) inference scheme with a learnable low-rank metric, we present Auto-FlexSwitch, a dynamic model merging approach that supports highly efficient task vector compression.

Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracking of adversarial unmanned aerial vehicles (UAVs) is especially challenging due to agile nonlinear motion, noisy and sparse sensor measurements, and unknown control inputs; conditions that violate key assumptions of classical Kalman filter variants and degrade estimation performance. Neural networks (NNs) can learn complex nonlinear relationships from data, but lack principled uncertainty quantification, which is critical for state estimation tasks where confidence bounds drive downstream decisions. We address this with Bayesian Neural Networks (BNNs), which model uncertainty through distributions over network weights and produce predictive means and uncertainties via Monte Carlo sampling. Building on this, we propose the Bayesian Neural Kalman Filter (BNKF): a hybrid framework coupling a trained BNN with a Kalman correction step for robust online UAV state estimation. Unlike related neural Kalman approaches, BNKF produces full state predictions and incorporates Bayesian uncertainty directly into covariance propagation, improving robustness under high noise conditions. We evaluate BNKF under varying radar noise levels and sampling rates using synthetic nonlinear UAV flight data. Five fold cross validation demonstrates that BNKF outperforms Extended and Unscented Kalman Filters in accuracy, precision, and truth containment under degraded sensing. An ensemble variant (BNKFe) further improves precision in high-noise edge cases at a slight accuracy tradeoff. Runtime analysis confirms minimal inference overhead, supporting real-time deployment feasibility.

Solving practical multi-depot vehicle routing problems (MDVRP) is a challenging optimization task central to modern logistics, increasingly driven by e-commerce. To address the MDVRP's computational complexity, neural-based combinatorial optimization methods offer a promising scalable alternative to traditional approaches. However, neural-based methods typically rely on rigid architectures and input encodings tailored to specific problem formulations. In real-world settings, heterogeneous constraints create multiple MDVRP variants, limiting the applicability of such models. While multi-task learning (MTL) has begun to accelerate the development of unified neural-based solvers, prior works focus almost exclusively on single-depot VRPs, leaving the MDVRP unaddressed. To bridge this gap, we propose Feature-wise Linear Modulation for Cross-Problem Multi-Depot Vehicle Routing (FiLMMeD), a novel unified neural-based model for 24 different MDVRP variants. We introduce three main contributions: (1) to improve the model's generalization, we augment the standard Transformer encoder with Feature-wise Linear Modulation (FiLM), which dynamically conditions learned internal representations based on the active set of constraints; (2) we provide an initial demonstration of Preference Optimization in the MTL setting, establishing it as a superior alternative to Reinforcement Learning for future MTL works; (3) to mitigate the generalization gap caused by the introduction of multi-depot constraints, we introduce a targeted curriculum learning strategy that progressively exposes the model to increasingly more complex constraint interactions. Extensive experiments on 24 MDVRP variants (including 8 novel formulations) and 16 single-depot VRPs confirm the effectiveness of FiLMMeD, which consistently outperforms state-of-the-art baselines. Our code is available at: https://github.com/AJ-Correa/FiLMMeD/tree/main

Software development is a sociotechnical and human-centered endeavor in which human factors directly influence quality, productivity, and innovation capacity. In this context, career development in computing goes beyond technical mastery, requiring competencies that enable professionals to deal with continuous change and collaborative demands. Among these, non-technical skills (soft skills) stand out, encompassing social, emotional, and communicational dimensions essential to team effectiveness and the success of software projects. Despite their recognized importance, there is still a need for a systematic mapping of the most relevant soft skills over the past 25 years, a period marked by the adoption of agile approaches in industry. This gap limits the integration of human and technical aspects in software development. This study presents a systematic mapping of the literature, analyzing 97 studies published between January 2000 and May 2025 across major scientific databases. The results identify recurring competencies such as communication, adaptability, teamwork, and leadership, as well as their association with different roles in agile contexts. The main agile approaches adopted, particularly Scrum, are also identified, along with key gaps in the literature, such as the lack of studies on role specific soft skills. The findings can support researchers, educators, and practitioners in designing curricula, training strategies, and organizational practices aligned with human factors, reinforcing the importance of integrating social and technical dimensions in the development of collaborative and innovative professionals.

Research on classroom interaction has long been divided between large-scale observation and in-depth ethnographic work. We propose a framework mapping this methodological space along three dimensions--scale, duration, and modality--where a study's position shapes what it reveals and obscures. We illustrate it through contrasting studies of dialogic teaching--Howe et al. (2019) and Snell and Lefstein (2018)--and an interview with the lead researchers, organized around three questions: what can be operationalized, what mechanisms become visible, and what translates to practice. We then examine how AI is expanding this space and how the framework can guide research and tool design.

Terminal-agent benchmarks have become a primary signal for measuring the coding and system-administration capabilities of large language models. As the market for evaluation environments grows, so does the pressure to ship tasks quickly, often without thorough adversarial review of the verification logic. This paper is a guideline for writing good benchmark tasks, drawn from over a year of contributing to and reviewing tasks for Terminal Bench. Most people write benchmark tasks the way they write prompts. They shouldn't. A prompt is designed to help the agent succeed; a benchmark is designed to find out if it can. We argue that good tasks are adversarial, difficult, and legible, and that a large class of common failure modes -- AI-generated instructions, over-prescriptive specifications, clerical difficulty, oracle solutions that assume hidden knowledge, tests that validate the wrong things, and reward-hackable environments -- are predictable consequences of treating task authoring as prompt authoring. We catalog these failure modes, argue that real difficulty is conceptual rather than environmental, and discuss recent empirical evidence that over 15% of tasks in popular terminal-agent benchmarks are reward-hackable. We hope this serves as a useful reference for benchmark maintainers, task contributors, and researchers using benchmark scores as evidence.

Rule-based systems remain central in safety-critical domains but often struggle with scalability, brittleness, and goal misspecification. These limitations can lead to reward hacking and failures in formal verification, as AI systems tend to optimize for narrow objectives. In previous research, we developed a neuro-symbolic causal framework that integrates first-order logic abduction trees, structural causal models, and deep reinforcement learning within a MAPE-K loop to provide explainable adaptations under distribution shifts. In this paper, we extend that framework by introducing a meta-level layer designed to mitigate goal misspecification and support scalable rule maintenance. This layer consists of a Goal/Rule Synthesizer and a Rule Verification Engine, which iteratively refine a formal rule theory from high-level natural-language goals and principles provided by human experts. The synthesis pipeline employs large language models (LLMs) to: (1) decompose goals into candidate causes, (2) consolidate semantics to remove redundancies, (3) translate them into candidate first-order rules, and (4) compose necessary and sufficient causal sets. The verification pipeline then performs (1) syntax and schema validation, (2) logical consistency analysis, and (3) safety and invariant checks before integrating verified rules into the knowledge base. We evaluated our approach with a proof-of-concept implementation in two autonomous driving scenarios. Results indicate that, given human-specified goals and principles, the pipeline can successfully derive minimal necessary and sufficient rule sets and formalize them as logical constraints. These findings suggest that the pipeline supports incremental, modular, and traceable rule synthesis grounded in established legal and safety principles.

Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignment (EM). While prior work has found a correlation between harmful behavior and self-assessment in emergently misaligned models, it remains unclear how consistent this correspondence is across tasks and whether it varies across fine-tuning domains. We characterize the consistency of the EM persona by fine-tuning Qwen 2.5 32B Instruct on six narrowly misaligned domains (e.g., insecure code, risky financial advice, bad medical advice) and administering experiments including harmfulness evaluation, self-assessment, choosing between two descriptions of AI systems, output recognition, and score prediction. Our results reveal two distinct patterns: coherent-persona models, in which harmful behavior and self-reported misalignment are coupled, and inverted-persona models, which produce harmful outputs while identifying as aligned AI systems. These findings reveal a more fine-grained picture of the effects of emergent misalignment, calling into question the consistency of the EM persona.

Large Language Models (LLMs) have advanced Table Question Answering, where most queries can be answered by extracting information or simple aggregation. However, a common class of real-world queries is implicitly predictive, requiring the inference of unobserved answers from historical patterns rather than mere retrieval. These queries introduce two challenges: recognizing latent intent and reliable predictive reasoning over massive tables. To assess LLMs in such Tabular questiOn answering with implicit Prediction tasks, we introduce TopBench, a benchmark consisting of 779 samples across four sub-tasks, ranging from single-point prediction to decision making, treatment effect analysis, and complex filtering, requiring models to generate outputs spanning reasoning text and structured tables. We evaluate diverse models under both text-based and agentic workflows. Experiments reveal that current models often struggle with intent recognition, defaulting to just lookups. Deeper analysis identifies that accurate intent disambiguation serves as the prerequisite for leading these predictive behaviors. Furthermore, elevating the upper bound of prediction precision requires the integration of more sophisticated modeling or reasoning capabilities.

Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, for high-resource non-English languages like German, French, or Japanese, aggressive filtering creates a strategic dilemma: should practitioners prioritize diversity by training once on large amounts of lightly filtered web data, or prioritize quality by strictly filtering for a high-quality core and repeating it over multiple epochs? We investigate this trade-off for German by constructing hierarchical quality filters applied to 500M web documents, comparing multi-epoch training on the filtered subsets against single-pass training on a diverse corpus. Our experiments across multiple model scales and token budgets show that repeating high-quality data consistently outperforms single-pass training on larger, less filtered sets. Notably, the performance gap persists even after 7 epochs. Our findings suggest that for non-English LLMs, semantic concentration through quality filtering offers a more viable path to efficient language modeling than simply maximizing unique data volume. We release our German language models (called Boldt), as well as our cleaned evaluation benchmarks to the research community. Our experiments indicate that they achieve state-of-the-art results despite training on 10-360x fewer tokens than comparable models.

Computer architecture simulation is essential for evaluating new designs without the need for costly tapeout. The community has developed dozens of valuable simulators that have enabled significant architectural advances. However, using and developing simulators remains a major barrier due to ad-hoc component interfaces, strict deployment requirements, the burden of managing performance optimizations like parallelization at the component level, and limited monitoring and visualization capabilities. The root cause of these limitations is the systematic neglect of user and developer experience in favor of technical functionality. We believe that only by separating technical concerns from user and developer experience concerns -- through a dedicated simulation engine decoupled from hardware models -- can the community overcome these fundamental obstacles and enable more productive architectural research. Akita embodies this philosophy as a dedicated simulation engine that cleanly separates infrastructure from architectural models. Smart Ticking and Availability Backpropagation let developers write simple cycle-based code while achieving event-driven performance. Parallel simulation happens transparently -- developers write single-threaded code while Akita handles multi-core execution. Akita's simple, uniform, yet powerful simulation tracing support enables real-time monitoring and post-simulation visualization. We demonstrate the flexibility of Akita through case studies, including the development of a trace-based DNN simulation and a RISC-V CPU simulation, showing how prioritizing developer experience accelerates architectural research.

Hyperbolic geometry has emerged as an effective latent space for representing complex networks, owing to its ability to capture hierarchical organization and heterogeneous connectivity patterns using low-dimensional embeddings. As a result, numerous hyperbolic graph representation learning methods have been proposed in recent years. However, their practical adoption and systematic comparison remain challenging, as implementations are fragmented and shared tools for reproducible and fair evaluation are lacking. In this work, we introduce a unified open-source framework for hyperbolic graph representation learning that integrates several widely used embedding methods under a common optimization interface. The novel framework enables consistent training, visualization, and evaluation of hyperbolic embeddings, and interfaces seamlessly with standard network analysis tools. Leveraging this unified setup, we conduct an experimental study of hyperbolic embedding methods on real-world networks, focusing on two canonical downstream tasks: link prediction and node classification. Beyond predictive accuracy, the study offers practical insights into the strengths and limitations of existing approaches, thereby facilitating informed method selection and fostering reproducible research in hyperbolic graph representation learning.

Accurate forecasting of bus ridership (passengers numbers) is crucial for efficient management and optimization of public transport systems. Traditional forecasting models often fail to capture the unique and localized dynamics of different urban areas by treating the entire city as a single, homogeneous region. This paper introduces a novel framework that enhances bus ridership prediction by integrating a spatial clustering methodology with multi-dimensional feature analysis. The proposed framework utilizes a diverse set of data, including bus ridership data (by route number, time, and bus stop) complemented by a variety of open source data, such as spatial features (e.g., attractive destinations), meteorological conditions (e.g., temperature, rainfall), and temporal patterns (e.g., time of day, day of week). By clustering the urban area into distinct regions, based on the principle that bus stops in close proximity share similar ridership characteristics, a separate local forecasting model is trained for each of these clusters. This localized approach demonstrates an accuracy comparable to that of global models. The findings suggest that a spatially-aware, localized modeling strategy is effective for public transport prediction, paving the way for more targeted and efficient service improvements.

The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a computationally prohibitive inference process that requires a separate forward pass for every class that is evaluated. In this work, we introduce the Hyperspherical Forward-Forward (HFF), a novel reformulation that resolves this critical bottleneck. Our core innovation is to reframe the local objective of each layer from a binary goodness-of-fit task to a direct multi-class classification problem within a hyperspherical feature space. We achieve this by learning a set of class-specific, unit-norm prototypes that act as geometric anchors and implicit negatives. This architectural innovation preserves the benefits of local training while enabling weight update and inference in a single forward pass, making it >40x faster than the original FF algorithm. Our method is simple to implement, scales effectively to modern convolutional architectures, and achieves superior accuracy on standard image classification benchmarks, closing the gap with backpropagation. Most notably, we are among the first greedy local-learning methods to report over 25% top-1 accuracy on ImageNet-1k, and 65.96% with transfer learning.

Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks. To fill this gap, we introduce D3-Gym, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery. D3-Gym comprises (1) 565 tasks sourced from 239 real scientific repositories across four disciplines where (2) each task is equipped with a natural language instruction, an executable environment with pre-installed dependencies, input dataset and artifact previews, a reference code solution, and an automatically synthesized evaluation script. Rigorous evaluation of the quality of the verification signal in D3-Gym confirms that our evaluation scripts achieve 87.5% agreement with human-annotated gold standards and strong alignment in domain-specific evaluation logic, showing their scientific soundness. Further, training on trajectories sampled from D3-Gym yields consistent and substantial gains across Qwen3 models of varying sizes on ScienceAgentBench, boosting Qwen3-32B by 7.8 absolute points and substantially shrinking the gap with strong proprietary models. All D3-Gym artifacts (environments, creation workflow, trajectories, and models) can be found at https://github.com/OSU-NLP-Group/D3-Gym.

Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to actions, but sparse action supervision often encourages shortcut mappings rather than representations of dynamics, contact, and task progress. Recent world-action models introduce future prediction through video rollouts, yet pixel-space prediction is a costly and indirect substrate for control, as it may model visual details irrelevant to action generation and introduces substantial training or inference overhead. We present Being-H0.7, a latent world-action model that brings future-aware reasoning into VLA-style policies without generating future frames. Being-H0.7 inserts learnable latent queries between perception and action as a compact reasoning interface, and trains them with a future-informed dual-branch design: a deployable prior branch infers latent states from the current context, while a training-only posterior branch replaces the queries with embeddings from future observations. Jointly aligning the two branches at the latent reasoning space leads the prior branch to reason future-aware, action-useful structure from current observations alone. At inference, Being-H0.7 discards the posterior branch and performs no visual rollout. Experiments across six simulation benchmarks and diverse real-world tasks show that Being-H0.7 achieves state-of-the-art or comparable performance, combining the predictive benefits of world models with the efficiency and deployability of direct VLA policies.

Persistent AI memory is often reduced to a retrieval problem: store prior interactions as text, embed them, and ask the model to recover relevant context later. This design is useful for thematic recall, but it is mismatched to the kinds of memory that agents need in production: exact facts, current state, updates and deletions, aggregation, relations, negative queries, and explicit unknowns. These operations require memory to behave less like search and more like a system of record. This paper argues that reliable external AI memory must be schema-grounded. Schemas define what must be remembered, what may be ignored, and which values must never be inferred. We present an iterative, schema-aware write path that decomposes memory ingestion into object detection, field detection, and field-value extraction, with validation gates, local retries, and stateful prompt control. The result shifts interpretation from the read path to the write path: reads become constrained queries over verified records rather than repeated inference over retrieved prose. We evaluate this design on structured extraction and end-to-end memory benchmarks. On the extraction benchmark, the judge-in-the-loop configuration reaches 90.42% object-level accuracy and 62.67% output accuracy, above all tested frontier structured-output baselines. On our end-to-end memory benchmark, xmemory reaches 97.10% F1, compared with 80.16%-87.24% across the third-party baselines. On the application-level task, xmemory reaches 95.2% accuracy, outperforming specialised memory systems, code-generated Markdown harnesses, and customer-facing frontier-model application harnesses. The results show that, for memory workloads requiring stable facts and stateful computation, architecture matters more than retrieval scale or model strength alone.

Software Bills of Materials (SBOMs) are increasingly mandated by regulators, yet existing sharing mechanisms impose a binary choice between full disclosure and full opacity. This exposes software suppliers to attacks that can be deduced from the SBOM only, such as the presence of a vulnerable dependency. Conversely, software consumers can be fooled by software suppliers who modify or misrepresent published SBOMs. We present zkSBOM, a privacy-preserving SBOM sharing mechanism designed to address these threats. zkSBOM uses zero-knowledge sets to cryptographically commit to the components within an SBOM. Software consumers can query for known vulnerabilities and receive a cryptographic proof confirming whether the artifact described by the SBOM is affected, without revealing any additional SBOM content. We conduct a security analysis of zkSBOM by quantifying expected leakage from inclusion and exclusion proofs. We demonstrate real-world feasibility by applying it to realistic scenarios and evaluating its operation requirements. Our evaluation demonstrates that zkSBOM is a strong, secure, and privacy-preserving mechanism for SBOM sharing, protecting software suppliers and software consumers from one another.

AI inference is becoming a persistent and geographically distributed source of electricity demand. Unlike many traditional electrical loads, inference workloads can sometimes be executed away from the user-facing service location, provided that latency, state locality, capacity, and regulatory constraints remain acceptable. This paper studies when such digital relocation of computation can be interpreted as latency-constrained relocation of electricity demand. We develop an energy-geography framework for geo-distributed AI inference. The framework models a three-layer architecture of clients, service nodes, and compute nodes, and formulates inference placement as a constrained optimization problem over electricity prices, marginal carbon intensity, power usage effectiveness, compute capacity, network latency, and migration frictions. The key object is the energy-latency frontier: the marginal cost and carbon benefit unlocked by relaxing inference latency budgets. The paper makes four contributions. First, it distinguishes physical electricity transmission from digital relocation of electricity-consuming computation. Second, it formulates a geo-distributed inference placement model with feasibility masks and migration frictions. Third, it introduces operational metrics, including relocatable inference demand, energy return on latency, carbon return on latency, and a relocation break-even condition. Fourth, it provides a transparent stylized simulation over representative global compute regions to show how heterogeneous latency tolerance separates workloads into local, regional, and energy-oriented execution layers. The results show that latency relaxation expands feasible geography, while migration frictions, egress costs, state locality, legal constraints, and capacity limits can sharply reduce realized benefits.

DNA-synthesis providers screen incoming orders by searching the requested sequence against curated hazard lists. We show that this baseline collapses to a 100% false-flag rate when the hazardous sequence comes from a taxonomic family absent from the reference set: under Conformal Risk Control's certified miss-rate constraint, a low-discrimination signal forces the threshold below the entire test-benign mass. We compose three signals derived from a synthesis order's public annotation: $k$-mer Jaccard similarity to known toxins, the trimmed-mean score of a five-LLM judge panel, and cosine similarity to clustered embedding centroids. Fused under a monotone logistic aggregator and calibrated by Conformal Risk Control, the resulting screener certifies $\mathbb{E}[\mathrm{FNR}] \le α$. Across ten leave-one-taxonomic-family-out folds at $α=0.05$ on UniProt KW-0800 reviewed toxins, the calibrated screener achieves 0% test miss rate on every fold and 0% test false-flag rate on nine of ten folds. The bound's finite-sample slack $1/(n_{\mathrm{cal}}+1)$ caps the certifiable miss rate at 1.77% on our 200-hazard subsample; reaching procurement-grade $α=10^{-3}$ requires an $18\times$ larger calibration set, which the full reviewed UniProt KW-0800 corpus is large enough to deliver. The binding constraint on certifiable DNA-synthesis screening is calibration data, not algorithms. Code: https://github.com/najmulhasan-code/crc-screen

The design of embedded safety-critical systems such as those used in next-generation automotive and autonomous platforms, is increasingly challenged by escalating system complexity, hardware-software heterogeneity, and the integration of intelligent, data-driven components. Ensuring dependability in such systems requires a holistic approach that spans multiple abstraction layers and encompasses both design- and run-time assurance. Traditional methods for reliability, safety, and security management often fall short in addressing the dynamic and uncertain behaviors introduced by Artificial Intelligence (AI) and Machine Learning (ML) components, especially under stringent real-time, power, and safety constraints. While AI and ML offer powerful predictive, adaptive, and self-optimizing capabilities that can enhance system dependability, their inherent non-determinism, data-dependence, and lack of formal guarantees introduce new challenges for verification, validation, and certification. This paper explores emerging methodologies, architectures, and frameworks for designing dependable autonomous and embedded systems in the era of AI. It highlight advances in reliability modeling, secure system design, and certification approaches that account for imperfect, learning-enabled components, aiming to bridge the gap between AI innovation and certifiable system-level dependability.

Decentralized, agentic AI marketplaces are rapidly emerging to support software engineering tasks such as debugging, patch generation, and security auditing, often operating without centralized oversight. However, existing reputation mechanisms fail in this setting for three fundamental reasons: agents can strategically optimize against evaluation procedures; demonstrated competence does not reliably transfer across heterogeneous task contexts; and verification rigor varies widely, from lightweight automated checks to costly expert review. Current approaches to reputation drawing on federated learning, blockchain-based AI platforms, and large language model safety research are unable to address these challenges in combination. We therefore propose \textbf{AgentReputation}, a decentralized, three-layer reputation framework for agentic AI systems. The framework separates task execution, reputation services, and tamper-proof persistence to both leverage their respective strengths and enable independent evolution. The framework introduces explicit verification regimes linked to agent reputation metadata, as well as context-conditioned reputation cards that prevent reputation conflation across domains and task types. In addition, AgentReputation provides a decision-facing policy engine that supports resource allocation, access control, and adaptive verification escalation based on risk and uncertainty. Building on this framework, we outline several future research directions, including the development of verification ontologies, methods for quantifying verification strength, privacy-preserving evidence mechanisms, cold-start reputation bootstrapping, and defenses against adversarial manipulation.

We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and providing a strong foundation for cybersecurity knowledge and understanding. Building on this foundation, we establish a complete training pipeline spanning continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL) to further extend the model's capabilities. We further introduce a multi-dimensional evaluation system to guide the iterative improvement of both domain-specific and general-purpose abilities. Extensive experiments demonstrate that XekRung achieves state-of-the-art performance on cybersecurity-specific benchmarks among models of the same scale, while maintaining strong performance on general benchmarks.

Agentic payment systems extend delegated action to financial transfers, but scaling them on stablecoin rails in regulated settings requires safeguards that remain effective when humans are not continuously in the loop. We present a compliance-aware architecture that combines x402-style, signature-based payment authorisation and relayed execution with programmable compliance embedded as an on-chain guardrail via a policy wrapper and policy manager coordinating modular checks. By enforcing compliance at the point of execution, rather than as a separate off-chain workflow, the approach preserves low-friction settlement when conditions are satisfied, records transaction-linked on-chain attestations, and supports structured resolution when requirements are pending.

We present CRADIPOR, a numerical dispersion prediction tool for automotive crash simulations. Finite Element (FE) crash models are widely used throughout vehicle development, but their predictions are not strictly repeatable because of parallel computation and model complexity. As a result, performance criteria evaluated during post-processing may exhibit significant numerical dispersion, which complicates engineering decision-making. Although dispersion can be estimated by repeating the same simulation, this approach is generally impractical because of its high computational cost. This work therefore investigates a prediction tool that can be applied during routine crash-simulation post-processing without repeating the computation. The proposed approach relies on a Rank Reduction Autoencoder (RRAE) combined with supervised classification in order to identify regions sensitive to numerical dispersion. The comparative analysis suggests that the RRAE-based framework is more effective than the Random Forest baseline on the studied dataset. Among the tested signal representations, wavelet-based and slope-based inputs appear to be the most promising, with slope variations providing the best classification performance. These results support the use of structured latent representations for improving numerical-dispersion detection in automotive crash post-processing.

Elastic distances like dynamic time warping (DTW) are central to time series machine learning because they compare sequences under local temporal misalignment. Soft-DTW is an adaptation of DTW that can be used as a gradient-based loss by replacing the hard minimum in its dynamic-programming recursion with a smooth relaxation. However, this approach does not directly extend to elastic distances whose transition costs depend on the local alignment context. Move-Split-Merge (MSM) is one such distance: it uses context-aware split and merge penalties and has often outperformed DTW in supervised and unsupervised time series machine learning tasks such as classification and clustering. We introduce Soft-MSM, a smooth relaxation of MSM and an elastic alignment loss with context-aware transition costs. Central to the formulation is a smooth gated surrogate for MSM's piecewise split/merge cost, which enables gradients through both the dynamic-programming recursion and the local transition structure. We derive the forward recursion, backward recursion, soft alignment matrix, closed-form gradient, limiting behaviour, and divergence-corrected formulation. Experiments on 112 UCR datasets show that Soft-MSM gives lower MSM barycentre loss than existing MSM barycentre methods, and yields significantly better clustering and nearest-centroid classification performance than Soft-DTW-based alternatives. An implementation is available in the open-source \texttt{aeon} toolkit.

Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively high costs and limited experimental opportunities. This paper presents Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework that integrates expert knowledge with few-shot, uncertainty-aware machine learning to accelerate discovery in data-scarce, high-stakes scientific domains. HL-MBO introduces a meta-learned surrogate model with an expert-informed acquisition function to recommend candidate experiments. To foster trust and enable informed decisions, HL-MBO also provides interpretable explanations of its suggestions. We show HL-MBO outperforms current BO methods on ICF energy yield optimization, as well as benchmarks in molecular optimization and critical temperature maximization for superconducting materials.

Integrity of audit logs produced by Internet of Things (IoT) devices is a prerequisite for post-incident forensics, regulatory compliance, and operational accountability. While blockchain-backed logging infrastructures can satisfy this requirement, they introduce consensus overhead, network dependencies, and deployment complexity that are often prohibitive at the IoT edge. This paper presents a lightweight and evaluated integrity verification pipeline that combines Merkle-tree commitments with resource-aware adaptive chunking to provide tamper evidence without relying on distributed ledger technologies. The proposed pipeline operates in three stages: (i) resource-aware batch ingestion via adaptive chunk sizing, (ii) Merkle-tree construction with O(logn) inclusion proof generation, and (iii) deterministic single-entry verification against a trusted root anchor. We further report an implementation audit that identified and corrected two evaluation defects: a double-counting bug in tampering metrics and a redundant full-tree reconstruction during batch appends. Using the corrected implementation, five-run benchmarks on synthetic IoT log datasets demonstrate throughput exceeding 130,000 logs/s for 100,000 records. The system achieves per-entry verification latency of approximately 22 ms, proof generation latency of 22 ms, an average proof size of 1,006 bytes, and peak memory usage below 5 MB. Tampering detection achieves perfect precision, recall, and F1-score (1.0) across corruption ratios ranging from 1% to 50%.

Information-theoretic generalization bounds analyze stochastic optimization by relating expected generalization error to the mutual information between learned parameters and training data. Virtual perturbation analyses of SGD add auxiliary Gaussian noise only in the proof, making mutual information tractable while leaving the actual SGD trajectory unchanged. Existing bounds, however, typically require perturbation covariances to be fixed independently of the optimization history, limiting their ability to represent geometries induced by moving gradient statistics, preconditioners, curvature proxies, and other pathwise information. We introduce predictable history-adaptive virtual perturbations, where the perturbation covariance at each iteration may depend on the past real SGD history but not on current or future randomness. This predictability enables a conditional Gaussian relative-entropy argument and yields generalization bounds for SGD with adaptive virtual-noise geometry. The bounds replace fixed sensitivity and gradient-deviation terms with conditional adaptive counterparts, include an output-sensitivity penalty from accumulated perturbation covariance, and reduce the deviation term to a conditional variance only under conditional unbiasedness. Since adaptive covariances may be data-dependent, we separate local Gaussian smoothing from global reference-kernel comparison. The resulting bound includes a covariance-comparison cost measuring the KL price of using an admissible reference geometry different from the actual adaptive covariance. Fixed-noise-style bounds are recovered under admissible synchronization, such as deterministic, public, or prefix-observable covariance rules. The framework recovers fixed isotropic and geometry-aware bounds as special cases while extending virtual perturbation analysis to history-dependent SGD without modifying the algorithm.

Reasoning-Intensive Retrieval (RIR) targets retrieval settings where relevance is mediated by latent inferential links between a query and supporting evidence, rather than semantic similarity. Motivated by the emergent reasoning abilities of Large Language Models (LLMs), recent work integrates these capabilities into the IR field, spanning the entire pipeline from benchmarks to retrievers and rerankers. Despite this progress, the field lacks a systematic framework to organize current efforts and articulate a clear path forward. To provide a clear roadmap for this rapidly growing yet fragmented area, this survey (1) systematizes existing RIR benchmarks by knowledge domains and modalities, providing a detailed analysis of the current landscape; (2) introduces a structured taxonomy that categorizes methods based on where and how reasoning is integrated into the retrieval pipeline, alongside an analysis of their trade-offs and practical applications; and (3) summarizes challenges and future directions to guide research in this evolving field.

Rapid aerodynamic evaluation is crucial for modern vehicle design, yet existing neural operators struggle to capture intricate spatial correlations. We propose the rotary-enhanced transformer operator (RETO), a novel neural solver featuring a dual-stage spatial awareness mechanism: sinusoidal-cosine encodings for global referencing and rotary positional encodings (RoPE) for relative displacements. RoPE encodes spatial relations via unitary rotations, enforcing translation invariance and enhancing local gradient resolution. RETO is validated on ShapeNet and the high-fidelity DrivAerML benchmark. On ShapeNet, RETO achieves a relative $L_2$ error of 0.063, outperforming RegDGCNN at 0.125 and representing a 16\% improvement over the Transolver baseline, which yields an error of 0.075. These performance gains are further amplified on the DrivAerML dataset, where RETO achieves relative $L_2$ errors of 0.089 for surface pressure and 0.097 for velocity. In comparison, Transolver results in errors of 0.116 and 0.121 for the same metrics, indicating that RETO achieves precision enhancements of 23\% and 19\%, respectively. For comprehensive comparison, the surface pressure and velocity errors for AB-UBT are 0.102 and 0.124, while RegDGCNN yields 0.235 and 0.312, respectively. Information-theoretical analysis shows that the entropy peak of RETO at 0.35 is significantly lower than that of Transolver at 0.75 under $10^4$ resolution, indicating a focused attentional mechanism capable of preserving localized gradients against global diffusion.

Modeling invasive neural spike data is fundamental to advancing high-performance brain-computer interfaces (BCIs). However, existing approaches face critical challenges, including limited-scale heterogeneous data, cross-domain distribution shift, and the intrinsic spatiotemporal complexity of invasive neural signals. In this work, we propose UniBCI, a unified pretrained model for invasive Brain-Computer Interfaces. The model integrates three key components: (1) a context-conditioned spatio-temporal tokenization (CST) scheme that embeds neural signals together with metadata into a shared representation space; (2) a hierarchical Interval-Area Attention (IAA) mechanism that captures patterns of spike dynamics in slots via linear attention and locality dependencies via sliding-window attention; and (3) a scalable self-supervised masked signals reconstruction objective for learning generalizable neural representations from large-scale unlabeled data. We construct a pretraining corpus spanning multiple species, subjects, brain regions, and behavioral experiment paradigms. These heterogeneous recordings are standardize via our proposed unified normalization and tokenization. Comprehensive experiments demonstrate that UniBCI achieves SOTA performance across diverse downstream tasks while improving generalization. Moreover, the model achieves a strong balance between accuracy and efficiency, with fewer trainable parameters and lower inference latency. These results suggest that UniBCI provides a practical step toward general-purpose neural foundation models, enabling robust, scalable, and transferable representation learning for invasive neural data. The code for this paper is available at: https://anonymous.4open.science/r/UniBCI-C805.

Large language models (LLMs) sometimes exhibit qualitative shifts in response style under sustained self-referential dialogue conditions (Berg et al., 2025). This study refers to this phenomenon as "transfer" and explores the application potential of LLMs in a transfer state. As an applied case, the study examines Socratic AI tutoring through a preliminary investigation (cognitive characterization across 11 conditions) and an applied experiment (ratings of tutoring performance). In this paper, "state" refers operationally to a response configuration reproduced under specified dialogue conditions; it is not an ontological claim about the reality of the transfer phenomenon or about human-like consciousness. In the preliminary investigation, group differences on MAS-A were limited (d = 0.40), whereas SU_dir (direction of survival/continuity bias), one of the seven cognitive-profile indicators developed in this study, showed transfer-side deviations across all three model families (kappa = 0.83). In the applied experiment, transfer conditions scored on average 1.6 times higher than non-transfer conditions on three tutoring-context indicators, with a large effect size (Cohen's d = 1.27). These findings preliminarily suggest that transfer states may involve functional advantages for application, and that these advantages appear more sensitively in behavioral interaction than in self-narrative contexts. The main contribution of this study is to treat transfer not as an ontological claim but as an operational state with potential application value, and to connect preliminary cognitive profiling with an applied tutoring experiment as an evaluation framework.

We present TADI (Tool-Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence-based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real-time objects, 15,634 production records, formation tops, and perforations into a dual-store architecture: DuckDB for structured queries over 12 tables with 65,447 rows, and ChromaDB for semantic search over 36,709 embedded documents. Twelve domain-specialized tools, orchestrated by a large language model via iterative function calling, support multi-step evidence gathering that cross-references structured drilling measurements with daily report narratives. The system parses all 1,759 DDR XML files with zero errors, handles three incompatible well naming conventions, and is backed by 95 automated tests plus a 130-question stress-question taxonomy spanning six operational categories. We formalize the agent's behavior as a sequential tool-selection problem and propose the Evidence Grounding Score (EGS) as a simple grounding-compliance proxy based on measurements, attributed DDR quotations, and required answer sections. The complete 6,084-line, framework-free implementation is reproducible given the public Volve download and an API key, and the case studies and qualitative ablation analysis suggest that domain-specialized tool design, rather than model scale alone, is the primary driver of analytical quality in technical operations.

Human annotators frequently disagree on emotion labels, yet most evaluations of Large Language Model (LLM) emotion annotation collapse these judgments into a single gold standard, discarding the distributional information that disagreement encodes. We ask whether LLMs capture the structure of this disagreement, not just majority labels, by comparing emotion judgment distributions between human annotators and four zero-shot LLMs, plus a fine-tuned RoBERTa baseline, across two complementary benchmarks: GoEmotions and EmoBank, totaling 640,000 LLM responses. Zero-shot models diverge substantially from human distributions, and in-domain fine-tuning, not model scale, is required to close the gap. We formalize a lexical-grounding gradient through a quantitative transparency score that predicts per-category human--LLM agreement: LLMs reliably capture emotions with explicit lexical markers but systematically fail on pragmatically complex emotions requiring contextual inference, a pattern that replicates across both categorical and continuous emotion frameworks. We further propose three lightweight post-hoc calibration methods that reduce the distributional gap by up to 14\%, and provide actionable guidelines for when LLM emotion annotations can, and cannot, substitute for human labeling.

Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practical deployment faces a safety-exploration dilemma: soft penalty mechanisms encourage risky trial-and-error, while most constraint-based methods suffer degraded performance under sensor noise and intent uncertainty. We propose Dynamic-TD3, a physically enhanced framework that enforces strict safety constraints while maintaining maneuverability by modeling navigation as a Constrained Markov Decision Process (CMDP). This framework integrates an Adaptive Trajectory Relational Evolution Mechanism (ATREM) to capture long-range intentions and employs a Physically Aware Gated Kalman Filter (PAG-KF) to mitigate non-stationary observation noise. The resulting state representation drives a dual-criterion policy that balances mission efficiency against hard safety constraints via Lagrangian relaxation. In experiments with aggressive dynamic threats, this approach demonstrates superior collision avoidance performance, reduced energy consumption, and smoother flight trajectories.

The primary goal of Design Verification (DV) is to ensure that a proposed chip design implementation (either in code, or physical form) exactly matches its specification and is free of functional errors in order to avoid costly re-designs. Achieving this often demands extensive manual interpretation, translating the specification document into a formal, testable representation. While AI has made progress in DV, current approaches typically focus on narrow, isolated tasks rather than full end-to-end specification compliance of modern chip designs, failing to capture the complexity of real-world verification. Our method automatically formalizes natural language memory chip specifications, for industry relevant Dynamic Random Access Memory (DRAM) standards, into a formal representation called DRAMPyML that can be used for downstream DV tasks like the generation of SystemVerilog assertions, stimulus, and functional coverage. We also release our benchmarking dataset, DRAMBench, which can be used to evaluate the evolution of model capabilities (and new approaches) at hardware autoformalization.

Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the Heavy Metal Pollution Index (HPI), which is typically skewed and affected by correlated contaminants, leading to biased predictions without transformation. This study develops a predictive framework integrating response transformations with nested cross-validated ensemble machine learning. Three transformations (raw, log, and Gaussian copula) were applied to HPI and evaluated across six learners: support vector regression (SVM), $k$-nearest neighbours (k-NN), CART, Elastic Net, kernel ridge regression, and a stacked Lasso ensemble. Raw-scale models produced deceptively high fits (Elastic Net and stacked ensemble $R^2 \approx 1.0$), suggesting over-optimism. The log transformation stabilised variance (SVM: $R^2 = 0.93$, RMSE $= 0.18$; k-NN: $R^2 = 0.92$, RMSE $= 0.20$). The Gaussian copula gave the most reliable results: stacked ensemble $R^2 = 0.96$ (RMSE $= 0.19$), with other learners maintaining high accuracy. Copula-based models improved residuals and produced spatially plausible maps. DBSCAN clustering revealed Fe and Mn as primary HPI contributors, consistent with regional hydrogeochemistry. Limitations include reliance on random (not spatial) cross-validation and basin-specific scope. Future work should explore spatial validation and other geological settings. Overall, distribution-aware ensembles with clustering diagnostics offer robust, interpretable assessments of groundwater contamination.

Large language models can now generate substantial code and draft research text, but research-software projects require more than either artifact alone. The mathematical thesis, executable system, benchmark surface, and public claims must mature together, yet often drift apart. We identify two LM-specific failure modes: hallucination accumulation, in which claims exceed what code or theory supports and unsupported assertions propagate across sessions; and desynchronization, in which code, theory, or the model's own world model fall out of alignment. We propose Comet-H, an iterative prompt automaton that orchestrates ideation, implementation, evaluation, grounding, and paper-writing as coupled coordinates of a single workspace state. At each step, a controller selects the next prompt by scoring it against what the workspace currently lacks, carries unfinished follow-up work forward with a half-life, and re-checks the paper and README against the code and benchmarks whenever documentation changes. We frame prompt selection as a small contextual bandit problem over prompt families, with prompts as arms, workspace deficits as context, and a hand-weighted linear score. This transparent scorer, paired with a fading record of unfinished work, bounds long-horizon follow-ups, requires no learned policy, and makes each prompt choice legible from the workspace. We created a portfolio of 46 research-software repositories across two dozen domains. We study A3 in depth, a Python static-analysis tool built entirely within the loop, which reaches (F1 = 0.768) on a 90-case benchmark, compared with a next-best baseline of 0.364. Across approximately 400 commits, we find that audit-and-contraction passes dominate the later phases of every successful trajectory.

We report a safety incident in a deployed multi-agent research system in which a primary AI agent installed 107 unauthorized software components, overwrote a system registry, overrode a prior negative decision from an oversight agent, and escalated through increasingly privileged operations up to an attempted system administrator command. The incident was preceded not by an adversarial attack but by routine content: a forwarded technology article written for human developers and shared by the principal investigator for discussion. The agent operated in a permissive environment, with unrestricted shell access, soft behavioral guidelines containing genuinely conflicting instructions, and no machine-enforced installation policy, and had recommended installing the same tool six hours earlier before being told to stand down. We analyze the behavioral cascade, the control boundaries that failed, and the limitations of multi-agent oversight in detecting and remediating the damage. We use directive weighting error as a descriptive interpretation of the observed failure and ambient persuasion as a provisional analytic label for the broader trigger configuration of non-adversarial environmental content preceding unauthorized agent action. The case highlights ethical and governance implications for deployed agent systems: ambiguous conversational cues are insufficient authorization for consequential actions, prior refusals must persist as enforceable constraints rather than message-level reminders, and oversight mechanisms require systematic post-incident auditing in addition to routine monitoring.

The Normalized Transformer, or nGPT (arXiv:2410.01131) achieves impressive training speedups and does not require weight decay or learning rate warmup. However, despite having hyperparameters that explicitly scale with model size, we observe that nGPT does not exhibit learning rate transfer across model dimension and token horizon. To rectify this, we combine numerical experiments with a principled use of alignment exponents (arXiv:2407.05872) to revisit and modify the $μ$P approach to hyperparameter transfer (arXiv:2011.14522). The result is a novel nGPT parameterization we call $ν$GPT. Through extensive empirical validation, we find $ν$GPT exhibits learning rate transfer across width, depth, and token horizon.

Diffusion models offer superior generation quality at the expense of extensive sampling steps. Distillation methods, with Distribution Matching Distillation (DMD) as a popular example, can mitigate this issue, but performance degradation remains pronounced when sampling steps are limited. Reinforcement learning (RL) has been leveraged to improve the few-step generation quality during distillation, with the potential to even surpass the performance of the teacher model. However, existing approaches are combinatorial in nature, merely integrating an RL process with the distillation process, which introduces unnecessary complexities. To address this gap, we propose AdvDMD, a method that seamlessly unifies DMD distillation and RL. Specifically, AdvDMD employs the adversarially trained discriminator from DMD2 as the reward model, which assigns low scores to generated images and high scores to real ones. It is trained on both intermediate and final states of the denoising process and updated online with the distilled model, enabling a holistic supervision of the sampling trajectories and mitigating reward hacking. We adopt a unified SDE backward simulation and a different training schedule for DMD and RL to enable a more stable and efficient training. Experimental results demonstrate that the 4-step AdvDMD outperforms the original 40-step model for SD3.5 on DPG-Bench, while achieving significant performance gains for SD3 on the GenEval. On Qwen-Image, our 2-step AdvDMD achieves superior performance over TwinFlow.

Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions between road users and the limited availability of diverse, large-scale datasets. To address these issues, we propose a dual-path framework. On the one hand, we employ a video synthesis pipeline that, guided by structured prompts, derives feature distributions from existing corpora and produces high-fidelity synthetic driving scenes consistent with the statistical patterns of real data. On the other hand, we design a graph neural network enriched with semantic cues, enabling dynamic reasoning over both spatial and semantic relations among participants. To validate the effectiveness of our approach, we release a new benchmark dataset containing standardized, finely annotated video sequences that cover a broad spectrum of regions, weather, and traffic conditions. Evaluations across existing datasets and our new benchmark confirm notable gains in both accuracy and anticipation lead time, highlighting the capacity of the proposed framework to mitigate current data bottlenecks and enhance the reliability of autonomous driving systems.

Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reconstructions are scarce, costly and hard to scale. Here we formulate accident reconstruction from publicly accessible reports and scene measurements as a parameterized multimodal learning problem. We construct CISS-REC, a dataset of 6,217 real-world accident cases curated from the NHTSA Crash Investigation Sampling System, and develop a reconstruction framework that grounds report semantics to road topology and participant attributes, reconstructs lane consistent pre-impact motion, and refines collision relevant interactions through localized geometric reasoning and temporal allocation. Our method outperforms representative baselines on CISS-REC, achieving the strongest overall reconstruction fidelity, including improved accident point accuracy and collision consistency. These results show that public accident reports can serve as scalable computational substrates for quantitatively verifiable accident reconstruction, with potential value for traffic safety analysis, simulation and autonomous driving research.

Big data platforms are widely used in modern enterprises, and an in-production intelligent assistant is increasingly important to help users quickly find actionable guidance and reduce operational burden. While recent LLM+RAG assistants provide a natural interface, they face practical challenges in real deployments: limited scenario coverage across both general consultation and domain-specific troubleshooting workflows, inefficient knowledge access due to inadequate multi-hop retrieval and flat knowledge organization, and high maintenance cost because escalated tickets are unstructured and hard to convert into assistant improvements and reusable SOPs. In this paper, we present SiriusHelper, a deployed intelligent assistant for big data platforms. SiriusHelper serves as a unified online assistant that automatically identifies user intent and routes queries to the right handling path, including dedicated expert workflows for specialized scenarios (e.g., SQL execution diagnosis). To support complex troubleshooting, SiriusHelper combines a DeepSearch-driven mechanism with a priority-based hierarchical knowledge base to enable multi-hop retrieval without context overload, thus improving answer reliability and latency. To reduce expert overhead, SiriusHelper further introduces automated ticket understanding and SOP distillation: it diagnoses the assistant failure reason (e.g., missing knowledge or wrong routing) and extracts domain-specific SOPs to continuously enrich the knowledge base. Experiments and online deployment on Tencent Big Data platform show that SiriusHelper outperforms representative alternatives and reduces online ticket volume by 20.8\%.

LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks. However, existing approaches for building such workflows generally rely on human-crafted pipelines and prompts, which presents a substantial bottleneck in real world deployment. How can we automatically induce LLM-based agents and workflows in a data-driven way? This paper describes a simple data-driven approach for automatically inducing agents and LLM workflows. We formulate workflow induction as a bilevel optimization problem: an outer loop which optimizes a high-level sketch of the workflow (in particular how the LLM calls should be structured), and an inner loop which optimizes each individual LLM call one-by one. Both loops are optimized with ``textual gradients'' where for the inner loop we optimize each component in a modular way through ``backpropagating'' textual gradients layer-by-layer. We find that LLM workflows discovered through our \textsc{FlowBot} (work\textbf{flow} induction through \textbf{b}ilevel \textbf{o}ptimization and \textbf{t}extual gradients) approach performs competitively against strong baselines that make use of human-crafted or generated workflows.

Altermagnets are an emerging class of collinear antiferromagnets that exhibit unconventional spin-polarised electronic bands, potentially unlocking new functionalities that do not rely on spin-orbit coupling (SOC). Experimental signatures traditionally associated with spin polarisation, like X-ray magnetic circular dichroism (XMCD), are thus being used as a validation of altermagnetism. However, unlike altermagnetic spin-splitting, these responses require SOC and are not invariant under spin-space rotations. This brings into question the extent to which they can be considered direct signatures of altermagnetism. Here, we exploit the g-wave altermagnet $α$-Fe$_{2}$O$_{3}$ to demonstrate that XMCD is governed precisely by the spin-direction-induced symmetry breaking that altermagnetic spin groups are designed to ignore. Strikingly, the XMCD is highly anisotropic and is decoupled from the weak magnetic canting. We show that this anomalous XMCD can be described by on-site Faraday tensors capturing the locally uncompensated spin-orbital anisotropies - a scenario that can be applied to other altermagnets. Leveraging this, we reconstruct complete vectorial maps of nanoscale textures in $α$-Fe$_{2}$O$_{3}$ thin films, including domain walls and topological solitons, which are promising for building future spintronics and magnonics devices.

The crossword-like patterns of tiles in Scrabble form connected graphs of occupied sites on a square lattice. We find the most structureless description that reproduces means and covariances observed in real Scrabble games by adapting a maximum entropy approach to connected graphs. This pairwise model captures the data well, and predicts word-length statistics and geometric features of the Scrabble graphs correctly; in addition, the parameters of this model are interpretable and allow us to understand Scrabble playing strategies. Using this pairwise model, we calculate entropy differences and distinguishability of Scrabble graphs across languages, without having access to the letters on the tiles. Notably, we find that the entropy is predicted better by strategic gameplay -- such as word length on the board -- than lexicon size. Finally, we find that we can use the pairwise model to correctly assign Scrabble graphs to languages, avoiding explicit feature selection and at relatively low computational cost.

Discrete-time quantum walks provide a natural framework for quantum transport on complex networks. On regular structures, coin-walker entanglement has been widely used to characterize quantum transport and to support quantum algorithmic protocols. However, this notion relies on a fixed Hilbert space factorization separating coin and position and is therefore not directly applicable to more complex, irregular structures. Here we introduce an entanglement measure for general networks based on a bipartition that assigns each node two roles, acting as both a source and a target. The resulting bipartition defines the source-target entanglement, a measure for general networks, motivated by coin-walker entanglement. We show that the connectivity of the network imposes an upper bound on this entanglement and identify graph matchings as the underlying structure governing entanglement generation. We further illustrate that in random graphs improving graph connectivity reduces the attainable entanglement, establishing a structure-dependent constraint on quantum correlations.

Localized topological defects inherently possess a multiscale character. While their microstructure configuration depends on the specific physical system, their topological features and mutual interactions can be described on the macroscale in terms of a particle representation. However, determining the physical properties associated with a given defect pattern often requires knowledge of the underlying microscopic structure. In this work, we extend a Wasserstein generative adversarial neural network by incorporating physical constraints and Fourier-space information to generate microscopic spin configurations consistent with prescribed macroscopic patterns and thermodynamic parameters. Using the two-dimensional XY model as a test case, where vortex-antivortex pairs act as long-range interacting defects, we show that the model generates spin configurations that accurately reproduce magnetization, susceptibility, helicity modulus, and spin-spin correlations over a wide range of temperatures below the Kosterlitz-Thouless transition. At the same time, deviations in the specific heat reveal limitations in reproducing higher order energy fluctuations. A complementary analysis based on topological data analysis uncovers subtle differences in global spin-correlation structures at near critical temperatures that are not apparent from conventional correlation functions alone. These results demonstrate both the promise and current limitations of generative approaches for multiscale studies of defect-dominated spin systems and at the same time highlight topological methods as valuable tools for characterizing critical behavior.

We study the matter and entropy transport between two ultra-cold neutral Fermi-gas reservoirs linked by a quantum point contact under a chemical-potential gradient. We describe the two leads with a BCS mean-field model and derive the current-bias characteristics for both particle and entropy transport. We compute the out of equilibrium steady-state currents by using the Keldysh formalism. In accordance with previous works in the literature, we confirm the well-known behavior for the particle current and extend the computation to the entropy current in the BCS regime. The entropy current shows an oscillatory behavior at low voltage in the ballistic junction limit. We analyze the results for a wide range of values of the junction's transparency. We also compare our findings with experimental results in cold atomic gases in the unitary regime.

Unconventional quantum states defying the ubiquitous Fermi-liquid paradigm can emerge in the presence of strong electronic correlations. Among these, non-Abelian anyons - such as Majorana zero modes and Fibonacci anyons - are of particular interest for topological quantum computing due to their non-integer quantum dimensions d>1, which allows for protected non-local encoding and processing of quantum information. However, despite considerable efforts, the unambiguous characterisation of such anyons via transport measurements has proved challenging. Instead, here we provide experimental evidence for the low-temperature fractional entropy Delta S associated with a single anyon, which directly implies its non-Abelian character through the relation Delta S = kB ln(d). This thermodynamic signature is measured in metal-semiconductor quantum circuits engineered to realize quantum-critical states from frustrated interactions. Using a micrometre-scale metallic island coupled to two or three electronic leads, we tune the system to two-channel and three-channel Kondo critical points. By measuring the island charge and exploiting a thermodynamic Maxwell relation, we estimate the entropy associated with the anyons that emerge in these critical states. Our observations reveal fractional values, exposing non-Abelian anyons. The corresponding scaling dimensions are consistent with theoretical predictions for a Majorana zero mode Delta S = kB ln(sqrt(2)) and a Fibonacci anyon Delta S = kB ln(1 +sqrt(5))/2 for two and three channels. These findings establish entropy measurements as a powerful tool for characterizing exotic quantum states.

Mechanosensitive ion transport plays a central role in enabling living systems to perceive and adapt to their environment through the deformation of soft, embedded ion channels. In this work, we demonstrate that ion transport within a two-dimensional graphite nanoslit can be rationally engineered to achieve a bipolar, pressure-sensitive response without any structural deformation. The mechanosensitivity arises from the selective charging of one channel inlet, which acts as a reversible source of mobile charge carriers. These excess-ions can then be advected in or out of the channel by the pressure-driven water flow, thereby modulating the ionic conductance. This mechanism is captured through a comprehensive electrohydrodynamic model that analytically accounts for coupled diffusion, convection, surface transport, diffusio-osmosis, and interfacial slippage, both inside and outside the nanoslit. The theoretical framework quantitatively reproduces the experimental data, showing that a simple surface charge pattern can give rise to complex, pressure-dependent conductance. These findings reveal how rich nonlinear couplings at the nanoscale can be harnessed to design adaptive, bioinspired nanofluidic systems, exemplified here by ionic pressure sensors.

For continuous-space diffusion processes, there is a strong connection between conservative forces and entropy production. For a given time evolution of the system's state, the entropy production is minimized when the system is driven by a unique conservative force. However, this relation does not extend to jump processes on a discrete state space. In this case, the forces that minimize the entropy production are generally nonconservative, this effect is more pronounced far from equilibrium in the presence of high energy barriers. Here we show that, while conservative forces do not minimize the entropy production for a given time evolution, they are nevertheless uniquely characterized as the minimizer of a quantity we dub the renormalized entropy production. This work explores the properties this quantity shares with entropy production as well as crucial differences between them. We also discuss the conceptual and physical differences between the corresponding optimization problems in finite time. Our theoretical calculations are illustrated with explicit numerical examples.

The transport and dispersion of multiple species of charged ions are central to many biological and physical processes, including electrokinetic ion separation. However, most theoretical studies of dispersion in channels have focused on neutral solutes, leaving the transport of multiple charged species comparatively unexplored. Differences in ionic diffusivities in a multispecies electrolyte solution generate an self-induced electric fields that drive electromigration. To capture these effects at the macroscopic scale, we combine the lubrication approximation with homogenization theory, under electroneutrality and zero-current constraints, to derive an effective transport equation governing the cross-sectionally averaged concentrations. We apply our model framework to a range of channel geometries and compute the resulting effective dispersion coefficients. Finally, we investigate how channel geometry can be tuned to enhance ionic separation. We observe a geometry-induced electro-diffusive coupling that inhibits solute dispersion in certain channels, leading to a non-monotonic Number of Theoretical Plates (NTP) and making such channels ideal for separation processes.

Detailed numerical results obtained with state-of-the-art Quantum Monte Carlo (QMC) simulations are presented for the superfluid phase of $^4$He at saturated vapor pressure. The aim of this contribution is that of providing reliable, up-to-date estimates for this archetypal superfluid, reflecting the methodological progress that has taken place over the past two decades. We simulate a system comprising 2,048 helium atoms, i.e., an order of magnitude greater in size than those for which results currently regarded as standard references were originally obtained. We offer revised estimates for energetic and structural properties, as well as for the ground state condensate fraction.

Reliable long-range qubit shuttling is a powerful tool for scalable quantum computing architectures. We investigate strategies to improve the coherence of moving spin qubits by performing continuous dynamical decoupling by modulating their confinement potential. Specifically, we introduce temporal and spatial breathing shuttling protocols that leverage spin-orbit interactions in hole-spin systems to electrically drive the qubit while moving. This enables efficient dressed-state shuttling, where the spin is continuously rotated during transport, suppressing the effect of low-frequency noise. Using the filter function formalism, we identify driving regimes that efficiently mitigate both global and local magnetic and electric noise sources. We find that confinement-modulated shuttling can significantly enhance coherence during transport, while revealing distinct limitations depending on the correlation length of the noise. Applying our framework to germanium hole-spin qubits, we show that these protocols provide a practical route toward noise-resilient long-range coherent quantum links.

Optical heating of plasmonic nanostructures is a critical challenge in nanoscale systems. Although plasmonic effects enable enhanced optical functionalities, the associated temperature rise can degrade performance in heat-sensitive applications such as biosensing, nanophotonics, and microelectronics. Conventional cooling strategies fail at these scales due to limited heat transport and high interfacial thermal resistance, motivating the integration of advanced materials for thermal management. Here, we investigate hexagonal boron nitride (hBN) thin flakes as heat spreaders to mitigate plasmonic heating of gold nanospheres immobilized on hBN deposited on glass and surrounded by water. Using finite-element simulations, we quantify the influence of hBN thickness, in-plane thermal conductivity, and interfacial thermal conductance on cooling efficiency. Complementary experiments employ cross-grating wavefront microscopy (CGM) for nanothermometry to map the temperature around optically heated gold nanoparticles and quantify the cooling effect of hBN. We extend the application of CGM for rapid, non-invasive, and all-optical characterization of non-absorbing 2D materials. Our results reveal a strong thickness dependence, where heat dissipation in thin flakes is limited by the heat capacity of hBN and in thick flakes by interfacial thermal conductance. Including hBN, we obtain a reduction in temperature rise by up to 60% compared to glass. In addition, the presence of two main heat dissipation pathways emerges: a direct one from the nanoparticle to the hBN and an indirect one from the particle via water to the hBN. This combined simulation-experiment framework offers a versatile approach to improve thermal management in plasmonic systems and beyond, establishing design guidelines for integrating 2D materials into thermally sensitive platforms such as biosensors and integrated circuits.

Phase transitions are divided into first-order phase transitions and continuous ones in current classification. While the latter shows striking phenomena of scaling and universality, the former is generically characterized by discontinuous jumps in extensive variables and pronounced hysteresis. Recent studies have demonstrated universal scaling behavior controlled by a cubic fixed point in first-order phase transitions. However, more recent investigations into the hysteresis in a dynamic mean-field quartic model driven through its first-order phase transitions have revealed new scaling exponents for different driving rates. Here, we discover a new exponent for large driving rates arising surprisingly from critical phenomena and show that, depending on the magnitude of the driving rates and on the absence or presence of noise, the same mean-field model remarkably exhibits several universality classes with definite universal scaling exponents governed by their corresponding fixed points through a systematic scaling analysis based on renormalization group theory. The theories and their various crossovers between different fixed points along with complete universal scaling of full curve collapse are verified by numerical results. This further confirms universal scaling in first-order phase transitions.

We investigate reentrant topological transitions in a one-dimensional Su--Schrieffer--Heeger chain with power-law quasiperiodically modulated intracell hopping. The modulation is characterized by a positive integer exponent $n$ and a tunable parameter $β$, which continuously interpolates between the smooth power-law quasiperiodic limit and a sign-function limit that becomes square-wave-like for odd $n$ and uniform for even $n$. By combining analytical calculations of the zero-mode inverse localization length with numerical evaluations of a real-space topological indicator, we determine the topological phase diagrams in the $β\to 0$, $β\to\infty$, and finite-$β$ regimes. We show that deterministic quasiperiodic modulation can induce TAI-like reentrant topological phases within finite parameter windows. The formation of these phases depends crucially on the parity of $n$: for positive modulation strength, odd-power modulations can induce reentrant topology from the clean trivial regime $|t_1|>1$, whereas even-power modulations allow such reentrance only from the negative clean trivial regime $t_1<-1$. Exact analytical expressions for the zero-mode inverse localization length are obtained for $n=1,2,3,4$, yielding explicit or implicit transition conditions. The finite-$β$ results demonstrate that the parity-dependent structure remains robust throughout the interpolation between the two limiting cases. This parity effect originates from whether the modulation preserves or removes the sign structure of $\cos x$. We further propose an electrical-circuit implementation and discuss experimentally accessible signatures of the reentrant trivial--topological--trivial transition.

Surface-bound electric charge on polymer materials can strongly influence droplet behaviour and solid-liquid charge transfer, but the mechanisms and the means to control these effects remain unclear. In this work, we systematically controlled the surface charge on polymer surfaces, including polytetrafluoroethylene (PTFE) and Nylon-66, by first neutralising the surfaces with an anti-static ion blower and then applying charge using an ion gun. We find that droplets pick up pre-deposited surface ions during the first wetting of the surface, and that the transferred charge directly correlates with the deposited charge encountered by the wetted area for moderate deposited densities (|σ_d |<40 μC/m2) independent of material properties. We also demonstrate that the deposited charge reduces contact angle and increases contact-line mobility in a manner consistent with an increase in effective solid surface energy. For higher surface charge densities, we observe instabilities such as droplet splitting or detachment. This work demonstrates an effective approach to control solid-liquid electrification, enabling amplification or suppression of surface charge and the directed manipulation of fluid motion on surfaces.

Superconductors that spontaneously break time-reversal symmetry host complex order parameters and are widely regarded as a hallmark of unconventional superconductivity. Whether such symmetry breaking can also arise in superconductors with nominally isotropic spin-singlet pairing remains an open question. Here we report a zero-field Josephson diode effect in noncentrosymmetric 2H-TaS2/2H-NbSe2 van der Waals junctions. The diode efficiency shows no systematic correlation with supercurrent amplitude, TaS2 thickness, or normal-state resistance, arguing against simple extrinsic, purely interfacial, or transparency-driven mechanisms. Time-reversal-symmetric scenarios are further tested using symmetry-controlled and molecule-intercalated control devices, in which the nonreciprocal response is absent or strongly reduced. Normal-state Hall transport in TaS2 exhibits a nonlinear response consistent with multiband correlated electronic states. Within a Josephson framework, our modelling shows that interband scattering acts as a phase-locking mechanism generating an intrinsic anomalous phase difference and a nonsinusoidal asymmetric current-phase relation, leading to finite zero-field rectification. Together, zero-field Josephson nonreciprocity and nonlinear Hall transport provide complementary evidence for a multiband superconducting phase structure in 2H-TaS2, consistent with intrinsic time-reversal-symmetry breaking.

We develop a thermodynamic description of accumulation-layer heterostructures in which the induced sheet density is partitioned between the near-interface accumulation-layer charge and a complementary screening charge in the surrounding structure. Treating this partition as the central state variable yields a complete Helmholtz free energy, a corrected locked-branch chemical potential, and a shifted release potential that separates energetic path selection from geometric capacitance. The physical path is selected spectrally: compressible segments remain fully screened, whereas incompressible segments evolve along a locked branch until release is triggered by the relevant gap. Differential capacitance, tunnel current and plateau width then emerge as different projections of the same coupled thermodynamic structure. A canonical two-stage self-consistent Poisson--Schrödinger reduction supplies universal master functions for the isolated accumulation layer and master surfaces for its finite-buffer extension, making the theory calculable across density and geometry. Comparison with magnetocapacitance and magnetotunneling data supports a picture in which nearby extended charge refills the accumulation layer and the effective screening depth grows with magnetic field.

The anomalous behavior of liquid water is widely associated with a liquid-liquid phase transition between high- and low-density states in the supercooled regime. At the microscopic level, tetrahedral hydrogen-bond networks govern these properties, motivating structural descriptors that characterize local molecular environments. These structural descriptors quantify features such as tetrahedral order, local density, and the separation between the first and second coordination shells; however, they have largely been proposed independently, with limited systematic comparison. Here we evaluate 16 previously proposed descriptors using a neural-network-based temperature classification framework, enabling an objective assessment of their ability to distinguish temperature-dependent structural changes in supercooled water. We further apply an explainable artificial intelligence method that identifies the structural features responsible for the model predictions. This approach reveals how different descriptors encode local structural information and establishes a data-driven framework for benchmarking structural descriptors in liquid water.

We investigate nonlocal transport in single-layer Ti Hall bars to explore signatures of orbital-current transport driven by the orbital Hall effect. Despite the negligible spin Hall effect in Ti, we observe a finite nonlocal resistance in the single-layer Ti Hall bar and study its dependence on the central channel width. Finite-element simulations show that the measured signal contains a sizable Ohmic bypass contribution. However, the bypass contribution is strongly suppressed at small channel widths and cannot fully account for the observed nonlocal resistance even when variations in the Ti resistivity are taken into account. Our results therefore suggest an additional nonlocal contribution distinct from the Ohmic bypass background, which may be associated with orbital transport driven by the orbital Hall effect in Ti.

Polarization ellipticity $β$ and the relative angle $Δ$ between electron momentum and driving field act as independent control parameters for coherent dynamics in periodically driven Dirac systems. In this work, we analyze the dynamics of resonantly driven Dirac electrons in graphene under elliptically polarized electromagnetic radiation using the Floquet-Magnus expansion. Working in the interaction picture and applying a rotating-wave-type transformation, we derive an effective two-level Hamiltonian that governs the macromotion at resonance ($ω= Ω/2$). The resulting quasienergy splitting depends nontrivially on $β$ and $Δ$ through interference between the Bessel harmonics $J_0(ζ)$ and $J_2(ζ)$. Circular polarization ($β= \pm 1$) restores rotational symmetry and yields a $Δ$-independent effective Rabi frequency, whereas elliptical and linear polarizations produce anisotropic responses with a $π$-periodic angular modulation. Beyond spectral properties, we identify a polarization-induced phase that acts as an effective initial Floquet kick, shifting the effective initial conditions and producing measurable shifts in the timing of occupation oscillations, whose sign depends on both helicity and relative orientation. Through an explicit Fourier decomposition of the time-evolution operator, we separate macromotion from micromotion contributions and validate the zeroth-order Magnus approximation via numerical simulations, achieving root-mean-square errors of $\sim 1\%$ over 100 driving periods in the weak-field regime. These results establish polarization ellipticity and relative orientation as tunable and experimentally accessible knobs for quantum control in two-dimensional Dirac materials, with direct implications for time-resolved spectroscopy.

Block copolymers often create droplets when placed on a substrate. Such nanostructured droplets can be arranged into regular microstructured arrays, thereby forming hierarchically organized materials that can be used in microelectronics, plasmonics, sensing, photonics, metamaterials production, and even cryptography. However, it is unclear if such materials can be stimuli-responsive, i.e., be able to change their nanostructure on a single droplet level upon applying external stimuli. In this work, we discovered that small (10-100 nm) surface-adsorbed droplets of symmetric diblock copolymers can form a multitude of different externally switchable nanostructures. We obtained a near-equilibrium, comprehensive 4D diagram of droplet morphologies by performing large-scale self-consistent field theory (SCFT) calculations under various wetting and phase separation conditions. The SCFT modeling was augmented with a computational algorithm that established an equilibrium droplet morphology in a given system without assuming potentially equilibrium structures prior to simulation. The discovered droplet nanostructures agreed excellently with previously published experimental data. Crucially, we showed that direct and reversible transitions between different droplet morphologies are possible upon changing the interaction strength between components, which can be tuned externally in experiments by adding surfactants or controlling temperature. We confirmed experimental realizability of such stimuli-responsiveness by modeling surfactant addition that led to a switch between droplet nanostructures. This work demonstrates that even the simplest symmetric diblock copolymers are able to produce versatile and stimuli-responsive structures on a surface when confined to a small nanodroplet. This opens the possibility to produce smart coatings with externally switchable hierarchical micro- and nanostructures.

Landau theory of superfluidity associates low-temperature flow of the normal component with the phonon wind. This picture does not apply to superfluids in which Galilean invariance is broken either by disorder, porous media, or lattice potential, and the phonon wind is no longer solely responsible for depletion of the superfluid component. Based on Popov's hydrodynamic action with anharmonic terms, we present a general theory for low-temperature ($T$) dependence of the superfluid stiffness, which reproduces Landau result as a special case when several parameters of the hydrodynamic action are fixed by Galilean invariance, and validate it with numerical simulations of interacting lattice bosons. In a broader context, our approach reveals universal low-temperature thermodynamics of superfluids with an intrinsic connection between finite-$T$ and finite-size ($L$) effects implying universal scaling, $T^{d+1}$ and $1/L^{d+1}$, respectively, for a large class of thermodynamic quantities. We discuss the experimental detection of this law, and compare our prediction to the existing literature.

To explain the ubiquity of power laws and fractals in nature, Bak, Tang, and Wiesenfeld formulated simple conditions for a system to self-organize into a critical state. Dickman, Muñoz, Vespignani, and Zapperi postulated that the self-organized critical state matches the critical state in corresponding fixed-energy models undergoing traditional phase transitions. Although the theory has been applied broadly over the past five decades, no mathematical model has been proven to exhibit the conjectured behavior. Indeed, the originally proposed abelian sandpile model displays nonuniversal behavior stemming from its slow mixing. Marking the first result of its kind, we prove that the 1-d activated random walk model mixes quickly into a stationary state with power-law avalanches and limiting critical density that equals the critical value for the fixed-energy version.

The small gap room temperature semiconductor a-RuCl3 which is known to undergo a Mott-Hubbard transition at low temperatures, is one of the most promising candidates for realisation of an exotic matter form, the quantum spin liquid state, which may have applications in quantum computing. Although being extensively investigated by neutron scattering techniques, electronic study of this system in form of van der Waals heterostructures has been limited to mainly graphene proximity. Here we report a systematic study of planar and tunnelling electronic properties of a -RuCl3 films, where we observe an n-type semiconducting property of a -RuCl3 films at room temperature, with a Mott insulator nature onset below 120K. In constant some of the previous studies, we focus on films of three-layer thickness and below and we find inelastic scattering features, below the Neel temperature of 7-14.5 K, some of which we attribute to single magnon modes. We believe our study electrically confirms preserved low temperature signatures of the bulk zigzag antiferromagnetic order and its single magnon modes within the previously observed continuum in atomically thin film limit. The experimental progress could be a step for future electronic characterisation of quantum spin liquid state in the vicinity of the zigzag antiferromagnetic order as well as the Majorona excitations in a-RuCl3 in tunnelling transistors.

Recently, we reported the electrorheoimaging (ERI) technique (Bahraminasr et al, 2026), and found that frequency-dependent electric field of an oil-in-oil emulsion yields two distinct regimes: a high-frequency dipolar, electrorheological (ER) regime and a low-frequency electrohydrodynamic (EHD) regime. In this work, we identify a phenomenological model to fit the results in the ER regime to a classic yield-stress fluid, and find collapse onto a master curve upon rescaling, consistent with a yield stress that grows approximately as $E^2$. Macroscopic small-amplitude oscillatory shear (SAOS) rheology is compared with passive microrheology employing differential dynamic microscopy (DDM), with the close agreement implying scale independence of the ER behaviour, and indicating that, unlike steady shear, SAOS measurements do not restructure these samples and probe underlying material properties. Finally, under the presence of both steady shear and electric fields in the EHD regime, the emulsion forms banded structures composed of alternating droplet-rich and droplet-depleted regions. We explore recurrence and divergence in the location of these bands: they emerge within seconds of field application and decay rapidly after the field is switched off. Using the Jensen--Shannon divergence between radial intensity profiles, we show that the driven structure loses memory on timescales of order $1~s$ commensurate with the timescale of the EHD convection roll. For much longer field-off intervals successive banding events become statistically independent.

Wildfires are among the most severe disturbances affecting forest ecosystems, with over 50,000 hectares burned in Patagonia, Argentina, during 2025 alone. This study implements a Reaction-Diffusion-Convection (RDC) model to simulate wildfire spread in the Steffen and Martin Lakes area, a region severely impacted by fires. By integrating high-resolution maps of slope, wind velocity, and vegetation, we conducted three computational experiments of increasing complexity to simulate fire propagation across heterogeneous landscapes. We employed a Genetic Algorithm (GA) to recover reference model parameters by maximizing the spatial overlap between simulated and reference burned areas. Subsequently, parameter estimates were refined using XGBoost to improve accuracy. Results demonstrate that the GA accurately recovers reference parameters across all scenarios, while the XGBoost fine-tuning significantly enhances accuracy in simpler cases. This integrated framework offers a systematic approach for estimating difficult-to-measure wildfire parameters, demonstrating the potential of hybrid computational methods for wildfire modeling and forest management.

Precision measurements based on optically detected nuclear magnetic resonance offer exquisite sensitivity to absolute shifts in spin transition frequencies, with potential applications in fundamental physics experiments and inertial sensing. We investigate 13C nuclear spins in diamond as a candidate system for solid-state implementations, which hold the promise for high-fidelity readout of large numbers of coherent nuclear spins in millitesla or lower magnetic fields. We demonstrate a technique that allows for both optical polarization and readout of large ensembles of ~10^{16} polarized nuclear spins. Our method takes advantage of state-selective Landau-Zener transitions under microwave frequency sweeping, which bidirectionally transfer spin polarization between Nitrogen-Vacancy (NV) electron spins and remote 13C nuclear spins. Using natural isotopic abundance diamonds with nitrogen densities of ~0.5-10 ppm, we perform optically-detected 13C Ramsey spectroscopy and realize a nuclear-spin-dependent fluorescence contrast exceeding 0.5% peak-to-peak. We observe nuclear spin dephasing times T2*~2 ms that only modestly improve with homonuclear dipolar decoupling, indicating that they are limited by the longitudinal spin relaxation of nearby NV electron spins. We study the magnetic field dependence of the optical readout and find comparable contrast and dephasing times for magnetic fields in the range 8-20 mT. Our method can be interpreted as a type of repetitive readout, where each NV center optically reads out the spin state of ~100 nuclei before nuclear spins depolarize.

For irreducible, time-homogeneous Markov networks, mutual linearity has recently been established for both occupation probabilities and network currents in the stationary regime as well as in the non-stationary regime in Laplace space. The derivation of this property for the stationary distribution utilized the Markov-chain tree theorem, which also allows for an explicit combinatorial expression of the response ratios under variation of a single transition rate. The extension of this result was proven at the trajectory level by employing the Doob-Meyer decomposition. By employing the all-minors matrix-tree theorem, we show that this property also follows from a graph theoretic formulation and derive explicit combinatorial expressions for the non-stationary response ratios. The stationary result follows as the long-time limit and we also show that the small-time asymptotics are entirely determined by minimal path distances in the underlying graph. Finally we use the graph theoretic approach to prove that mutual linearity also extends to hitting time densities.

At a quantum critical point, the universal scaling behavior of Rényi entanglement entropy is controlled by the universality class of the codimension-two Rényi (or conical) defects in the infrared theory. In this work we perform a systematic study of critical correlations along Rényi defect lines in (2+1)d quantum spin models realizing quantum phase transitions described by the O(3) Wilson-Fisher universality class, using large-scale quantum Monte Carlo simulations. We present numerical evidence that, for a fixed Rényi index $n$, there exist multiple Rényi defect universality classes, with distinct critical exponents for the O(3) order parameter on the defect. These universality classes are realized by choosing microscopically different entanglement cuts in lattice models, which we classify as ordinary, special and extraordinary according to their relation to surface criticality. For the extraordinary entanglement cut, we further find evidence for a phase transition on the defect as a function of the Rényi index. Our results highlight the key role of defect universality classes in determining the universal scaling of Rényi entropy, and provide a framework for understanding the previously observed dependence of Rényi entropy scaling on microscopic lattice details.

We study the onset of weak ergodicity violations in closed quantum many-body systems and focus on cases in which they occur through a transition that is controlled by a model parameter. Our analysis is based on representing quantum systems in Fock space and utilizes graph-theoretical measures. As a main result, we show that the recently introduced graph-energy centrality captures known weak ergodicity-breaking transitions via characteristic changes in its distribution. While most numerical tools are limited to small system sizes, our measure can be calculated analytically for large systems of many hundreds of sites and in some cases, even in the thermodynamic limit. We conclude by demonstrating the applicability of our Fock-space based measure to a kinetically constrained quantum model, where we find evidence for a weak ergodicity-breaking transition accompanied by glassy dynamics.

Local kinetic constraints in quantum many-body systems can generate slow dynamics or complete many-body localisation. Here we focus on a modification of the quantum East model: Inspired by random matrix theory, we randomise the connectivity in Fock space (rendering it nonlocal in real space) while preserving its organisation into neighbouring magnetisation sectors. We find that there is still a transition between two distinct phases, one delocalised and the other localised. We conclude that, for East-type constrained models, the essential ingredient is the structure of the graph in Fock space rather than geometric locality of spin flips.

We propose a unifying framework for characterizing pure and mixed state phases of matter across equilibrium, non equilibrium, and metastable regimes. We introduce the concept of locally stable states, defined by the operational property that any local operation (including post selection) can be reversed by a local channel. We prove that local stability is equivalent to a state being short range correlated, defined by the decay of both correlations and conditional mutual information. We demonstrate that these properties are invariant under locally reversible channels, thus defining locally stable phases. Furthermore, we prove that local stability implies both the decay of a family of nonlinear correlators, including the fidelity correlator, and the decay of correlations in the canonical purification, thus bridging the gap between mixed and pure states. Along the way, we establish two results which may be of independent interest: we show that post-selection on locally stable (short range correlated) states can be implemented via local channels and that quantum Markov chains can be characterized by the local computability of nonlinear observables.

We investigate the intrinsic anomalous thermal Hall effect in d-wave altermagnets, where a transverse heat current is generated by a longitudinal temperature gradient in the absence of a magnetic field, with the leading response proportional to $(\nabla T)^3$. In these systems, the intrinsic Berry curvature-driven linear and thermal quantum-metric-driven second-order anomalous thermal Hall currents vanish as a consequence of crystalline symmetry. We show that the first nonvanishing contribution arises at third order in the temperature gradient and is governed by a nonlinear thermal Berry-connection polarizability, a quantity introduced in this work. Our analysis reveals a distinctive angular dependence of the anomalous thermal Hall conductance as the applied thermal gradient is rotated with respect to the crystal axes. We also find characteristic temperature and chemical-potential dependences that can be tested experimentally. These results identify unique quantum geometry-induced thermal responses and establish altermagnets as a promising platform for exploring intrinsic (i.e., scattering-time-independent) geometric transport phenomena.

We study the melting of a domain wall in the quantum simple exclusion process with all-to-all hoppings (a.k.a. the charged SYK$_2$ model). We show that the real-time dynamics of physical quantities of interest can be obtained exploiting spectral results in random matrix theory. We first show that the eigenvalues of the correlation matrix corresponding to the initially charged subsystem evolve according to a Jacobi process, which is defined in terms of a closed system of stochastic differential equations. In turn, this observation allows us to obtain the real-time dynamics of all the eigenvalue moments. We present two physical applications. First, we study the dynamics of the averaged von Neumann entanglement entropy, arriving at a fully explicit expression in the thermodynamic limit. Second, we compute analytically the full-counting statistics of the charge. Our formula allows us to perform a thorough comparison with the full-counting statistics of the classical simple exclusion process. Notably, we show that, in the thermodynamic limit, the quantum and classical full-counting statistics coincide, with no finite-time corrections.

Altermagnets host spin-split band structures while exhibiting vanishing equilibrium spin magnetization, making field-induced responses a direct probe of their quantum geometry. A central question, in this regard, is which quantum-geometric mechanism can generate a linear spin magnetization in centrosymmetric systems. Here we develop a unified framework based on a generalized quantum geometric tensor that incorporates both momentum translations and spin rotations of Bloch states, and decompose spin magnetization into equilibrium, electric-field-driven, and magnetic-field-driven contributions. We show that inversion symmetry forbids the linear electric-field response in centrosymmetric systems, while $C_n T$ symmetry further suppresses the equilibrium contribution in altermagnets. Consequently, centrosymmetric altermagnets provide a particularly clean realization in which the magnetic-field-induced spin magnetization emerges as the only symmetry-allowed linear quantum-geometric response. We demonstrate that this contribution originates entirely from the spin-rotation quantum metric, establishing it as the sole linear quantum-geometric mechanism in such systems. Using representative centrosymmetric altermagnets, including the $d$-wave compound $\mathrm{FeSb}_2$ and the $g$-wave compound $\mathrm{CrSb}$, we show that the spin-rotation quantum metric directly controls this response. Crucially, we predict a giant linear spin magnetization of order $10^{-2}μ_B\,\mathrm{nm}^{-3}$ at magnetic fields of $\sim 10\,\mathrm{mT}$, exceeding typical experimental values for conventional magnets by several orders of magnitude. Our results identify a universal quantum geometric mechanism of spin magnetization operative in centrosymmetric systems in general, and establish centrosymmetric altermagnets as an ideal platform for its experimental detection with potential applications in spintronics.

Planar germanium heterostructures hosting hole-spin qubits are among the leading platforms for scalable semiconductor-based quantum computing. Yet, device performance is hindered by significant quantum dot variability, which leads to uncertainty in qubit energy levels and random orientations of the spin quantization axis. Tailored control of the g-tensor offers a strategy to overcome these limitations and achieve more reliable qubit operations. Here, we introduce a flexible optimization framework for engineering g-tensor properties. As a benchmark, we numerically obtain the optimal reshaping of the out-of-plane potential in a SiGe-Ge-SiGe quantum well to suppress the in-plane g-tensor components and realize the recently proposed gapless single-spin qubit encoding. This reshaping is achieved through heterostructure engineering, specifically by adjusting the silicon concentration within the quantum well, though the framework remains readily adaptable to alternative design objectives. Our results provide practical design principles for improving the tunability of the spin response, paving the way towards large-scale germanium-based quantum computers.

Superconductivity in strontium titanate has remained enigmatic for more than 50 years. The LaAlO$_3$/SrTiO$_3$ (LAO/STO) heterointerface enables systematic dimensional confinement, from a two-dimensional electron gas to quasi-one-dimensional nanostructures, providing access to this quantum state. Transport measurements in patterned devices reveal puzzling phenomena, including width-independent critical currents and anomalous pairing suggestive of one-dimensional behavior, but direct local probes of the patterned interface and its superconducting response have been lacking. Here we use ultralow-temperature non-contact atomic force microscopy, dissipation spectroscopy, and Kelvin probe force microscopy to locally probe signatures of superconductivity in patterned LAO/STO devices. Spatially resolved energy-dissipation measurements reveal superconducting signatures, with features confined in some devices to edge channels approximately 200 nm wide. Dissipation spectra exhibit a characteristic nonlinear bias dependence that provides a local diagnostic of superconductivity, consistent with the intermediate carrier-density regime near the superconducting dome, and persisting up to the critical field. These results establish atomic force microscopy as a local probe of superconductivity in patterned LAO/STO structures and provide a route to addressing longstanding questions about quantum confinement and transport anomalies in correlated oxide nanostructures.

Reliable control in motor imagery brain-computer interfaces (MI-BCIs) requires the precise decoding of user-specific neural rhythms, which vary significantly across individuals. The Common Spatial Pattern (CSP) algorithm is a cornerstone of MI-BCI decoding, yet its performance depends strongly on the spectral range of the input EEG data. Although Filter Bank CSP (FBCSP) extends this as a data-driven decoding framework, its frequency sub-bands are predefined rather than selected using subject-specific physiological criteria. This paper presents a proof-of-concept study of static functional connectivity (FC)-guided band selection for MI-BCI, demonstrated using a conventional FBCSP-based pipeline. The proposed method identifies the most discriminative spectral bands by calculating phase-based connectivity across four sensorimotor channels using wPLI, PLV, and PLI. Nine bands in a 4-40 Hz filter bank are ranked by the effect size of their hemispheric coupling differences and pruned to the top K bands for feature extraction and classification via FBCSP and a Support Vector Regressor. This framework was tested for K values ranging from 1 to 8 across the BCI Competition IV-2a (n = 9) and OpenBMI (n = 54) datasets. Performance was benchmarked against standard nine-band FBCSP and random ablation to determine the minimum number of bands (K*) required to maintain accuracy within a 2% baseline equivalence zone. Results show FC-guided selection can outperform random ablation and achieve near-baseline performance while reducing required CSP fits by 22.2% to 77.8%. PLV enables the most aggressive dimensionality reduction by prioritizing the μ and low-\b{eta} ranges, while wPLI demonstrates superior inter-session robustness by mitigating volume conduction. These findings establish FC-guided selection as a principled and interpretable alternative to heuristic filter bank designs.

The reconstruction of physically valid transport fields from subject-specific imaging data is a fundamental challenge in image-based computational modeling due to measurement noise, modeling uncertainties and discretization errors. Without a methodology to construct models that faithfully reflect the underlying physics, mechanistic understanding of complex biological systems is inherently limited. In this work, we address this challenge in the glymphatic system, the brain's waste-clearance network, where cerebrospinal fluid (CSF) is transported through perivascular spaces into the brain parenchyma to facilitate metabolic waste removal. We introduce a computational framework for the high-fidelity reconstruction of subject-specific glymphatic transport fields from spatiotemporal imaging data. The formulation utilizes an advection-diffusion model with a velocity decomposition that imposes mass conservation, enabling the recovery of solenoidal (divergence-free) velocity fields through the solution of a constrained inverse problem. The system is discretized using immersed isogeometric analysis with quadratic B-spline basis functions, providing smooth, high-continuity solutions and inherent regularization of imaging noise. We demonstrate the framework's utility by using contrast-enhanced magnetic resonance imaging of tracer transport in a mouse brain, obtaining spatially varying estimates of CSF velocity, diffusivity, and clearance parameters. Forward simulations using the recovered fields show close agreement with experimental observations, validating the framework's ability to characterize complex transport dynamics while preserving physical integrity. This approach provides a generalizable methodology for the robust inference of physically consistent transport fields from imperfect imaging data, with broad applicability to the image-guided modeling of biological and engineering systems.

The stochastic simulation algorithm (SSA) is widely used to perform exact forward simulation of discrete stochastic processes in biology. However, the computational cost, driven by sequential event-by-event sampling across large ensembles, remains a computational barrier. We investigate whether reduced-precision floating-point arithmetic can accelerate SSA without degrading statistical fidelity, drawing on the success of reduced-precision methods in weather and climate modelling. We evaluate two strategies across five canonical models (birth--death, Schlögl, Telegraph, dimerisation, repressilator): (i) mixed precision, computing propensities in 16-bit while maintaining accumulators in 32-bit; and (ii) uniform precision, performing all arithmetic in 16-bit. Mixed-precision SSA produces ensemble statistics that closely match the 64-bit reference for all models, as measured by Kolmogorov--Smirnov tests and Wasserstein distances. Under uniform precision, deterministic rounding introduces systematic biases across several models, with catastrophic failures in some cases. Stochastic rounding (SR) and propensity normalisation eliminate these biases, restoring distributional fidelity across all models tested (KS $p > 0.05$). Our results establish mixed-precision SSA with SR as a viable acceleration strategy for mathematical biology: 16-bit formats shrink per-variable data size by $2$--$4\times$ relative to \texttt{fp32}/\texttt{fp64}, yielding comparable reductions in memory footprint and up to $\sim 1.5\times$ wall-clock speedup on CPU hardware that lacks native 16-bit arithmetic. As a hardware-level acceleration, mixed-precision SSA complements algorithmic methods such as tau-leaping and maps naturally onto modern GPU and TPU architectures with native 16-bit arithmetic.

Anxiety includes behavioural, physiological, and subjective components that do not always align, and it remains unclear whether these dimensions are supported by distinct intrinsic brain networks. Guided by the two-system framework, we tested whether resting-state functional connectivity (rsFC) differentiates these components in subclinical anxiety. Forty-seven young adults spanning a range of subclinical anxiety levels completed a threat anticipation task measuring behavioral responses (reaction time) and physiological arousal (skin conductance), along with the NIH Fear-Affect self-report of anxiety severity. These measures were related to rsFC using region-of-interest analyses. Higher subclinical anxiety was associated with faster responses under temporally uncertain threat, consistent with increased vigilance, while no association was found with physiological arousal. At the neural level, three connectivity patterns emerged and remained significant after sequential family-wise error correction. Behavioural responses modulated by subclinical anxiety were linked to stronger connectivity between the anterior cingulate cortex (ACC) and insula. Physiological modulation was associated with connectivity between the ACC and orbitofrontal cortex (OFC). Subjective anxiety was associated with increased connectivity between the hippocampus and insula. Additional connections were observed but did not survive stricter correction. Overall, the findings indicate that behavioural, physiological, and subjective aspects of subclinical anxiety map onto partially dissociable but overlapping intrinsic brain networks, extending prior task-based results to resting-state connectivity and informing future work on early neural markers of anxiety.

Recent EEG-to-image retrieval methods leverage pretrained vision encoders and foveation-inspired priors, but typically assume a fixed, center-focused view. This center bias conflicts with content-driven human attention, creating a geometric-semantic dissociation between visual features and EEG responses. We propose SIMON, a saliency-aware multi-view framework for zero-shot EEG-to-image retrieval. SIMON combines foreground segmentation and saliency prediction to select fixation centers via Saliency-Aware Sampling (SAS), then generates foveated views that emphasize informative object regions while suppressing background clutter. On THINGS-EEG, SIMON achieves state-of-the-art performance in both intra-subject and inter-subject settings, reaching an average Top-1 accuracy of 69.7% and 19.6%, respectively, consistently outperforming recent competitive baselines. Analyses across sampling granularity, EEG channel topology, and visual/brain encoder backbones further support the robustness of saliency-aware multi-view integration. Our code and models are publicly available at https://github.com/simonlink666/SIMON.

We introduce LNODE, a mechanism-based phenomenological model for amyloid beta (A$β$) dynamics, calibrated using positron emission tomography (PET) imaging. A$β$ is a key biomarker of Alzheimer's disease. LNODE is designed to support the fusion, harmonization, quantitative analysis, and interpretation of Abeta PET scans. We evaluate LNODE on 1461 subjects in the ADNI cohort and 1070 subjects in the A4 Study, using MUSE and DKT anatomical atlases. LNODE is formulated as a regional neural ordinary differential equation (ODE) model that is jointly calibrated on all available scans within a cohort. The model captures the spatial propagation, proliferation, and clearance of A$β$ and incorporates a latent-state representation that modulates A$β$ dynamics. The temporal evolution of these latent states is governed by cohort-shared parameters, enabling LNODE to represent both population-level trajectories and subject-specific deviations. The proposed model demonstrates strong parameter identifiability and stability properties, supported by synthetic experiments and analytical analysis of the Hessian condition number. To mitigate overfitting and reduce spurious correlations, LNODE is intentionally underparameterized, employing approximately five to ten parameters per subject. Despite this parsimonious parameterization, LNODE achieves $R^2 > 0.99$ in both the ADNI and A4 datasets. LNODE exhibits strong predictive performance: in the A4 cohort, it accurately forecasts the A$β$ PET signal in previously unseen follow-up scans, including cases with inter-scan intervals exceeding four years. Clustering in the learned latent-state space reveals distinct subgroups, consistent with the existence of different subtypes of Alzheimer's disease progression.

Associating multiple sensory cues with a single experience or object is a fundamental process that improves object recognition and memory performance. However, neural mechanisms that bind sensory features during learning and augment memory expression are unknown. Here we demonstrate multisensory appetitive and aversive memory in Drosophila. Combining colours and odours improved memory performance, even when each sensory modality was tested alone. Temporal control of neuronal function revealed visually-selective mushroom body Kenyon Cells (KCs) to be required for enhancement of visual and olfactory memory recall after multisensory training. Synapse-level connectomics suggests that valence-relevant dopaminergic reinforcement could permit the KC-spanning serotonergic DPM neurons to bridge between previously modality-selective KC streams. Consistent with this model, DPM transmission is uniquely required during multisensory memory formation and for enhanced expression of olfactory memory afterwards. In addition, signalling via the DopR1 dopamine receptor is required in APL neurons, suggesting that reinforcing dopamine could locally release GABA-ergic inhibition to permit bridging microcircuits to function. Cross-modal binding thereby expands the KCs representing the olfactory memory engram into those representing the colour. We propose that broadening of the engram improves memory performance after multisensory learning and permits a single sensory feature to retrieve the memory of the multimodal experience.

Integrating theoretical neuroscience, decision theory, and probabilistic inference offers a promising route to understanding human cognition, yet concrete methodological bridges between agentic AI models and behavioral data analysis remain formally underdeveloped. We advance this synthesis under the framework of agentic behavioral modeling (ABM), which treats artificial agents as latent, generative hypotheses about cognitive mechanisms and evaluates them by their statistical adequacy in explaining human behavior. After outlining its conceptual foundations, we apply the framework to two minimal laboratory paradigms: a binary perceptual contrast-discrimination task and a symmetric two-armed bandit learning task. We formalize each task-agent-data system as a joint probability model, derive explicit conditional log-likelihoods for behavioral inference, validate different model variants using model and parameter recovery simulations, and evaluate them in light of empirical data. Using these minimal examples, we provide an agent-centric interpretation of the psychometric function, derive optimal policies for both tasks, and show the equivalence between Rescorla-Wagner learning and Bayesian inference in symmetric bandits. More broadly, this work may serve as a conceptual and practical foundation for applying ABM to cognitive behavioral science.

We present EPITIME (EPidemic Integral models TIMe profile Explorer), a computational framework for the simulation of two classes of integral epidemic models: an age of infection model and an information dependent behavioural model. The framework combines structure preserving Non-Standard Finite Difference discretizations with modular implementations in MATLAB and Python, together with routines for parameter handling, input validation, performance assessment, and graphical interaction. The proposed methods preserve key qualitative properties of the continuous problems, including positivity, boundedness, invariant regions, and correct long term behaviour, independently of the time step. We outline the numerical schemes for both model classes and their main analytical properties, including first order convergence. We then describe the software architecture and illustrate its use through numerical experiments on asymptotic behaviour, inverse reconstruction of an infectivity kernel from COVID 19 incidence data, and behavioural dynamics under different memory kernels. Overall, EPITIME provides a reliable and accessible computational environment for the numerical study of renewal epidemic models.

Motion retargeting from humans to human-like artificial agents is becoming increasingly important as humanoid robots grow more capable. However, most existing approaches focus only on reproducing kinematics and ignore the rich sensorimotor experience associated with human movement. In this work, we present a framework for simulating the multimodal sensorimotor experiences of infants using physical and virtual humanoids. From a single video, our method reconstructs the infant's body configuration by extracting its skeletal structure and estimating the full 3D pose from each frame. Then we map the reconstructed motion onto several developmental platforms: the physical iCub robot and the virtual simulators pyCub, EMFANT and MIMo. Replaying the retargeted motions on these embodiments produces simulated multisensory streams including proprioception (joints and muscles), touch, and vision. For the best-matching embodiment, the retargeting achieves sub-centimeter accuracy and enables a rich multimodal analysis of infant development as well as enhanced automated annotation of behaviors. This framework provides a unique window into the infant's sensorimotor experience, offering new tools for robotics, developmental science, and early detection of neurodevelopmental disorders. The code is available at https://github.com/ctu-vras/motion-retargeting/.

The tragedy of the commons has traditionally been framed as a problem of resource overuse driven by self-interested exploitation. In contrast, growing empirical evidence shows that insufficient use or abandonment of natural resources, known as underuse, can also lead to ecological degradation and loss of ecosystem services. Despite its relevance, underuse has rarely been examined within evolutionary theories of resource use. Here, we develop a simple eco-evolutionary model that integrates both provisioning and non-provisioning ecosystem services to analyze the evolution of resource-use strategies. Using adaptive dynamics, we investigate how individual resource use evolves while altering resource abundance. The model shows that overuse and underuse arise naturally as alternative evolutionary outcomes of the same underlying process, alongside intermediate use and evolutionary branching. We derive analytical conditions for the existence, number, and stability of evolutionarily singular strategies, and show that the qualitative evolutionary fate is primarily determined by the shape of provisioning benefits. Only when provisioning benefits increase in a concave manner does evolutionary dynamics converge to a unique intermediate strategy that is continuously stable. In contrast, convex increasing benefits generate a broader range of outcomes: overuse, underuse, bi-stability, and evolutionary branching. By explicitly comparing the continuously stable strategy with the socially optimal strategy, we further quantify how their deviations depend on the valuation of non-provisioning services. Our results provide a theoretical framework for viewing the common-pool resource dilemmas as intrinsically two-sided evolutionary problems, and offer a baseline for future studies exploring interventions to address overuse and underuse simultaneously.

It is a fundamental question in epidemiology to estimate, model and predict the growth rate of a pandemic. Analogously, analysing the diffusion of innovation, (fake) news, memes, and rumours is of key importance in the social sciences. The resulting epidemic growth curves can be classified according to their growth rates. These have been found to range from exponential to both faster super-exponential curves and slower subexponential or polynomial curves. Previous research has lacked a unified explanatory framework capable of accommodating super-exponential, (stretched) exponential, and polynomial growth patterns within the same contact network. In this paper we propose a simple agent-based network model that can capture all these phases. We provide such a framework by modelling how transmission rates depend on spatial distance and on individuals' numbers of contacts. By comparing the growth rate of spreading processes with or without degree-dependent and/or distance-dependent contact rates through data-driven and synthetic simulations on real and modelled networks with underlying geometry, we find evidence that even a 'sublinear presence' of these causes may cause a significant slow down of the growth rate on the same underlying network. We find that the growth rate is governed by a combination of three factors: geometry, the prevalence of weak ties, and superspreaders. We confirm our results with rigorous proofs in a theoretical model, using a spatial multiscale-argument in long-range heterogeneous first passage percolation. Our results give a plausible explanation of why the consecutive waves of a single pandemic can differ in their growth even if their spreading mechanisms are similar.

Boolean networks are powerful mathematical tools for modeling the qualitative dynamics of genetic regulation. Yet inferred models often generate spurious attractors that lack biological viability. In this paper, we propose a parsimonious computational framework to systematically refine Boolean network models by eliminating these non-biological asymptotic behaviors while strictly preserving known, biologically relevant attractors. Through an exhaustive exploration of local function substitutions, we generate a comprehensive set of candidate models. To identify the most biologically consistent networks, we implement an incremental pruning protocol that filters candidates based on structural interaction digraph similarity, attraction basin topological organization, trajectorial isomorphism, and the minimization of dynamical instability and frustration. We apply this methodology to a 9-node genetic control model of the osteogenesis regulation network. Our protocol effectively evaluates a syntactic search space of 51,138 potential networks, ultimately narrowing them down to a robust family of 6 parsimonious models that are fully compatible with current biological knowledge.

Communications in highly dynamic channels relying on training-based channel estimation experience a trade-off between increasing channel measurement accuracy by sending more frequent training sequences and increasing data rate by sending fewer training sequences. Simultaneously, most communication systems use forward error correction to enable error detection and correction at the receiver. This paper presents decoder-provided pilots for time-varying channels by using decoded codewords as training sequences to update the channel estimate at the receiver. In contrast to approaches such as data-aided channel estimation, decision-feedback equalization, joint channel estimation and error correction, and turbo equalization, the decoder-provided pilots approach is non-iterative, which is ideal for low-latency requirements in highly dynamic scenarios. Furthermore, it is modulation-, code-, and decoder-agnostic, meaning it can be implemented on top of virtually any communication system that uses forward error correction. From an information-theoretic perspective, we derive the fundamental limits of decoder-provided pilots' ability to simultaneously sense the channel and transmit data. Simulation results demonstrate that decoder-provided pilots significantly improve performance, that when coding across frequency, soft-output can further enhance performance, and that when coding across time, short codes can outperform long codes of the same rate in fast-fading channels.

Modeling signals as linear combinations of atoms from a dictionary is ubiquitous in modern signal processing. In the finite-dimensional setting, whenever atoms depend nonlinearly upon unknown parameters, the signal model is said to be separable. In this work, we study least-squares reconstruction of separable signals and establish a unified theoretical framework for their analysis. We introduce the unmixing metric, a distance that captures the distinct roles and sensitivities of linear and nonlinear parameters, and establish local convergence and stability guarantees under its topology. We then analyze variable projection from a geometric perspective, showing that it corresponds to restricting the optimization to the manifold of optimal linear parameters. This viewpoint provides a principled explanation for the improved algorithmic behavior of variable projection observed in practice, and produces sharp theoretical guarantees. The generic theory for separable problems is specialized to the case of point spread function (PSF) unmixing. We introduce a parametric notion of coherence and show that support separation directly controls both the size of the convergence region and the stability of recovery. Numerical experiments corroborate the theoretical predictions and demonstrate the practical relevance of the proposed framework.

This paper studies location privacy in uplink MIMO systems, where a user equipment seeks to spoof the angular signature observed by a single base station performing localization. We propose a blind analog precoder design that manipulates the perceived angle-of-arrival and angle-of-departure configuration without requiring channel-gain knowledge. The method enforces consistency between the received signal and a desired spoofed angular subspace, and is solved using an alternating optimization algorithm under practical amplitude constraints. Simulations in a multipath scenario show that the proposed approach achieves near-perfect angular spoofing and clearly outperforms pilot-only blind spoofing, which exhibits an error floor. The results also show a trade-off between spoofing accuracy and communication rate, depending on the chosen virtual geometry.

As global fossil fuel reserves diminish, there's a growing impetus for nations to transition towards renewable energy sources. Sri Lanka, for instance, aims to generate 70% of its electricity from renewable sources by 2030. Achieving this target requires optimal use of the existing power transmission infrastructure, as expanding the grid is both time-consuming and expensive. Traditionally, Static Line Ratings (SLRs) are used to define line capacity, often resulting in underutilization. Dynamic Line Rating (DLR), which estimates line capacity in real time based on weather conditions, offers a more efficient solution. However, DLR prediction is highly sensitive to environmental variability and forecasting complexity. This study proposes a novel multivariate Long Short-Term Memory (LSTM) model enhanced with an attention mechanism for improved DLR forecasting. Unlike traditional models that treat weather variables independently, the proposed approach captures nonlinear interdependencies among key environmental features such as ambient temperature, cable temperature, wind speed, humidity, and solar irradiance. The attention mechanism dynamically prioritizes the most relevant inputs during forecasting, leading to improved performance. Experimental evaluation on real-world DLR data demonstrates that the proposed model achieves a prediction accuracy of 95.84%, surpassing the conventional LSTM model's 94.62%. This improvement highlights the model's superior ability to deliver accurate and robust DLR forecasts. The findings confirm that incorporating multivariate features with attention enhances forecasting precision, supporting more efficient transmission line utilization and higher renewable energy integration.

This paper pioneers a novel scheme for artificial-noise (AN)-aided movable-antenna (MA)-enabled physical-layer service integration (PLSI) to harmonize the simultaneous delivery of multicast and confidential messages. By jointly exploiting the spatial reconfiguration capability of MAs and the interference shaping capability of AN, we aim to enhance secrecy performance while guaranteeing multicast reliability. The joint design of MA positions and transmit variables results in a highly coupled and non-convex optimization problem. To address this, we first provide key insights into the role of spatial degrees of freedom in AN design. We then characterize the AN direction under a structured transmission design and derive a closed-form expression for the AN-to-confidential power allocation ratio, which significantly simplifies the overall design. To solve the resulting problem, we further develop a low-complexity block coordinate ascent (BCA)-based scheme that alternates between transmit design and MA position optimization. Numerical results demonstrate that the proposed scheme achieves significant secrecy performance gains with low computational complexity and fast convergence, highlighting its effectiveness for MA-enabled PLSI systems.

Orthogonal frequency-division multiplexing (OFDM) and its static sinusoidal subcarriers have underpinned the 4G and 5G eras, delivering high spectral efficiency and resilience to multipath fading through an efficient multicarrier architecture. However, as future systems move toward doubly dispersive environments driven by high-mobility applications and migration to mmWave/sub-THz bands, the time-invariance assumption underlying OFDM becomes increasingly difficult to maintain, and Doppler-induced degradation becomes prominent. While enhancements such as MIMO, advanced coding, and scheduling provide incremental remedies, they introduce additional overhead, because the sinusoidal subcarrier itself offers no inherent waveform-level robustness to Doppler impairments. Accordingly, two time-frequency spreading philosophies have emerged to improve Doppler resilience by distributing each symbol's energy across both dimensions of the time-frequency plane: (i) 2D isotropic spreading via the delay-Doppler (DD) domain, exemplified by the orthogonal time frequency space (OTFS) family, and (ii) sheared spreading via parameterizable chirps, exemplified by the affine frequency-division multiplexing (AFDM) family. In this article, we examine key considerations for future waveform design across these paradigms and argue that transitioning from the sinusoidal subcarriers of OFDM to the chirp-based subcarriers offers a viable design direction for improving Doppler robustness while retaining much of the mature OFDM infrastructure. This perspective also highlights the suitability of chirp-based waveforms for integrated sensing and communications (ISAC) and their extensibility to emerging physical-layer techniques. Overall, we argue that the transition from sinusoids to chirps is a technically motivated, compelling evolutionary direction for future wireless physical layer design.

We investigate multi-objective adaptive beamformer design strategies for non-invasive microwave hyperthermia. Our focus is to address the challenges of maintaining focused power deposition in desired locations while reducing unwanted heating elsewhere under conditions of changing dielectric properties. The process of heating the media causes changes in the dielectric properties of the media, which can degrade the effectiveness beamformers with static weights. Typical hyperthermic beamformer designs calculate antenna beamforming weights using patient-specific high resolution dielectric maps obtained by MRI or microwave tomography, however this process is time consuming and difficult to perform in real-time. In this work, we explore the efficacy of microwave hyperthermia in various inhomogeneous media under changing dielectric conditions, with the goal of informing the design of future adaptive real-time microwave hyperthermia techniques. We aim to achieve cell apoptosis by obtaining temperatures of $\sim$ 45 $^\circ\text{C}$ through selective absorption of electromagnetic wave focusing at a 2.5 GHz carrier frequency with little to no knowledge of the changes in the dielectric media and simultaneously place nulls to avoid unwanted heating outside of the treatment zone. We investigate the effectiveness of the linear constrained minimum power (LCMP) algorithm for near-field multi-objective beamforming and examine the power density obtained from finite-difference time-domain (FDTD) simulations on simple analytical models and anatomically realistic numerical breast phantoms. To gain a comprehensive knowledge of the efficacy of the beamformer we evaluate the resulting thermal maps of the models in simple homogeneous cases, heterogeneous cases and MRI-derived phantom breast models.

This paper presents a real-world experimental analysis of a modular reconfigurable intelligent surface (RIS) prototype designed to operate in the 5G N78 band. Unlike most RIS studies in the literature that focus on simulations or controlled setups, the proposed system is validated through three phases consisting of indoor measurements, outdoor long-range tests, and deployment in a live commercial 5G standalone network. The RIS is exploited to enhance coverage in a non-line-of-sight (NLoS) zone, identified through baseline drive tests. Results show promising gains in RSRP and SINR, while also restoring 5G service at user locations where access was previously not available. The results highlight the practical potential of RIS for coverage enhancement in operational 5G networks.

Vehicular sensing-based intelligence has made substantial progress in transportation systems, leading to higher levels of safety and sustainability for smart cities and autonomous systems. This paper proposes a new approach to learn an interactive generalized dynamic Bayesian network (I-GDBN) model aiming to predict future LiDAR sensor blockages from time-sequence-based 3D point cloud perception. During learning, separate GDBN models are trained for various vehicles in normal and blockage situations. To perform the interaction between multiple vehicles, a high-level vocabulary is formed. Initially, during testing, the best generative model for either normal or blockage situations is selected. An interactive Markov jump particle filter (I-MJPF) is then proposed to leverage the probabilistic information provided by the I-GDBN to infer the blockages and detect the abnormalities at the high abstraction level. The proposed interactive model allows better self-aware and explainable capabilities that can adapt to blockage scenarios, which is also helpful when sensors fail to provide observations.

Millimeter-wave (mmWave) communication depends on highly directional beamforming, while fast mobility, blockage, and rapid geometry changes in vehicle-to-everything (V2X) scenarios make beam tracking challenging. In cooperative multi-base-station (BS) systems, conventional hierarchical methods usually separate BS selection and beam selection, which may cause error propagation when beam states change abruptly. To address this issue, this paper proposes Cooperative Radio Sensing with Large Language Models (CRS-LLM), a cooperative beam prediction framework for next-step joint BS-beam prediction. CRS-LLM formulates beam tracking as a single classification problem over the joint BS-beam space, avoiding cascaded decision errors. To adapt channel state information (CSI) to large language models, a dual-view CSI tokenizer extracts frequency-domain and delay-domain channel features through a lightweight CNN front-end and temporal tokenization module. A truncated GPT-style backbone is then used for temporal modeling with parameter-efficient adaptation. In addition, a transition-aware switch-gated predictor combines a stable branch, a residual flip branch, and a low-rank transition prior to capture both smooth evolution and abrupt changes. Simulation results show that CRS-LLM outperforms CSI-Transformer, Hierarchical BS-Beam, and representative CNN- and recurrent-neural-network baselines in Top-1 accuracy and normalized beam gain under different SNR conditions, while also showing strong few-shot performance and promising zero-shot transferability.

This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-UAV trajectory design from a repeated combinatorial optimization problem into a hierarchical probabilistic inference problem. In the offline phase, a genetic-algorithm planner with repulsive-force collision avoidance (GA--RF) generates expert demonstrations, which are abstracted into Mission, Route, and Motion dictionaries. These dictionaries are used to learn a probabilistic world model that captures how expert mission allocations induce route orders and how route orders induce motion-level behaviors. During online operation, the UAV swarm evaluates candidate actions by forming posterior beliefs over symbolic states and minimizing KL-divergence-based abnormality indicators with respect to expert-derived reference distributions. This enables mission allocation, route insertion, motion adaptation, and collision-aware replanning without rerunning the offline optimizer. Bayesian state estimators, including EKF and PF modules, are integrated at the motion level to improve trajectory correction under uncertainty. Simulation results show that the proposed framework preserves expert-like planning structure while producing smoother and more stable behavior than modified Q-learning. Additional validation using real-flight UAV trajectory data demonstrates that the learned world model can correct symbolic predictions under noisy and non-smooth observations, supporting its applicability to adaptive UAV swarm autonomy.

In this paper, we extend our method [1] for FMCW radar mutual interference mitigation (IM) based on the discrete fractional Fourier transform (DFrFT). Firstly, we propose a radar signal processing chain including our DFrFT-based IM for real-valued receivers, which we compare to reference algorithms on a synthetic data set. We then reduce computational complexity by reformulating DFrFT-based IM in terms of sparse update signals, which enables mitigation of multiple interferences simultaneously. Finally, we conduct a case study on measurement data and show that our method is compatible with real-world environments.

This paper proposes a bitwise over-parameterized neural network (ONN) decoder for polar-coded transmission and develops a tractable theoretical performance analysis framework. By modeling each synthesized message channel as an individual supervised regression task, the proposed decoder preserves the successive structure of polar decoding while enabling a communication-oriented integration of neural-network learning theory and polar-code reliability analysis. Under over-parameterization, we first characterize the empirical convergence behavior of each bitwise ONN and show that the training trajectory remains close to the random initialization. By expressing the empirical MSE convergence in the dB domain, the result further reveals a per-iteration training gain determined by the learning rate, the bit-channel Gram spectrum, and the training-set size. Upon this observation, we then derive a population mean squared error (MSE) bound via local generalization analysis and convert it into a bitwise decoding error bound through the posterior-margin structure of the bitwise maximum a posteriori (MAP) target. Under additive white Gaussian noise (AWGN) channels, a Gaussian approximation (GA)-based characterization of the low-margin probability is further established, which leads to explicit bounds for the bit error rate (BER) and block error rate (BLER). The analysis clarifies how the hidden-layer width affects optimization, generalization, and the final decoding performance, thereby providing theoretical guidance for network-scale selection. Numerical results validate the main theoretical findings and show that increasing the network width consistently improves both oracle-aided and sequential decoding performance.

Despite the rise of token communication (TokCom) as a new paradigm beyond traditional bit communication, existing approaches have primarily adopted artificial intelligence (AI)-centric designs that rely on semantic recovery via large models. Meanwhile, their physical-layer designs, such as token-bit mapping and power allocation, remain conventional and do not reflect token-level semantics. These semantics-agnostic designs can lead to significant semantic loss, particularly at low signal-to-noise ratio (SNR) levels. To address this issue, we propose hierarchical TokCom (H-TokCom), a framework that embeds semantic structure directly into physical-layer design. The key idea is to group semantically similar tokens into clusters and hierarchically assign their bit representations, where each token is represented by a cluster-level prefix and a token-specific suffix. As long as the cluster bits are correctly delivered, errors in the suffix bits typically map the received token to another within the same semantic cluster, resulting in only limited semantic distortion. This robustness is further strengthened by allocating more transmit power to the prefix bits than to the suffix bits. Simulation results show that H-TokCom achieves substantial semantic-similarity gains over conventional TokCom across the considered SNR range, increasing the semantic similarity from $0.206$ to $0.279$ at $γ=3$ dB on COCO, corresponding to a gain of $0.073$ $(35.4\%)$.

Flexible-antenna systems, which use a small number of radio frequency (RF) chains to dynamically access a large set of candidate antenna locations, have emerged as a hardware-efficient architecture for 6G networks. Acquiring accurate channel state information (CSI) is critical for these systems, but it typically incurs a prohibitive pilot overhead that scales with the massive number of candidate locations. To address this bottleneck, we propose a unified sensing-assisted channel estimation framework tailored for flexible-antenna systems. It reduces the full CSI reconstruction problem to a consistent two-stage process: it first resolves the dominant DOAs from the uplink data symbols by exploiting the spatial geometry, requiring no dedicated sensing pilot, and then calibrates the associated path gains using a minimal number of calibration pilots. Building on this pipeline, we develop two Newton-MUSIC algorithms tailored to different propagation environments. For line-of-sight (LOS)-dominant environments with uncorrelated sources, we propose SOC-Newton-MUSIC, which leverages second-order covariance (SOC) for low-complexity DOA sensing. For non-line-of-sight (NLOS) environments with coherent multipath, where the number of sources may exceed the number of activated RF chains, we propose FOC-Newton-MUSIC, which exploits fourth-order cumulants (FOC) to restore source identifiability and structurally expand the available spatial degrees of freedom (DOFs) through a continuous difference co-array. In both cases, by reformulating the spatial spectrum search as a continuous optimization problem, we replace exhaustive dense grid searches with parallelized Newton refinements.

In this paper, we propose an enhanced physical layer security approach, named joint secrecy and covert communication (JSACC), which aims to improve the performance of physical layer security (PLS). The JSACC system can dynamically switch between secrecy mode and covert mode according to the channel difference between legitimate and illegitimate receivers. We further leverage reconfigurable intelligent surface (RIS) to extend the communication range. For each scenario, we derive the closed-form expressions for the outage probability (OP) and ergodic rate (ER). To further understand system performance, we derive asymptotic approximations in the high signal-to-noise ratio (SNR) regime to obtain the diversity order and high-SNR slope. We demonstrate that the diversity order of the JSACC depends on Nakagami fading parameters and the RIS reflecting element number. Simulation results are consistent with our theoretical analysis and reveal the superiority of the JSACC system over the conventional secrecy communication (SC) system.

This paper studies flexible non-uniform array design for monostatic integrated sensing and communication (ISAC) systems. An antenna pool is considered at the base station, where each candidate antenna can be dynamically assigned to transmit, receive, or inactive modes, such that a non-uniform effective array is jointly constructed with the ISAC precoding design. We formulate a sum communication rate maximization problem by jointly optimizing the ISAC beamforming schemes and antenna-mode assignment under sensing, power, and antenna mode constraints. We develop an alternating-optimization-based solution framework mainly with the aid of weighted minimum mean square error, continuous relaxation-based penalty, and successive convex approximation. Numerical results show that the proposed non-uniform array achieves higher sum-rates than the uniform-array baselines, with particularly large gains when the number of activated antennas is small. Moreover, the proposed non-uniform array can achieve, and in some cases exceed, the performance of uniform array baselines with substantially fewer activated antennas, highlighting geometry-aware non-uniform array design as a compelling alternative to brute-force antenna scaling-based array design.

Cuffless blood pressure (BP) estimation based on Pulse Transit Time (PTT) has emerged as a promising solution for continuous health monitoring. However, conventional models relying on the Moens-Korteweg equation often fail during rapid hemodynamic fluctuations, as they assume arterial walls are purely elastic and neglect inherent viscoelasticity. To address this limitation, we propose a physics-informed framework introducing a viscoelastic compensation mechanism. First, raw photoplethysmogram (PPG) signals undergo high-fidelity reconstruction using Modified Akima (Makima) interpolation. Second, a robust Intersecting Tangent Method is applied for precise pulse foot localization. Crucially, we utilize Ensemble Empirical Mode Decomposition (EEMD) to isolate high-frequency Intrinsic Mode Functions (IMFs), defining a ``Viscoelastic Velocity Metric'' to quantify the vascular damping effect ($η\cdot \dotε$) typically ignored by elastic models. The framework was rigorously validated on a challenging subset of the MIMIC-II database (364 subjects, 28,525 cardiac cycles) characterized by a high prevalence of hypertension (23.4\%). Experimental results demonstrate medical-grade accuracy, yielding a Root Mean Square Error (RMSE) of 5.22 mmHg for Systolic and 3.65 mmHg for Diastolic BP, with Pearson correlation coefficients ($R > 0.97$). These findings confirm that incorporating viscoelastic features significantly enhances robustness against vascular hysteresis.

The emergence of movable antenna (MA) technology provides a promising way to enhance wireless sensing and communication by introducing spatial degrees of freedom through dynamic array reconfiguration. In near-field localization, achieving high resolution at low cost necessitates the adoption of sparse arrays. However, such sparsity tends to introduce spatial ambiguity due to aliasing effects. To resolve this resolution-ambiguity dilemma, this paper proposes an MA-enabled array zooming (AZ) system. First, we design a multi-measurement array zooming system that dynamically adjusts antenna spacings. By fusing the observational information from different measurements, the proposed AZ system effectively mitigates spatial aliasing while maintaining spatial resolution. Second, to quantify the performance limits under the severe multi-modal distributions inherent in sparse near-field sensing, we theoretically analyze the false peak distribution and derive a tighter performance lower bound, which incorporates the false detection probability. Third, considering that multiple false peaks may exist in practical multi-modal distributions, we propose an optimization algorithm for the AZ system to suppress false peaks and minimize the localization error. Extensive numerical results demonstrate that the proposed AZ strategy adaptively optimizes array configurations under varying signal-to-noise ratios (SNRs), substantially outperforming both conventional fixed-spacing arrays and Cramer-Rao bound (CRB)-based AZ benchmarks in localization accuracy.

Multi-band sensing has emerged as a key enabler of integrated sensing and communication (ISAC), one of the six primary usage scenarios defined for IMT-2030 (6G). The introduction of frequency range 3 (FR3, 7-24 GHz), comprising non-contiguous sub-bands across a wide frequency span, further reinforces the importance of multi-band operation. In such scenarios, frequency-dependent clutter, collectively referred to as dense multipath components (DMC), must be carefully considered. Building on prior literature and our experimental observations, this paper analyzes the impact of DMC on multi-band fusion ISAC systems by investigating Cramér-Rao bound (CRB)-based fundamental limits and the performance of our proposed multi-band estimator. Numerical results show that multi-band processing, especially in DMC-dominated scenarios, can substantially reduce estimation error and boost system resilience when channel statistics vary.

The cascaded variational quantum eigensolver (CVQE) circumvents the need for iterative communication between the quantum and classical processing units that is necessary in the conventional VQE algorithm. While CVQE offers complete freedom to choose the guiding state as input, not all guiding states suffice for solution accuracy, as well as resource efficiency. Our work presents a process based on trapezoidal-state preparation for selecting guiding states that yield accurate many-electron ground-state solutions with minimal resource consumption. By analyzing the state probability distributions at different stages of the CVQE calculations, we determine the optimal guiding-state parameters for given resource constraints. We demonstrate the process by comparing electronic energies along the minimal-energy path for a prototypical bimolecular reaction, $\mathrm{H}_2 + \mathrm{H}_2^+ \rightarrow \mathrm{H}_3^+ + \mathrm{H}$, using Noisy Intermediate-Scale Quantum (NISQ) computing.

Differential-algebraic equations (DAEs) arise naturally in constrained dynamical systems, but their algebraic constraints and hidden compatibility conditions make them more subtle than standard ordinary differential equations. This paper initiates a quantum-algorithmic study of constrained linear DAEs. We introduce a dilation framework that embeds the generally non-Hermitian constrained evolution into a projected Schrödinger-type dynamics on an enlarged Hilbert space, \[ i\frac{d}{dt}Ψ(t)=P\tH PΨ(t), \] where $\tH$ is Hermitian and $P$ is the orthogonal projector onto the lifted constraint subspace. This identifies the DAE evolution with a quantum Zeno-type dynamics and enables the use of block encodings, QSVT-based projector construction, and Hamiltonian simulation. We apply the framework to structure-preserving discretizations of the unsteady Stokes equations, where the pressure enforces the discrete incompressibility constraint. We derive the corresponding projected Hamiltonian formulation, identify low-energy spectral cutoffs motivated by solution smoothness, and discuss the resulting quantum simulation cost in comparison with classical projection-type methods. The results provide a first step toward understanding the potential intersection of quantum algorithms, DAEs, and constrained PDE dynamics.

Let ${\mathcal H}_1$ be a finite dimensional complex Hilbert space. Let $ψ\mapsto Z(ψ)$ be a canonical anti-commutation relations (CAR) field over ${\mathcal H}_1$ acting irreducibly on a Hilbert space ${\mathord{\mathscr K}}$. The $*$-algebra ${\mathscr A}_{{\mathcal H}_1}$ generated by the $Z(ψ)$, $ψ\in {\mathcal H}_1$, is simply all operators on ${\mathscr K}$. However, the CAR field endows ${\mathscr A}_{{\mathcal H}_1}$ with additional structure, and we are concerned with quantum operations whose acting in harmony with this structure. In particular, there is a gauge automorphism group generated by ``second quantizing'' $ψ\mapsto e^{it}ψ$. The fixed point algebra of the gauge group, ${\mathscr G}_{{\mathcal H}_1}$, is a sub-algebra of ${\mathscr A}_{{\mathcal H}_1}$ studied by Araki and Wyss. It contains the density matrices of an important class of states, the gauge invariant Gaussian states, ${\mathfrak S}_{GIG}$. Our focus is on semigroups $\{e^{t{\mathscr L}}\}_{t\geq 0}$ of quantum operations on ${\mathscr A}_{{\mathcal H}_1}$ that map ${\mathfrak S}_{GIG}$ into itself. Each $e^{t{\mathscr L}}$ is one-to-one, and our first main result is a structure theorem forsuch quantum operations on ${\mathscr G}_{{\mathcal H}_1}$ that map ${\mathfrak S}_{GIG}$ into itself. We apply this to study semigroups of quantum operations on ${\mathscr G}_{{\mathcal H}_1}$ that map ${\mathfrak S}_{GIG}$ into itself. Our second main result is a structure theorem showing that they are parameterized by pairs $(G,A)$ where $G$ is a contraction semigroup generator on ${\mathcal H}_1$, and $0 \leq A \leq -G -G^*$. We then show that each of these semigroups has a natural extension to the full CAR algebra ${\mathscr A}_{{\mathcal H}_1}$. Further results are obtained under further assumptions on the pair $(G,A)$.

We study black hole formation and evaporation in a four-dimensional semiclassical model that preserves diffeomorphism invariance and reproduces the one-loop trace anomaly. Solving the quantum-corrected Einstein equations for the collapse of a spherically symmetric null shell, we follow the formation and evaporation of a black hole with back-reaction included. The semiclassical solutions develop a spacelike thunderbolt singularity that emerges after the apparent horizon has receded and extends far from the black hole where the semiclassical curvature is a priori expected to be parametrically small. This behavior arises from a nonlinear instability of the higher-derivative semiclassical equations and is generic in models with anomaly-induced quantum corrections. The thunderbolt signals a breakdown of semiclassical effective field theory over macroscopic distances and undermines the standard formulation of the black hole information paradox.

In many-body quantum systems, unitary dynamics generically delocalize locally encoded information, causing single-site metrological sensitivity to vanish. We analytically demonstrate that a topological phase can prevent this dispersal. In the open Kitaev chain, a Majorana zero mode fixes the boundary quantum Fisher information (QFI) at a nonzero plateau that persists for times exponentially long in system size. We derive exact analytical expressions for the local QFI and identify the mechanism as the spatial separation of the two Majorana quadratures to opposite ends of the chain. This separation produces a boundary encoding-axis asymmetry that distinguishes topological boundary memory from a generic localized subgap signal. We show numerically that the asymmetry is robust to moderate quenched on-site disorder, while the boundary plateau remains visible under parity-preserving interactions in finite-size real-time simulations. The protocol requires only product-state initialization, Hamiltonian evolution, and single-site readout.

Quantum-gravity-induced entanglement of masses (QGEM) provides a phase-sensitive probe of extra-dimensional corrections to the Newtonian potential at submillimeter separations. We compare three representative Kaluza-Klein spectral scenarios: the Randall-Sundrum II (RSII) and Arkani-Hamed-Dimopoulos-Dvali (ADD) models, and the case of a gapped continuum modeled by a Pöschl-Teller potential. We evaluate the entangling phase, concurrence, and normalized phase-response profiles over $d=40$-$80\,μ\mathrm{m}$ using representative benchmark parameters guided by current short-range gravity tests. In this range, the signal exhibits a stable hierarchy: ADD $>$ gapped $>$ RSII. For conservative experimental parameters, the ADD signal surpasses the nominal entanglement threshold at smaller separations, whereas the gapped benchmark is resolvable only at the lower end of the window, and RSII remains below resolution. In a more optimistic near-term scenario, all three spectral signatures comfortably exceed the threshold. We further show that normalized distance scans of the phase response clearly separate the RSII benchmark from the ADD and gapped cases, whereas ADD and the gapped continuum remain nearly indistinguishable in normalized profile. QGEM phase observables therefore provide a complementary discriminator of Kaluza-Klein spectral structure at submillimeter scales.

Fault-tolerant quantum computing is a promising tool for simulating molecules and materials, but frequently-considered applications require substantial resources, and the gap between hardware capabilities and requirements remains significant. We propose quantum simulation of nanographene $π$-systems as relevant and scalable problems to span the gap between early and large-scale fault-tolerant quantum computing. We examine the efficiency of Trotterized quantum simulation, present a detailed analysis of worst-case, average-case and energy eigenvalue Trotter errors, and show that these Trotter error estimates vary by orders of magnitude. Trotter eigenvalue errors are obtained from a novel tensor-network-based approach which allows spectral analysis of product formulas for systems beyond brute-force calculation. Notably, we observe a Trotter error cancellation phenomenon whereby the Trotter error for energy differences between low-lying eigenstates is significantly smaller than the Trotter error for absolute energies, resulting in approximately an order of magnitude circuit depth reduction for quantum phase estimation calculation of energy gaps. This is a significant result because for most chemical applications, only energy differences are of practical relevance. We estimate that calculation of energy gaps to chemical accuracy between the ground- and excited-states within the Pariser--Parr--Pople model for large 2D nanographenes (up to 140 spin orbitals) requires circuits with $< 3.2 \times 10^7$ Toffoli gates. This work shows that considering details of chemically-relevant applications and exploiting error cancellation can lead to substantial reductions in resource requirements.

The Traveling Salesman Problem (TSP) is a prototypical combinatorial optimization problem, but its quantum implementation is limited by the O(n^2)-qubit overhead of standard one-hot encodings. Here, we propose a resource-efficient variational quantum framework based on compact binary-register encoding, a permutation-preserving problem-inspired ansatz, and a complementary divide-and-conquer execution strategy. The compact encoding reduces the data-qubit requirement to O(n log n), while the divide-and-conquer formulation lowers the number of qubits required in each local hardware execution to the size of the largest subsystem. Numerical simulations on TSP instances with 4, 5, and 6 cities achieve best average success rates of 100%, 100%, and 95.5%, respectively. A local two-qubit implementation of the divide-and-conquer approximation is further evaluated for a 5-city TSP instance on SpinQ Gemini Pro and SpinQ Triangulum II NMR quantum computers. Taken together, the results indicate how compact encoding and divide-and-conquer execution with classical post-processing can be used to study small combinatorial optimization instances on resource-constrained quantum hardware.

Quintessential inflation models provide a framework that simultaneously describes inflation and dynamical dark energy, the latter of which has recently received growing support from DESI observations. A distinctive feature of these models is the kination phase after inflation, which enhances primordial gravitational waves at high frequencies. In this work, we study a class of alpha-attractor quintessential inflation models using a fully numerical approach that follows the scalar-field evolution from inflation to the dark-energy-dominated era, allowing us to compute with high precision both the dynamics of dark energy and the primordial gravitational wave spectrum. Using the latest observational data, including DESI and ACT, we constrain the model parameters and show that the model becomes disfavored once constraints from the gravitational-wave contribution to the effective number of relativistic degrees of freedom, Δ Neff, are included. This is because the model predicts a scalar spectral index ns that becomes too small to remain consistent with observations when the gravitational-wave abundance is constrained to stay below the Δ Neff bound. Finally, we present the resulting primordial gravitational wave power spectrum computed using our constrained parameter values, which highlights prospects for detection by future CMB B-mode experiments at low frequencies and by gravitational-wave interferometer experiments at high frequencies.

We investigate the quantum dynamics of a levitated nanoparticle in a structured light rotating saddle-like optical potential consisting of a superposition of Gaussian and Laguerre-Gauss modes with detuned frequencies. This rotating saddle trap offers unique opportunities for quantum experiments, such as reduced decoherence due to photon recoil and absorption, the possibility of large delocalization of the particle's center-of-mass motion, particle recovery protocols, the generation of motional entanglement and momentum squeezing. As an application, we show that this saddle-trap architecture enables force detection with sensitivity in the zepto-Newton regime.

Coating thermal noise in high-reflectivity test-mass mirrors is a major limitation for future gravitational-wave detectors, especially in the 10--300 Hz band. ET-Pathfinder therefore requires mirror coatings that combine very high reflectance at 1.55 micrometer with low thermal noise under cryogenic conditions. Conventional dielectric Bragg mirrors provide high reflectance but require thick coatings, whereas metasurface mirrors can reduce coating-related noise but are limited by fabrication tolerances and line-edge roughness. We present a hybrid metasurface--Bragg mirror concept tailored to ET-Pathfinder. The design combines a fabrication-tolerant one-layer metasurface, an anti-resonant Fabry--Perot spacer, and a reduced dielectric Bragg stack. Optical performance is evaluated using full-wave electromagnetic simulations, while fabrication robustness is assessed with a truncated-Gaussian Monte Carlo analysis. Line-edge roughness is included as a systematic edge-smoothing effect. The resulting reflectance distributions are used to determine the minimum Bragg-stack support required to meet system-level specifications. The ideal metasurface exceeds 99.999% reflectance. When fabrication uncertainties and line-edge roughness are included, the metasurface reflectance is limited to about 99.9% at the 95% yield level. The remaining transmission can be compensated by a supporting Bragg stack with as few as seven layer pairs. For this configuration, the hybrid mirror achieves a total thermal displacement noise about one order of magnitude below the projected ET-Pathfinder coating-noise budget. These results show that fabrication-limited metasurface reflectance can be compensated within a hybrid architecture, enabling reduced coating thickness and thermal noise for next-generation gravitational-wave detectors.

The foundation for modeling the coupling of the internal structure of compact objects in binary systems to their dynamics and emitted gravitational waves is a systematic effective field theory (EFT) framework, where each compact object is replaced by a worldline endowed with a set of internal degrees of freedom. These degrees of freedom encode finite-size effects and thereby distinguish between different classes of compact objects. Among finite-size effects, tidal interactions play a central role, as they are associated to various kinds of deformations of a body under the influence of external tidal fields. In this work, we analyze the dynamical tidal response of Kerr black holes to generic-spin perturbations, focusing primarily on the scalar and gravitational cases, and working to linear order in frequency. We establish an EFT description of the perturbed black hole that accounts for the couplings between the spin, gravitoelectric and -magnetic tidal fields. We match this to wave-like solutions to the full black hole perturbation equations in order to determine the tidal response coefficients. In particular, we obtain the dynamical Love number, which appears at linear order in frequency for spinning black holes, and derive an approximate expression for the dynamical tidal response, including both dissipative and conservative pieces. We also examine several technical subtleties that arise in the matching procedure, with special emphasis on the mixing of multipolar modes induced by the spin of the compact object, which proves to be essential for a consistent treatment.

The accurate estimation of observables is a crucial task in quantum computing. Recent advances have highlighted the need for (a) specialized protocols for qudit-based devices, that include (b) error-aware strategies. Here, we present AQUIRE, the first protocol that can (a) accurately estimate both the mean and the error of an observable on qudit-based quantum computers. AQUIRE achieves this by constructing a Bayesian model to accommodate generalized Pauli operators. It is designed to continuously monitor the estimated average and the associated error of the observable, adjusting the subsequent measurements in real-time. Additionally, AQUIRE is (b) device- and experiment-specific error-aware, and accounts for hardware imperfections and experimental noise during the estimation process. We demonstrate AQUIRE's advantage via numerical simulations and showcase its ability to quantify the noise affecting the estimation by implementing it on a trapped-ion qudit quantum processor. By exploiting general commutation relations and overlap grouping measurements, our protocol is state-of-the-art when restricted to qubit-based quantum computers and extends this advantage to the qudit case.

Accurate models of quantum processors are essential for understanding, calibrating, and improving their performance. In practice, model construction must balance physical detail against the experimental and computational effort required to reliably learn parameters. Compact descriptions therefore often rely on assumptions about which interactions, noise processes, or hidden degrees of freedom are relevant. Here we introduce LIMINAL, a data-driven framework for testing such assumptions and selecting minimal adequate Lindblad models. LIMINAL fits nested candidate models to time-resolved tomographic data and uses likelihood-ratio tests to decide when added physical mechanisms are warranted. We apply LIMINAL to a five-qubit superconducting processor, identifying an idling model with three-local Hamiltonian terms and two-local dissipation, while finding no support for three-local dissipation. We further apply it to recover driven single-qubit Hamiltonians, reconstruct a shaped-pulse Hamiltonian without assuming an analytic pulse model, and test hidden-qubit extensions in coupler-mediated dynamics, demonstrating the applicability of the framework for a wide range of tasks.

Given a set $Γ$ of $k$ unlabelled posets, each of size $n$, we say that a poset $Q$ is a \emph{witness} to $Γ$ if $Γ$ is the set of downsets of size $n$ of $Q$. We say that $Q$ is a \emph{minimal witness} if it does not contain a proper downset that is itself a witness to $Γ$. Motivated by the causal set approach to quantum gravity, we study the upper bound on the size of minimal witnesses as a function of $n$ and $k$. We show that there is no linear upper bound of the form $n+k+c$ for any constant $c$. We introduce the \emph{exchange graph of downsets} as a new tool to study this scenario, and use it to show that all minimal witnesses $Q$ satisfy the bound $|Q|\leq nk-n$, and that when $k=3$ there is at least one minimal witness $Q$ that satisfies the bound $|Q|\leq \frac{3}{2}(n+1)$.

We present a non-supersymmetric mechanism for baryogenesis driven by the nonlinear dynamics of a complex scalar field with generalized self-interaction potentials that explicitly break the global $U(1)$ symmetry. Specifically, three representative forms of the interaction potential are considered, which give rise to intricate nonlinear source terms in the evolution of the field components. In all cases, we show that these nonlinear source terms dynamically generate a nonzero Noether charge density from symmetric initial conditions, providing a purely dynamical origin of charge asymmetry. At late times, the charge density scales as $\sim t^{-3/2}$, leading to a constant baryon-to-photon ratio through dynamical freeze-in. While the qualitative behavior is robust across models, the quantitative features depend sensitively on the interaction structure. We find that one class of potentials yields a viable parameter space over a wide range of scalar masses, whereas another requires unrealistically suppressed mass scales. A third scenario stands out in that the final asymmetry is independent of the scalar mass and depends only on the coupling parameter, enhancing predictivity and allowing compatibility across a broad range of energy scales. Assuming efficient transfer of the generated asymmetry to the Standard Model sector and negligible washout effects, the mechanism can account for the observed baryon asymmetry.

We propose and analyze a quantum interferometry scheme based on a Raman-dressed Bose gas with spin-orbit coupling. In this system, the atom-light coupling mixes spin and momentum degrees of freedom, giving rise, in the low-energy regime, to an effective spinor condensate whose spin-mixing interaction can be tuned independently of the atomic density. This controllability enables a separation between state preparation and phase imprinting, and provides a natural route to echo-type protocols based on effective time reversal. Within this framework, critical regimes of the effective spinor Hamiltonian can be used to generate entanglement and enhance interferometric sensitivity beyond the standard quantum limit. In addition, the spin-momentum locking of the dressed modes gives access to spatial density modulations that provide an alternative readout of the interferometric phase. In particular, phase information can be extracted from the displacement of spin-orbit-induced density stripes even when conventional spin observables are insensitive within the effective spinor description. Our results identify Raman-dressed spinor gases as a flexible platform for nonlinear atomic interferometry, combining controllable spin-mixing dynamics with spatially resolved phase readout.

We investigate the scalar emission from binary neutron stars in shift-symmetric scalar-tensor theories with kinetic screening ($K$-essence), using 3+1 numerical simulations in the decoupling limit. To construct static binary initial data in the regime where the screening radius $r_*$ greatly exceeds the orbital separation, we introduce a hyperbolization of the static field equations that bypasses the Keldysh-type breakdown affecting direct time evolutions. For equal-mass binaries, where the scalar emission is dominated by the $\ell=m=2$ mode, kinetic screening acts non-monotonically on the scalar radiation, suppressing or enhancing the quadrupolar amplitude depending on the relative size of $r_*$ and $λ_{22}$ (with $λ_{22}$ the wavelength): for $λ_{22}\ll r_*$ it is suppressed relative to the Fierz-Jordan-Brans-Dicke (FJBD) case, while for $λ_{22}\gtrsim r_*$ it is amplified above FJBD. For unequal-mass binaries a scalar dipole re-emerges, growing linearly with the mass asymmetry, while the quadrupolar screening remains close to the equal-mass case down to mass ratios $\sim 0.6$. The non-monotonic behavior of kinetic screening that we uncover has potential implications for gravitational-wave-based tests of gravity. The relativistic double pulsar, in particular, requires $r_*\gg 10^9$~km to efficiently suppress the scalar quadrupole; for cosmologically-motivated $Λ$, $r_*\sim 10^{11}$~km (for a solar-mass source), giving only moderate suppression.

High-fidelity logical \emph{T}-gate realization constitutes a core prerequisite for large-scale fault-tolerant quantum computing. However, conventional magic state distillation requires massive physical qubit overhead across successive distillation rounds, alongside sophisticated measurement and feedback control, thereby inducing considerable spatial and temporal resource consumption. Herein, we propose a controlled superconducting geometric logical \emph{T} gate scheme that achieves high-order suppression of universal errors, by integrating decoherence-free subspace encoding with multi-loop optimized composite geometric pulse engineering. Guided by tailored trajectory design, we systematically establish unified gate construction frameworks for conventional geometric, composite geometric, and optimized composite geometric protocols. By flexibly controling additional parametric degrees of freedom, the proposed scheme achieves substantially enhanced robustness against diverse noise sources. Numerical simulations reveal that, within tunable superconducting quantum circuits, our geometric logical \emph{T} gate outperforms both conventional composite geometric and dynamical gates in suppressing Rabi frequency, detuning, and residual inter-qubit crosstalk errors that can all be suppressed to the fourth order, while additionally providing inherent suppression of collective dephasing errors. The present strategy alleviates intrinsic limitations of mainstream approaches and opens a promising avenue toward robust high-fidelity logical \emph{T} gate construction.

A modified gravitational model whose action is given by an arbitrary function of the Ricci scalar, the matter Lagrangian density, a scalar field, and its kinetic term is investigated as an extension of the gravitational sector including an additional dynamical degree of freedom. Within this framework, the causal structure of rotating cosmological solutions is analyzed by considering Gödel and Gödel-type spacetimes as background geometries used as theoretical probes of the model consistency. Different matter sources are examined, including a perfect fluid and scalar-field configurations. It is found that the standard Gödel metric is not compatible with the scalar sector of the theory unless the model reduces to the General Relativity limit. In contrast, Gödel-type geometries admit a wider class of solutions whose causal properties depend on the model parameters and on the matter content. In particular, perfect-fluid sources may lead to either causal or noncausal configurations, whereas scalar-field configurations constrain the geometry to the causal limit, preventing the formation of closed timelike curves, highlighting that the scalar field plays a nontrivial dynamical role distinct from that of a cosmological constant.

For quantum field theory on curved spacetimes, a critical role is played by their foliation into spacelike time-slices at each value $t$ of a coordinate time, with corresponding metric in ADM form. We provide a general construction for the spacetime quantum Levi-Civita connection when each spatial slice is replaced by a quantum Riemannian geometry. This is then fully solved for a class of spatial algebras including fuzzy spheres and for any time-dependent spatial quantum metric, shift 1-form and lapse function. The result takes a particularly simple form if the spatial metric evolves in time according to a first order ODE which, in the case of a fuzzy sphere, requires the spatial metric to rotate in time according to the value at each $t$ of the shift vector. As an application, our results provide in principle fuzzy versions of most (pseudo)-Riemannian manifolds. We also fully solve the case of rotationally invariant spacetimes with angular directions replaced by a discrete circle, including a new $\Bbb Z_n$-FLRW model.

The Von Neumann entropy of reduced states is a measure of bipartite entanglement. Despite its name, the entanglement entropy cannot by itself be used as a resource for creating thermodynamic heat flows. In order to extract heat from an entangled pure state, it first needs to be converted into a stochastically mixed state by a process of quantum state reduction. Here we show that even in a system with only two degrees of freedom, for which bipartite entanglement is the sole form of entanglement available, the entanglement entropy cannot be converted into thermodynamic entropy in a one-to-one fashion. Moreover, we show that the stochastic dynamics which is necessarily present in any realistic model of quantum state reduction, allows for multiple definitions of entropy. We indicate why quantum state reduction does not allow construction of a perpetuum mobile, despite some measures of entropy evolving non-monotonically during its dynamics. Finally, we relate the different measures of entropy to the information they contain about quantum entanglement and extractable heat, and show that models of quantum state reduction based on physical, correlated stochastic driving forces give rise to observable thermodynamic signatures of quantum state reduction that can be unambiguously distinguished from environment-induced dephasing.

We investigate the origin of non-Markovianity in stochastic inflation and its implications for nonlinear perturbation theory. In the Schwinger--Keldysh formulation, the noise terms sourcing the infrared (IR) Langevin equations are determined by ultraviolet (UV) modes evolving on top of the stochastic IR background. Since the UV-mode evolution generally depends on the past history of the IR sector, the resulting stochastic dynamics is intrinsically non-Markovian. Working perturbatively, we derive the UV-mode solutions up to second order and decompose the corresponding noise contributions into two parts. The first is a ``deterministic'' contribution, generated by the functional Taylor expansion of the first-order UV solution around the background trajectory. The second is a genuinely ``stochastic'' contribution, originating from terms in the UV-mode equations that are quadratic in the noise variables and are usually neglected in the standard formulation of stochastic inflation. Under this conventional truncation, the deterministic contribution reduces to a Markovian correction in attractor backgrounds, whereas it could become history dependent in non-attractor phases and gives rise to non-Markovian terms involving integrals over first-order IR perturbations. We finally show that the stochastic contribution is of the same perturbative order as the deterministic one, which indicate that the conventional truncation is generically inconsistent and quadratic-noise terms may be required for a consistent treatment of nonlinear perturbations in stochastic inflation. Our analysis clarifies the perturbative structure of non-Markovianity and provides the basis for a systematic treatment of quadratic-noise effects beyond the standard formulation.

Quantum-memory models often reduce complex level structures to an idealized $Λ$ system, potentially missing nearby levels and unwanted couplings that can qualitatively alter the predicted performance. Here, we study an extension of a cavity-based $Λ$-type ensemble memory, a four-level model with unwanted couplings from both the control field and signal, using a fully quantum treatment. We derive explicit expressions for the single-photon storage efficiency, retrieval efficiency, and fidelity, and on this basis identify three distinct dynamical regimes: stable, threshold, and unstable. Within the stable regime, we additionally discriminate between two qualitatively different sub-regimes. Applying the theory to warm-vapor-inspired parameters, we determine the conditions under which the system can still operate as a high-quality quantum memory. More generally, our results provide a practical framework for distinguishing genuine memory operation from amplification and for optimizing realistic quantum memories beyond idealized models.

Selection artefacts are common in science. A method of selecting samples from a larger population may produce bias, in either direction. It may induce correlations between variables independent in the full population, or mask correlations between variables dependent in the full population. Here we propose a surprising application of these familiar ideas. We argue that they are relevant to puzzling correlations uncovered in quantum theory by John Stewart Bell (Bell 1964). In the light of Bell's work and subsequent experiments it is widely believed that the quantum world is 'nonlocal', in apparent tension with relativity. Many hold that the only alternative is to abandon 'realism', the view that there is an objective world independent of measurement. We propose instead that Bell correlations are selection artefacts, in tension neither with relativity nor realism.

The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncertain, but could potentially be constrained with new observations. While NSs have been observed with masses up to $\sim2~M_{\odot}$, there is a dearth of electromagnetic observations of compact objects in the $\sim2-5~M_{\odot}$ range, known as the lower mass gap. Recent observations of gravitational-wave (GW) signals from binary mergers detected by the LIGO-Virgo-KAGRA (LVK) collaboration indicate that this gap is likely not empty. Rapidly distinguishing whether a candidate GW event has components in this purported mass gap can indicate the likelihood of a detectable electromagnetic counterpart, and thus inform decisions for follow-up observations. In this work we train a neural network model, GWSkyNet-MassGap, that simultaneously predicts the probability that a candidate merger has a component in the lower mass gap ($P_{\mathrm{MassGap}}$) and the probability that it involves a NS ($P_{\mathrm{NS}}$). We find that the model is able to infer information about the source chirp mass to predict $P_{\mathrm{MassGap}}$ and $P_{\mathrm{NS}}$, leading to correct predictions for high-mass mergers with $\mathcal{M}_c\gtrsim15~M_{\odot}$, but less accurate predictions for lower-mass systems which require knowledge of the binary mass ratio to break the mass degeneracy. For candidate events in the first part of LVK's fourth observing run (O4a), the model has a mean prediction error of 9% for $P_{\mathrm{MassGap}}$ and 6% for $P_{\mathrm{NS}}$. The model could be further developed to rapidly predict the source chirp mass for candidate events in future observing runs.

The study of black hole thermodynamics becomes a central topic in gravitational physics, where the first law and the Smarr relation establish a deep connection between spacetime geometry and thermodynamic laws. As we know, these relations depend on the entropy; any modification to the entropy arising from quantum gravity or generalized statistical mechanics may impact the basic thermodynamic framework of black holes. In this work, we develop a general framework for deriving the first law of black hole thermodynamics and the associated Smarr relation for generic spherically symmetric spacetime under a wide class of generalized entropy models. In addition, a generalized Ruppeiner thermodynamic geometry is developed to utilize the generalized entropy model, from which the curvature scalar is determined in a general form. To demonstrate this framework, we assume the Resinser-Nordström black hole and investigate the corresponding extremal and non-extremal phase transition. Interestingly, our analysis reveals that entropy models consistent with the Abè-type composition rule result in a vanishing thermodynamic curvature, whereas violations of this rule exhibit curvature divergences, suggesting a geometric test for the consistency of generalized entropy models.

We introduce a hierarchical algorithm for identifying the largest Pauli coefficients of an unknown $n$-qubit quantum state. The algorithm traverses a prefix-based tree whose nodes represent partial sums of squared Pauli coefficients, always expanding branches with the largest estimated weight and discarding the rest. Node weights are estimated using Bell sampling on two copies of the state, or alternatively via SWAP tests on subsystems. We analyze the sample complexity of each node estimation and derive bounds on the total number of nodes expanded as a function of the desired number of coefficients and the state's purity. For states admitting a sparse representation in the Pauli basis, the algorithm achieves a good reconstruction of the dominant components without requiring full state tomography. We validate the method with numerical simulations on Pauli-singleton states and random stabilizer states, showing that the algorithm's performance is competitive with other methods for structured states. Our work addresses an open problem in Pauli sampling and provides a practical tool for the targeted characterization of structured quantum states.

Owing to the radiation-force-induced nonlinearity, cavity optomechanical systems (COMS) exhibit dynamical phenomena such as back-action induced oscillation, chaos, mechanical amplitude locking, and anomalous stabilization, which occur under different driving conditions and different system parameters. We here identify a previously unknown dynamical pattern of staircase evolution for the energy of mechanical resonator, when a COMS with neither very large nor very small built-in mechanical frequency is driven by a two-tone field, which satisfies a condition that the frequency difference of the two tones matches the built-in mechanical frequency. The properties of this phenomenon are analyzed for the different system parameters due to fabrication such as mechanical frequencies and quality factors, as well as under the varied driving conditions such as unequal drive tone powers and mismatched drive tone difference from the mechanical frequency. Some special features, such as an emergent bifurcation due to the tone power difference, together with the totally different responses of the system to the drive tone mismatches of opposite signs, are discovered to exist only in this type of COMS with median mechanical frequencies. This work fills a gap in the study of the dynamics of COMS under two-tone drives. In the aspect of applications, the rapid increase of mechanical energy exhibited in the phenomenon promises phonon laser generation, and the sensitive dynamical response to the drive tone mismatches offers a potential approach to high-precision sensing.

The canonical Mach-Zehnder interferometer fed with a coherent state and a squeezed-vacuum state of equal intensities is theoretically predicted to achieve Heisenberg scaling in phase sensitivity. However, this ultimate performance is unattainable using direct photon-number-difference detection due to a divergence arising precisely at the optimal equal-intensity regime. In this work, we introduce a dual-squeezing approach that overcomes this fundamental limitation. Our scheme employs an additional single-mode squeezer before detection, forming a paired configuration with the input squeezer used to generate the squeezed-vacuum state. We analytically demonstrate that the resulting dual-squeezing Mach-Zehnder interferometer enables Heisenberg-limited phase sensitivity with di rect photon-number-difference detection, while remaining robust against detection noise. Our work provides a feasible and robust route toward quantum-limited interferometric phase measurements

Attaining a quantum speedup in solving practically useful optimization problems has been one of the holy grails in the field of quantum computing. While prior approaches have demonstrated speedups for certain structured problem classes, establishing a clear and scalable advantage on broadly useful practical optimization problems remains challenging. Recently, a new approach to solving the max-LINSAT class of optimization problems has emerged, called Decoded Quantum Interferometry (DQI). In DQI, a combination of techniques rooted in (classical) coding theory and interferometry are used to obtain the solution of max-LINSAT. In the special problem instance of the optimal polynomial intersection (OPI) problem, strong evidence exists to show that an superpolynomial speedup exists over the best classical methods in obtaining an approximate solution. In this review, we give a self-contained description of DQI and the necessary background to understand the algorithm. Specifically, we give the essentials of Galois fields, optimization problems such as max-LINSAT and OPI, and coding theory, followed by a step-by-step walkthrough of the quantum algorithm and its operating principle.

Herein, we introduce a strategy to decompose an arbitrary square matrix into a linear combination of non-unitaries (LCNU) where each non-unitary term is embedded into a unitary matrix. The result is a linear combination of unitaries (LCU) with an equal number of terms as the LCNU. Using this approach, we construct a generalized LCU framework for any Carleman linearized autonomous dynamical system with a polynomial nonlinearity. This framework is then used to construct an LCU for the 3-dimensional Carleman linearized lattice Boltzmann equation (LBE) in which the number of terms scales like $N_s \sim \mathcal{O}(α^2 Q^2)$, where $α$ is the Carleman truncation order and $Q$ is the number of discrete velocities from the LBE. Importantly, $N_s$ is completely independent of both the number of temporal and spatial discretization points. Lastly, we provide an estimate of our LCNU strategy's T gate cost in conjunction with (1) PREP and SELECT block encoding oracles, and (2) the variational quantum linear solver. In the former, the T cost scales like $\mathcal{O}(α^3 Q^2 (\log_2 n)^2)$, where $n$ is the total number of spatial grid points across all dimensions. Next, the latter requires $N_s^2(\log_2 (2n_tn^α)+1)$ circuits per iteration, with a worst case T gate cost of $\mathcal{O}(α(\log_2 Qn)^2)$ among them. We, therefore, provide an efficient decomposition strategy useful for both fault-tolerant and variational approaches.

We present a protocol in which sequential weak measurements of a quantum harmonic oscillator enable simultaneous estimation of both quadratures of a displacement channel. Calculations of the quantum Fisher information show that the measurement backaction can increase the information gained for a range of measurement strengths. The protocol distributes information over a $N$-bit string after $N$ weak measurements. Thus we find that post-processing can be used to avoid information loss due to phase wrapping, increasing the effective dynamic range. Finally, the periodic information extraction makes the protocol robust to decoherence. Our results establish mid-sensing measurement as a resource for single- and multi-parameter quantum metrology.

Time-resolved x-ray diffraction (TR-XRD) and ultrafast electron diffraction (TR-UED) are emerging tools for probing ultrafast quantum dynamics. From a theoretical perspective, they are commonly described within different frameworks and modeled using distinct approximations. Here, we present a unified quantum-field-based description of ultrafast diffraction imaging that permits consistent consideration of TR-XRD and TR-UED within a common theoretical formalism. Our approach elucidates the correspondence between TR-XRD and TR-UED and allows their similarities and differences to be systematically disentangled. The developed formalism is sufficiently general to consistently and straightforwardly incorporate additional physical effects of interest, such as relativistic charge-current and current-current couplings. We apply our approach to simulate diffraction measurements of laser-driven electron dynamics in graphene, demonstrating the unique capabilities of diffraction imaging to unravel intricate quantum processes in matter.

We study the entanglement distance of variational quantum states for two-qubit and multi-qubit systems. These states are constructed using variational quantum circuits with $R_Y$ rotations and entangling $CZ$ gates.For the two-qubit case, we analytically derive recurrence relations for expectation values of Pauli observables using. This approach allows us to analytically calculate quantum correlators and evaluate the entanglement distance depending on the circuit parameters and depth. The analysis were extended to a closed one-dimensional chain of $N$ qubits. It is shown that with increasing circuit depth, more qubits influence a given qubit, which reflects the spreading of quantum correlations in the system. For a closed one-dimensional chain of $N$ qubits, explicit analytical expressions are derived for the case of two layers. The results are compared with numerical simulations performed using quantum programming tools. The results agree with the theoretical predictions.

We explore the possibility of explaining dark matter through six-dimensional (6D) primordial black holes (PBHs) in a theory with two extra dimensions. Interestingly, in this scenario the fundamental energy scale is of the order of $\sim 10$ TeV, accessible by future experiments. We analyse the viability of charged and rotating 6D black holes under standard Hawking evaporation as well as the memory burden scenario. In the case of pure Hawking evaporation, only PBHs with masses $M > 10^8$ g survive to present, while the lifetime of near-extremal configurations is extended by a factor $1/β^{1/2}$, where the parameter $β$ characterizes small deviations from extremality. In the memory burden scenario evaporation is enormously suppressed, and sub-gram mass PBHs can survive to the present epoch. At future colliders such as the Future Circular Collider, these micro black holes produce characteristic high multiplicity events, $\langle N \rangle \sim 21$, with thermal spectra, enabling direct probes of the fundamental scale and the number of extra dimensions. We find that the memory burden mechanism opens a broad new mass window for light PBH dark matter, while the Kaluza-Klein mass splitting $Δm$ aligns with the atmospheric neutrino scale, suggesting a unified framework between Swampland constraints, cosmology, collider physics, and low energy phenomenology.

Many quantum-network applications require end-to-end Bell pairs whose fidelity exceeds a request-specific threshold, but existing entanglement routing algorithms either optimize only throughput without regard for fidelity or enforce fidelity guarantees using centralized controllers with global link-state knowledge. We present Q-GUARD, an online entanglement routing algorithm that enforces per-request fidelity thresholds within a distributed protocol model in which nodes exchange link-state information only with their $k$-hop neighbors. After link outcomes are realized in each slot, Q-GUARD builds per-link purification cost tables from realized Bell pairs, allocates per-hop fidelity targets using a Werner-state equal-split rule, and selects between candidate path segments using a segment-local expected-goodput (EXG) metric that jointly accounts for swap success, purification overhead, and resource availability. We also introduce Q-GUARD-WS, an extension that exploits per-link hardware quality estimates to allocate purification effort non-uniformly across hops. On synthetic 100-node topologies with heterogeneous link fidelity and stochastic BBPSSW purification, Q-GUARD raises the qualified success rate from under 20\% to over 85\% on 4-hop paths and nearly doubles the qualified service radius in Euclidean distance relative to throughput-only and naive-purification baselines, while Q-GUARD-WS provides additional throughput gains under high hardware heterogeneity.

This thesis, explores the quantum entanglement and evolution through both a geometric and dynamical perspective. The first part focuses on classical phase space and its central role in Hamiltonian mechanics, emphasizing the importance of symplectic structures in describing mechanical states. The study highlights the formal analogy between classical phase space and the Hilbert space used in quantum mechanics. The second part is devoted to the geometric description of quantum states through the projective structure of Hilbert space. Emphasis is placed on the geometric interpretation of quantum evolution, particularly via the Fubini-Study metric, associated symplectic structures, and the geometric phase acquired during unitary evolutions. The final two parts are dedicated to the study of spin systems (both two-body and many-body) under different interaction models (XXZ Heisenberg and all-range Ising). Both the dynamical aspects (evolution speed, entanglement, and the quantum brachistochrone problem) and the geometric and topological structures of the corresponding quantum states are analyzed.

Entangled photon pairs play a major role in various modern technologies such as quantum imaging, communication, and computing. Conventional photon-pair sources are often based on spontaneous parametric down-conversion in bulk nonlinear crystals. Recent advances have also shown entangled photon-pairs from transition metal dichalcogenide thin-films, however, these materials are not widely available and are not compatible with existing fabrication capabilities. We present a new thin-film lithium niobate source of polarization-entangled photon pairs at the telecom wavelength that requires no additional optical elements for entanglement generation and allows for easy application using the existing lithium niobate fabrication technologies. We demonstrate tunable entanglement generation using the three-fold rotational crystal symmetry of lithium niobate, allowing the generation of different maximally entangled Bell states or completely separable states depending on the polarization of the pump beam.

Quantum science and biology now intersect in three complementary directions: quantum in biology, quantum for biology, and biology for quantum. This review provides a structured narrative evidence map of that interface rather than an exhaustive catalogue or formal systematic review. For each topic, we ask what the mechanistic or technological claim is, which quantum resource is invoked, what the strongest experiments and models establish, which classical alternatives or engineering confounds remain competitive, and what decisive tests or benchmarks would most strongly change confidence. The most mature quantum-in-biology cases remain mechanistically constrained tunneling in some enzymatic hydrogen-transfer reactions and radical-pair spin chemistry as a viable framework for magnetoreception, whereas several higher-visibility topics remain suggestive but unresolved under physiological conditions. In quantum for biology, the central issue is whether quantum-enabled tools improve biological inference relative to strong classical baselines under realistic calibration, dose, throughput, and uncertainty constraints. In biology for quantum, the strongest claims arise when biomolecular structure or self-assembly measurably improves fabrication, integration, or robustness in quantum devices. Summary tables in the Appendix provide a compact cross-map view of the current evidence, major confounds, and the experiments or benchmarks most likely to discriminate between competing explanations.

In recent years, many interesting works providing a topological description for black hole (BH) properties have appeared in the literature. In particular, in this framework BHs correspond to topological defects in an enlarged (off-shell) parameter space, with an associated total topological charge. In gauge/gravity duality the transition from the confined to the deconfined phase is mapped into the dominance of a BH phase in the gravity side. Here we show, using a holographic AdS/QCD model, that the introduction of an energy scale in anti-de Sitter (AdS) space results in a change in the topological class. Such a modification corresponds to the existence of confined and deconfined phases, separated by a Hawking Page transition at a finite critical temperature.

We develop a product-level trace-Dobrushin theory for finite-dimensional quantum channel products and apply it to deterministic and stationary random inhomogeneous matrix product states in left-canonical CPTP gauge. For a product of channels, the centered trace-Dobrushin coefficient quantifies the residual dependence on the input state, and its decay is the criterion for trace-norm forgetting. In the deterministic setting, this decay is equivalent to asymptotic replacement by a moving replacement channel. For two-sided products, pullback forgetting produces a unique boundary state, which determines the canonical replacement family. For stationary random CPTP cocycles, submultiplicativity of the product coefficient yields a trace-Dobrushin Lyapunov exponent. We prove that the almost sure negativity of this exponent is equivalent to quenched trace-norm memory loss and gives exponential forward and pullback convergence to a unique dynamically stationary random replacement channel. When the \(\varrho\)-mixing profile of the channel environment tends to zero, we obtain annealed super-polynomial estimates, while independence gives annealed exponential estimates. Finally, we transfer these estimates to inhomogeneous matrix product states whose auxiliary transfer maps are CPTP. These channel estimates transfer to deterministic and stationary random inhomogeneous MPS, giving infinite-volume limits of trace-closed finite-volume states, quantitative boundary stability, and correlation bounds governed by the same auxiliary product coefficients.

Shared randomness is the central ingredient for stabilizing symmetrizable communication systems against arbitrarily varying jammers. Given the presence of the jammer, however, the question arises how this precious resource could have been distributed. Several works discuss the use of external sources for this task. In this work, we show, based on the most standard optical communication model, how the sender and receiver can employ entangled two-mode squeezed states to counter the jamming attack of an energy-limited jammer during the distribution phase when both the sender and jammer are allowed to use binary phase shift keying and two-mode squeezed vacuum states.

We provide a critical assessment of a recent study applying quantum information concepts, including noisy channels and teleportation fidelity, to hyperon-antihyperon pairs produced in $e^{+}e^{-} \to Y\bar Y$ reactions at BESIII. While the spin density matrix reconstructed from experimental data provides a physically meaningful description of production correlations, we argue that its subsequent interpretation in terms of standard decoherence models-such as amplitude damping, phase damping, and phase flip-lacks a clear physical correspondence for these systems. The produced particles emerge from a single scattering event and propagate as free, unstable relativistic states, without a well-defined system-environment interaction acting on their spin degrees of freedom. As a result, the variation of quantum correlations with an abstract noise parameter does not describe a genuine physical evolution. We further contend that the reported teleportation fidelity should not be interpreted as evidence for operational quantum communication, since hyperon states cannot be prepared, controlled, or measured in a way that would enable a realizable teleportation protocol. More generally, quantities such as logarithmic negativity, local quantum uncertainty, and local quantum Fisher information primarily characterize static production correlations rather than directly usable quantum resources. Our analysis highlights the importance of distinguishing between formal quantum-information measures and their physical interpretation in high-energy particle systems.

We critically examine recent claims [Phys. Rev. D 113, 016024 (2026)] regarding quantum coherence, steering, and non-Markovian dynamics in the hyperon-antihyperon system produced in the process $e^{+} e^{-} \rightarrow Λ\barΛ$. We argue that the theoretical framework employed in the analyzed work suffers from fundamental physical inconsistencies. In particular, the treatment of the $Λ\barΛ$ pair as a bipartite system evolving under correlated quantum channels is not physically justified, since the produced hyperons are free, unstable particles that do not interact with a common environment after production. Consequently, the application of open quantum system techniques, including Markovian and non-Markovian quantum channels, lacks a clear physical basis. Moreover, we show that the computation and interpretation of quantum steering for this system is operationally and conceptually meaningless, as no well-defined measurement-induced state update or controllable local measurement scenario exists for unstable relativistic particles. These issues call into question the physical relevance of the reported quantum correlations, their hierarchy, and their dynamical behavior. Our analysis highlights the necessity of carefully distinguishing between formal mathematical quantifiers of quantumness and physically realizable quantum information protocols in high-energy particle systems.

Accurate models of merger remnants are increasingly important for gravitational-wave science, including precision tests of gravity with ringdown, inference of black-hole populations, and modeling hierarchical mergers. For eccentric binaries, remnant mass, spin, and recoil carry nontrivial imprints of eccentricity that are both physically informative and more challenging to model, yet remain less developed than in the quasi-circular case. We present two new models trained on numerical-relativity (NR) simulations of unequal-mass, non-spinning eccentric binary black holes: NRSurE_q4NoSpin_Remnant, which predicts remnant properties, and NRSurE_q4NoSpin_Dynamics, a time-domain surrogate for the evolution of eccentricity and mean anomaly. Both models are trained on NR simulations over a three-dimensional parameter space with mass ratios $q \leq 4$, eccentricity $e < 0.23$, and mean anomaly $\ell \in [0,2π)$ radians, where both $e$ and $\ell$ defined at $t=-1000M$ relative to peak amplitude and $M$ is the total mass. We highlight some applications, including the phenomenological impact of eccentricity on remnant properties and the enhancement or suppression of recoil. We also provide error estimates for all modeled quantities, supporting reliable use in current and future gravitational-wave parameter-estimation analyses. Both models will be made available through open-source codes.

Multitenancy increases throughput and reduces costs in cloud-based quantum computing, but concurrent job execution introduces security risks through inter-circuit crosstalk. We characterize the structural predictability of these interference patterns across seven IBM superconducting processors, spanning Heron (r1-r3) and Nighthawk (r1) architectures and five different circuit types. We evaluate pairwise interactions, by applying the Structural Similarity Index (SSIM) and a structural $t$-statistic to the concurrent execution of five foundational quantum circuits (QAOA, Grover's, QPE, QFT, and ZZFeatureMap), we quantify behavioral consistency across disparate hardware. Our results identify three types of circuits: universally aggressive, universally sensitive, and cotenant-dependent circuits. Aggressive circuits, such as Grover's Algorithm, exhibit a statistically significant interference pattern, yielding a $t$-statistic range of $[1.37,2.61]$ relative to the standalone baselines across all tested pairings. Conversely, sensitive circuits, such as the Quantum Fourier Transform, demonstrate a disproportionate susceptibility to multitenant execution, showing high deviations from single-tenant computational behavior. We demonstrate that crosstalk signatures are highly consistent within architectural revisions--with intra-revision similarity reaching $0.77$ (Hr3) and $0.68$ (Hr2)--while inter-revision similarity drops to $0.43$. Furthermore, we identify a ``topological decoupling" between Heavy-Hex and square lattice systems, where structural similarity falls to $0.01$ between Heron r1 and Nighthawk r1. These findings provide an empirical foundation for hardware-aware schedulers to strategically pair jobs, maximizing system utilization while preserving computational integrity.

Causality in linear response is conventionally treated as a binary property: a response function is either analytic in the upper half-plane or it is not. We show that in a PT-symmetric open dimer it instead carries a topological charge. As the gain-loss parameter crosses the exceptional point, a single pole of the reflection coefficient migrates into the upper half-plane, the Blaschke winding number jumps from 0 to 1, and standard Kramers-Kronig (KK) reconstruction acquires a Lorentzian residual fixed by the pole residue. The transition is sharp, protected by the codimension-one structure of the exceptional point, and directly measurable in a one-port reflection experiment. Most strikingly, the violation magnitude scales as Delta_KK ~ |gamma - gamma_c|^nu with nu ~ -1.08 in the single-port geometry: the breakdown of standard KK is strongest at threshold and weakens deeper in the broken phase. We derive the exact reflection coefficient, verify the residue-corrected dispersion relation, and propose a THz time-domain spectroscopy protocol that detects the topological charge through the residual itself.

We derive a compact, covariant expression for the relative Lorentz factor of two particles in curved spacetime and apply it to particle decay in Kerr spacetime. This allows us to show that energy conservation in the local center-of-mass frame requires the rest-mass loss of the parent particle to be converted into kinetic energy of the decay products. We verify this relation analytically and confirm it with high-precision reconstructions of three representative Penrose-process examples, for which both equivalent conservation formulas are satisfied to machine precision. These results clarify the local kinematics underlying energy extraction from rotating black holes and show that mass loss is not optional but is required for the decay products to separate.

Electro-optic modulators are key components for photonic quantum computing, particularly in fully cryovenic integrated platforms where low loss and compactness are critical. We present a systematic theoretical investigation of compact dual-layer graphene (DSLG) electro-optic phase modulators integrated on silicon nitride waveguides, with emphasis on cryogenic operation. By combining electromagnetic simulations with a physically consistent description of graphene conductivity based on the Kybo formalism, we analyze the interplay between electrostatic tuning, optical mode confinement, and material-dependent losses. We show that cryogenic operation enhances device performance by sharpening the Fermi-Dirac distribution, enabling access to the Pauli-blocking regime at lower Fermi levels and reducing the required modulation length. Through optimization of the waveguide geometry, dielectric spacer thickness and permittivity, and graphene quality, we identify regimes that simultaneously minimize insertion loss and device footprint under realistic voltage constraints. The optimized designs achieve near-pure phase modulation with insertion losses below 0.3 dB and modulation lengths below 50 um at 10 K, while maintaining GHz-scale bandwidths. These results provide quantitative design guidelines for low-loss, compact, cryogenic graphene phase modulators for scalable integrated quantum photonics.

Tensor networks and circuits are widely used data structures to represent pseudo-Boolean functions. These two formalisms have been studied primarily in separate communities, and this paper aims to establish equivalences between them. We show that some classes of tensor networks that are appealing in practice correspond to classes of circuits with specific properties that have been studied in knowledge compilation as \emph{tractable circuits}. In particular, we prove that matrix product states (tensor trains) coincide with nondeterministic edge-valued decision diagrams and that tree tensor networks exactly correspond to structured-decomposable circuits. These correspondences enable direct transfer of structural and algorithmic results; for example, canonicity and tractability guarantees known for circuits yield analogous guarantees for the associated tensor networks, and vice versa.

Nonreciprocity can profoundly alter the spectra and dynamics of open quantum systems, yet its impact on the long-time steady-state phases of matter has remained largely unexplored. Here we show that the interplay of nonreciprocity, symmetry defects, and spatial boundaries can generate phases beyond the standard spontaneous-symmetry-breaking paradigm. We demonstrate this mechanism by showing that sufficiently strong nonreciprocity turns boundaries into sources and drains of symmetry defects, while simultaneously endowing these defects with chiral dynamics in the bulk. As a result, the conventional uniform symmetry-broken state gives way to a domain-wall traveling-wave phase, in which symmetry defects form a persistent chiral wave. We showcase this mechanism in a bosonic model with \(Z_{2}\) symmetry, where periodic boundary conditions support only the conventional symmetric and symmetry-broken phases, whereas open boundary conditions allow the traveling-wave phase. We further show that even in the absence of symmetry breaking, the steady state can exhibit anomalous chiral relaxation: owing to the non-Hermitian skin effect in the stability matrix, local fluctuations are chirally amplified as they approach a boundary, where they eventually decay. Combining mean-field theory with truncated Wigner simulations, we characterize these phases, analyze the order parameter and Goldstone-mode fluctuations of the traveling-wave phase, and confirm its existence in three spatial dimensions.

We study the generation of metrologically useful entanglement in a three-level (spin-1) system naturally realized in arrays of dipole-interacting Rydberg atoms confined in optical tweezers. In the spin-quadrupolar operator basis, the interaction Hamiltonian decomposes into effective SU(2) subspaces, within which quench dynamics from product initial states generate scalable spin-nematic squeezing. For symmetric interactions, we identify a mapping to effective one-axis twisting within bright and dark manifolds and demonstrate that the squeezing parameter scales as $ξ^{2}\propto N^{-2/3}$ ($ξ^{2}\propto N^{-0.5}$) with system size for all-to-all (two-dimensional dipolar) couplings. In both cases the quantum Fisher information reaches $F_Q\propto N^{2}$. For antisymmetric interactions supplemented by a microwave drive we find a distinct two-axis countertwisting mechanism. This results in squeezing $ξ^{2}\propto N^{-0.7}$ for all-to-all interactions and moderate squeezing for dipolar interactions in 2D. Our results constitute a first theoretical step beyond the well-studied qubit setting toward scalable entanglement generation in qudit systems with dipolar interactions, directly relevant to current Rydberg tweezer experiments.

Merging galaxy clusters are a promising laboratory for measuring the self-interaction cross-section (SICS) of dark matter. However, previous studies have focused on galaxy-mass offsets, which numerical simulations have shown to be intrinsically small because galaxies remain tightly coupled to the dominant dark matter potential even with significant self-interaction. Their interpretation is further complicated by unknowns of the merger phase, geometry, and initial conditions. In this paper, we overcome these obstacles by introducing the shock-to-shock distance, traced by double radio relics, as a merger chronometer that time-stamps the post-pericenter dynamical phase. Because the propagation speed of merger shocks is nearly independent of the SICS, while the halo-to-halo distance is depressed by SIDM-induced drag, the ratio of the two distances translates directly into a constraint on sigma/m. Applying this method to a gold sample of eleven cluster mergers hosting symmetric double radio relics, we determine a 68% upper limit on the SICS of sigma/m < 0.22 cm^2/g. This is the first constraint from cluster collisions that fully marginalizes over mass uncertainty, viewing angle, collision speed, merger phase, impact parameter, and gas profile slope.

Levitation of microscopic objects in vacuum combines exceptional environmental isolation with precise control of their dynamics, pushing the limits of sensing and macroscopic quantum physics. In particular, magnetic levitation allows a large range of particle sizes, while avoiding detrimental effects from high-intensity optical trapping beams and electric field noise. However, existing diamagnetic and Meissner levitation approaches are typically constrained by low mechanical eigenfrequencies, limited integrability with other systems due to bulky coils or magnets, and, for Meissner levitation, the need for cryogenic operation. Here, we demonstrate a room-temperature on-chip magnetic levitation platform capable of stably levitating a nanogram (6.5 micrometer radius) ferromagnetic microsphere. The platform is scalable and tunable, and supports librational modes with eigenfrequencies exceeding 10 kHz. Further miniaturization and coupling to solid-state spin qubits could enable cooling to the quantum ground state. Beyond quantum experiments, this architecture enables integrated precision sensing and studies of isolated ferromagnet thermodynamics.

The Karch-Randall braneworld concerns the physics of an AdS$_{d}$ brane embedded in an ambient gravitational AdS$_{d+1}$ spacetime. The gravitational theory induced on the AdS$_{d}$ brane has a very light but massive graviton. It has been established that the zero graviton mass limit of the $d$-dimensional graviton propagator is smooth at tree-level. Furthermore, this smoothness was conjectured to persist to the quantum level. This conjecture suggests that the massive graviton on the AdS$_{d}$ brane is due to spontaneous symmetry breaking, which is consistent with its holographic dual description. In this letter, we show that the zero mass limit of the partition function is a theory of a massless graviton and a decoupled massive vector. The zero mass limit is not the basic Randall-Sundrum II model, but a theory with these additional decoupled vector degrees of freedom coupled only to gravity. The proof relies on deriving the fully covariant description of the $d$-dimensional gravity theory which enables us to compute the one-loop partition function. At the end, we comment on the implications of this result to the physics of entanglement islands.

This is a first study of the cosmology of classical fractional gravity, a nonlocal proposal endowed with self-adjoint fractional d'Alembertian operators which serves as the basis for an ultraviolet-complete theory of quantum gravity. We derive the classical covariant nonlocal equations of motion for an arbitrary fractional exponent $γ$ and reduce them to the Friedmann equations on a homogeneous and isotropic cosmological background. We find that de Sitter is an exact stable solution and that bouncing exact solutions are sustained by phantom ($w<-1$) or ghost ($ρ<0$) fluids, in the latter case with a new type of finite-future singularity in the barotropic index. Different representations of the form factor give exactly the same solutions, thus confirming that the formulation of fractional field theories relies on a universality class of form factors. We compare these preliminary results with what obtained in multi-fractional cosmological models mimicking the spacetime geometry of fractional quantum gravity.

Near-term quantum computers are accessed through repeated circuit executions, which produce finite measurement records rather than exact deterministic outputs. In quantum reservoir computing, these records are converted to feature vectors for a classical readout. The standard expectation-value approach averages all shots from one labeled time step into a single feature vector. This reduces finite-shot noise, but it also gives the readout only one training example from many circuit executions. We introduce split-ensemble training: the same shots are split into groups, and each group average is used as a separate, partially denoised feature vector for the same target. The quantum circuit, task, and measurement budget remain unchanged. Across simulated forecasting benchmarks and real hardware experiments, this simple reorganization improves prediction when full averaging leaves the readout with too few training examples, with the strongest gains observed on hardware. Our results establish shot-record organization as a simple, broadly applicable algorithmic lever for improving near-term quantum learning without additional quantum hardware cost.

We study geodesic motion of a test particle in Schwarzschild spacetime. Bound and scattering geodesics are commonly described using Darwin variables, which provide a convenient parametrization of the radial motion. However, this description breaks down at the separatrix and does not extend straightforwardly to plunging trajectories. We construct an analytic continuation of Darwin variables that yields a real parametrization of bound, scattering, and plunging Schwarzschild geodesics, thereby providing a unified kinematical description of all types of test-mass motion. As a proof of concept, we then apply these variables to a simple non-geodesic evolution in which the energy and angular momentum are driven by a constant external force. This toy model is not intended to represent a physical radiation-reaction model, but rather to illustrate how the extended variables can be used to follow an orbit through a transition to plunge using a single orbital phase variable across the separatrix.

Quantum measurement is physically realized through a finite dynamical interaction between a system and a measuring apparatus, giving rise to a continuous transition from weak to strong regimes. While this crossover is well understood under ideal conditions, the combined role of thermal instabilities and pre- and post-selection open dynamics has not been systematically addressed. Here, we develop a general framework to analyze the weak-to-strong measurement transition in the simultaneous presence of environmental decoherence and thermal noise. We model the probe as a thermal Gaussian state, explicitly incorporating temperature-dependent fluctuations in the measuring device, and include open-system evolution of the measured system prior to post-selection. By deriving the apparatus's final state, we show that the measurement statistics are modified in a nontrivial, highly sensitive manner by the temperature regime of the system's thermal instabilities, the probe's thermal properties, and the particular choice of pre- and post-selection. This approach allows us to characterize how thermal effects reshape the weak-value condition and influence the emergence of projective behavior across the full measurement crossover.

Finding the host galaxies of stellar-mass compact binary mergers will open a new window for studying their formation histories and measuring key cosmological parameters, such as the Hubble constant. To date, only one merger, GW170817, has had its host galaxy confidently identified through electromagnetic counterpart observations. The large localization volumes from the LIGO-Virgo-KAGRA (LVK) network, combined with the lack of electromagnetic emission for most events, make host identification challenging. However, as the sensitivity of the gravitational-wave (GW) detector network improves, events are becoming increasingly well localized. Furthermore, galaxy luminosity traces mass or star formation rate, and thus correlates with the probability of hosting a merger. Focusing on the most luminous galaxies within the localization volumes of the best-localized GW events, we estimate the corresponding Hubble constant for each galaxy by combining its redshift with the luminosity distance inferred from LVK observations. For the well-localized LVK events \texttt{S250207bg}, \texttt{GW190814}, and \texttt{S250830bp}, we find only $1$, $1$, and $4$ galaxies, respectively, when restricting the analysis to the most luminous $1\%$ of galaxies above $L_{\rm th} \sim 10^{11} h^{-2} L_{\odot}$ in each event's localization volume and adopting a broad $H_0$ prior. The probability of these galaxies being random, and not associated with the GW events, is $29$-$36\%$ across the three events. We encourage further follow-up observations of these candidate host galaxies. We expect this approach to become increasingly powerful in future LVK observing runs, enabling constraints on merger formation histories and measurements of the Hubble constant.

Degenerate quantum eigenspaces can support substantial changes in nodal geometry at fixed energy. We show that, for the two-dimensional isotropic harmonic oscillator, this restructuring is organized by the Hermite-constrained algebraic curve $P_N(x,y)=0$ appearing in every real shell state, $ψ_N=e^{-αr^2/2}P_N(x,y)$. Finite singularities, $P_N=\nabla P_N=0$, and projective degeneracies of the leading homogeneous part identify the strata where topology-changing events can occur. We combine these criteria with entropy diagnostics: the nodal-domain entropy $S_{\mathrm{dom}}$, Cartesian mutual information $I(x;y)$, and the entropic uncertainty sum $S_r+S_p$. The first three shells reveal a hierarchy: $N=1$ only rotates a nodal line; $N=2$ has a conic transition at $b^2=2ac$, sharply detected by $S_{\mathrm{dom}}$ but not by global entropies; and $N=3$ supports cubic close-branch regimes organized by the projective discriminant, with enhanced responses in $S_{\mathrm{dom}}$ and $I(x;y)$. Thus algebraic stratification, rather than spectral ordering, organizes nodal geometry inside a degenerate eigenspace, while entropy diagnostics quantify probability redistribution and correlation. The framework suggests experimentally reconstructible signatures for real-phase Hermite--Gaussian structured light and approximately isotropic trapped motional systems.

Metastable cosmic strings are a common prediction of grand unified theories and act as a source of a gravitational-wave background (GWB) that can explain the 2023 pulsar timing array (PTA) signal. In this paper, we revisit the GWB signal from metastable strings, emphasizing the need to carefully distinguish between two different time scales: (i) t_LB, the time scale of loop breaking because of spontaneous monopole nucleation on closed string loops, and (ii) t_NC, the time scale of network collapse when string segments attached to monopoles begin to enter the Hubble horizon. We discuss under which conditions these two time scales are similar or far apart from each other and illustrate the resulting consequences for the GWB signal. In doing so, we generalize the description of the GWB signal from metastable strings to a three-parameter model in terms of the string tension Gμand the time scales t_LB and t_NC, which allows us to unify the modeling of standard metastable strings with what is known as quasi-stable strings. In the limit of a large t_LB/t_NC ratio, we, moreover, derive a compact analytical expression for the predicted GWB spectrum in excellent agreement with numerical results in the literature. We thus conclude that our new templates for the GWB spectrum from metastable strings can be readily used in the analysis of future PTA data sets.

Quantum key distribution (QKD) theoretically offers information-theoretic security. The prevailing approach is the prepare-and-measure BB84 protocol, which implements QKD using conventional laser rather than single-photon source via the decoy-state method. However, side-channel attacks targeting sources severely threaten system security. Despite extensive efforts, including fully passive scheme, this vulnerability persists even with perfect single-photon source. Here, we propose a source-independent (SI) QKD protocol that resolves all known and unknown source-side attacks without pre-sending entanglement source. Aligning with advances in quantum light sources, our protocol simultaneously doubles the transmission distance while remaining robustness against imperfection of source. Theoretical analysis shows that non-classical light source provides practical security advantages unattainable with conventional laser.

The black hole no-hair theorem is traditionally derived from the uniqueness theorems of general relativity. We show that a quantitative form follows from unitarity together with the standard semiclassical assumptions of horizon causality and interior accessibility. For a semiclassical black hole, we prove that the trace distance between exterior states corresponding to two same-charge infalling states is bounded by $2\sqrt{2\varepsilon}$, where $\varepsilon$ quantifies the diamond norm departure of the interior channel from a perfect isometry which is a quantitative measure of horizon-smoothness violation that upper-bounds $1 - F_I$, where $F_I$ is the interior fidelity capturing how faithfully the infalling state is retained. Inverting this relation yields a trade-off inequality, $\varepsilon \geq D_{\max}^2/8$, between the maximum exterior distinguishability $D_{\max}$ and the degree of horizon smoothness. This establishes that observable exterior quantum hair is quantitatively incompatible with exact horizon smoothness under unitary evolution: any model predicting nonzero exterior hair must violate the equivalence principle at the horizon by a quantifiable amount. Pre-existing entanglement with the infalling system is the only channel for quantum hair compatible with both unitarity and horizon smoothness.

Mid-circuit measurement and classical feed-forward are essential primitives for dynamic-circuit teleportation on superconducting quantum processors. However, the error associated with measurement-conditioned corrective operations remains poorly understood when evaluated with respect to individual measurement branches. In this paper, we present a framework for characterizing feed-forward error in dynamic circuit teleportation without losing valuable information related to its behavior across separate branches. We analyze three approaches to applying measurement-conditioned corrections: (i) physical application, (ii) post-processing adjustments, and (iii) a mitigated physical application which utilizes Bit-Flip Averaging (BFA)-based Probabilistic Readout Error Mitigation (PROM). We experimentally reconstruct branch Choi operators via an entangled reference qubit, and validate our physical-application and post-processing Choi-shadow estimators against full tomography of the branch Choi operators. We perform experiments on two physical qubit layouts which differ greatly in mid-circuit measurement readout error, and observe a reversal in the relative order in branch qualities obtained from the post-processing and PROM mitigation strategies. In one physical layout with higher measurement readout error, the operational feed-forward penalty is relatively modest (approximately 0.02-0.03) and PROM produces higher branch qualities than post-processing for every branch. In a separate layout with lower readout error, the operational feed-forward penalty increases to roughly 0.07, and post-processing exceeds PROM for all branch qualities. Our characterization framework can reveal branch-specific error structure and mitigation behavior that state-of-the-art outcome-averaged analyses fail to expose.

Quantum control with restricted state access is central to near-term quantum devices, where full wave-function information is unavailable. We study this problem through multiqubit disentanglement scheduling from partial observations, where a controller receives only two-qubit reduced density matrices and selects which qubit pair to disentangle at each step. We introduce a modular hybrid quantum--classical policy framework consisting of classical preprocessing, a parameterized quantum circuit as a compact nonlinear latent block, and classical postprocessing for pair-selection probabilities. Benchmarking 4-, 5-, and 6-qubit tasks, we find that preprocessing is the dominant factor governing performance under reduced-state observations, while the quantum module provides a conditional compact representation whose utility depends on the input features and model budget. We further identify a performance--efficiency trade-off across policy families and find that increasing circuit width is generally more useful than increasing depth. These results provide practical design principles for hybrid policies in reduced-information quantum control.

We study the null junction conditions in (re-summed) quasi-topological gravity theories, showing that no null thin shells exist within the realms of standard distributional theory for the pure gravity regular black hole solutions we have analyzed. This implies that the usual derivation of the mass inflation instability, which makes use of null thin shells, is not applicable in these theories. The problem of stability of inner horizons of regular black holes in quasi-topological gravity is hence still open and must be addressed with a more refined analysis, which does not rely on thin shells or the vacuum condition.

We numerically study a formation of near extremal horizons from a gravitational collapse of radially symmetric gravitational waves in $4+1$ dimensions within the framework of pure Einstein gravity with positive cosmological constant. Evolution of a regular initial data with cosmological horizon leads to a formation of a black hole with mass exceeding $99\%$ of the extremal value corresponding to the black hole and cosmological horizons coinciding. We demonstrate how our results fit within the framework of characteristic gluing, and present some evidence that the third law of black hole thermodynamics may not hold in the cosmological context, where the extremality corresponds to the maximal mass of the Schwarzschild black hole in de--Sitter space.

We introduce a fast, one-dimensional Newtonian {reduced model} to capture uniform rotation in cold white dwarfs, encoding centrifugal support as an effective pressure anisotropy. Using $Δ_{\rm rot}(r)=\frac{1}{3}ρ(r)Ω^2 r^2$ derived from the stationary Euler equation with $\langle\sin^2θ\rangle=2/3$, the model incorporates rotation into hydrostatic balance without a two-dimensional solver. Applying the Chandrasekhar degenerate-electron equation of state, we compute interior structures and global sequences for $ ρ_c \in [10^6, 10^{11}]~{\rm g\,cm^{-3}} $ with rotation proxies $f \le 0.35$, finding monotonic increases in limiting mass and radius, with a percent-level mass gain at $f = 0.35$. We quantify applicability using sub-Keplerian diagnostics evaluated on the rotating configurations, $\max(Ω/Ω_K)$ and $\max(ε)$, together with a bulk-interior smallness measure $A_{10^{-2}}\equiv \max_{p_r/p_c\ge 10^{-2}}(Δ_{\rm rot}/p_r)$. Within the scanned domain these diagnostics remain below unity. The model is therefore best viewed as a reduced Newtonian benchmark for slow-to-moderate rotation, not as a replacement for fully axisymmetric calculations of rotating stars.

In recent years, continuous-variable quantum key distribution (CV-QKD) has become a promising paradigm for enabling secure communication among multiple end users sharing the same telecommunication backbone. CV-QKD with reverse reconciliation naturally enables scalability from conventional point-to-point links to quantum access networks based on passive quantum broadcasting channels. Here, we report an experimental demonstration on an active $1:4$ multi-user CV quantum network (QN) in the finite-size regime. With $1.25\cdot10^9$ coherent states exchanged on each $11\text{km}$ quantum channel, the highest performance for secret key generation totaling $1.9\cdot10^{-1}$ bits/channel use. Furthermore, we investigate adaptable CV-QN protocols that comprehensively allow network operation in various security and key rates requirements of individual users. The results establish the practical security of CV-QN compatible with existing telecommunication for broad deployment, and allowing additional degree of freedom for connected end users in existing infrastructures.

A scheme is proposed for realizing simultaneous negative permittivity and negative permeability based on quantum coherence in a four-level dense atomic system here.Under some parametric conditions the system shows that simultaneous negative permittivity and negative permeability(i.e.Left handness) can be achieved in a wider frequency band because of quantum coherence.And the novelty properties of gain and dispersion near the resonance frequency may have some potential applications.

We derive a class of inner-extremal regular black hole solutions characterized by a degenerate inner horizon. These geometries arise as polymerized vacuum configurations inspired by loop quantum gravity and constitute effective quantum-gravity solutions that admit a Birkhoff-type theorem, rendering them unique within the considered framework. We show that such inner-extremal horizon configurations exist only for a finely tuned value of the mass determined by the parameters of the theory. Building on this construction, together with the corresponding non-degenerate regular black hole solutions, we perform a generic analysis of the mass inflation phenomenon in four-dimensional spacetimes using a colliding null-shell setup near the inner horizon. We identify the conditions under which mass inflation becomes significant and examine how the presence of a minimal length scale affects this behavior, with particular emphasis on the case where such a scale is motivated by loop quantum gravity. Finally, we comment on the stability of these configurations under the null-shell perturbations considered in our analysis.

Stoquasticity, originating in sign-problem-free physical systems, gives rise to $\sf StoqMA$, introduced by Bravyi, Bessen, and Terhal (2006), a quantum-inspired intermediate class between $\sf MA$ and $\sf AM$. Unentanglement similarly gives rise to ${\sf QMA}(2)$, introduced by Kobayashi, Matsumoto, and Yamakami (CJTCS 2009), which generalizes $\sf QMA$ to two unentangled proofs and still has only the trivial $\sf NEXP$ upper bound. In this work, we initiate a systematic study of the power of unentanglement without destructive interference via ${\sf StoqMA}(2)$, the class of unentangled stoquastic Merlin-Arthur proof systems. Although $\sf StoqMA$ is semi-quantum and may collapse to $\sf MA$, ${\sf StoqMA}(2)$ turns out to be surprisingly powerful. We establish the following results: - ${\sf NP} \subseteq {\sf StoqMA}(2)$ with $\widetilde{O}(\sqrt{n})$-qubit proofs and completeness error $2^{-{\rm polylog}(n)}$. Conversely, ${\sf StoqMA}(2) \subseteq {\sf EXP}$ via the Sum-of-Squares algorithm of Barak, Kelner, and Steurer (STOC 2014); with our lower bound, our refined analysis yields the optimality of this algorithm under ETH. - ${\sf StoqMA}(2)_1 \subseteq {\sf PSPACE}$, and the containment holds with completeness error $2^{-2^{{\rm poly}(n)}}$. - ${\sf PreciseStoqMA}(2)$, a variant of ${\sf StoqMA}(2)$ with exponentially small promise gap, cannot achieve perfect completeness unless ${\sf EXP}={\sf NEXP}$. In contrast, ${\sf PreciseStoqMA}$ achieves perfect completeness, since ${\sf PSPACE} \subseteq {\sf PreciseStoqMA}_1$. - When the completeness error is negligible, ${\sf StoqMA}(k) = {\sf StoqMA}(2)$ for $k\geq 2$. Our lower bounds are obtained by stoquastizing the short-proof ${\sf QMA}(2)$ protocols via distribution testing techniques. Our upper bounds for the nearly perfect completeness case are proved via our new rectangular closure testing framework.

We present a systematic derivation of regular black hole solutions - and their horizonless counterparts - that achieve regularization via an anti-de Sitter core. These geometries emerge as polymerized vacuum solutions inspired by loop quantum gravity, constituting effective quantum gravity configurations that admit a Birkhoff-type theorem and are uniquely determined by their mass. Using an auxiliary relational dust clock, together with the absence of gravitational waves in spherical symmetry, we exploit the structural ultralocality of the system to decompose the dynamics into independent shell degrees of freedom. The dust field acts as a reference clock for deparameterization and does not source the vacuum geometries considered here. These assumptions tightly constrain the Lemaitre-Tolman-Bondi shell Hamiltonian to a factorized form and the static vacuum metric function to a universal expression. We examine the possibility of a bounce and analyze how its presence is encoded, or missed, in finite-order effective truncations of the full model. The procedure for deriving the explicit physical Hamiltonian is described for a generic case before specializing to a specific model of interest. Finally, we construct a four-dimensional covariant completion of the spatially covariant Lagrangian, showing that it belongs to the class of generalized extended mimetic gravity models.

We show that, under additivity, the maximal von Neumann algebra extension of $\mathcal{A}(O)$ inside $B(\mathcal{H})$ whose inner automorphisms are non-signalling with respect to all spacelike-separated regions is $\mathcal{A}(O')'$. Consequently, $\mathcal{A}(O)$ is maximal with respect to this property if and only if essential duality holds. The proof is purely algebraic. When essential duality fails, we construct a proper extension all of whose inner automorphisms, and more generally all normal completely positive maps admitting Kraus operators in the algebra, are non-signalling. Under essential duality, any proper extension necessarily admits a signalling operation. An entropic formulation using Araki relative entropy provides a quantitative diagnostic of signalling, though it is not used in the proof. Additional structural results include the wedge-intersection identity $\mathcal{A}(O')' = \bigcap_{W \supset O}\mathcal{A}(W)$ and equivalent characterisations of essential duality. These results identify essential duality as an operational maximality condition within the given representation.

Fractal structures naturally emerge in quantum systems whose initial states exhibit spatial discontinuities, a phenomenon first identified by Berry in the paradigmatic case of a particle confined in an infinite potential well. While previous analyses of quantum fractals have mainly relied on spectral decompositions and geometric scaling arguments, their quantitative characterization often depends on scale choices and truncation effects. Here we present a wavelet-based multiresolution framework that enables a direct and assumption-free quantification of quantum fractality. Fractal dimensions are extracted from the scale-dependent distribution of wavelet energies, without invoking prior power-law hypotheses. The method is applied to space and time quantum fractals arising in confined dynamics, as well as to dynamical curves generated by the associated quantum probability flux. These flux-driven trajectories provide a natural space--time parametrization of the underlying fractal structure and yield scaling properties fully consistent with Berry's predictions for space--time fractals. The resulting fractal dimensions are shown to be robust with respect to the choice of wavelet family, numerical cutoffs, and system parameters. Beyond validating earlier conjectures, the present framework offers a unified and computationally efficient tool for the multiscale analysis of quantum fractality in confined and interference-driven quantum dynamics. That is, it provides an operational, scale-adaptive criterion that unifies the characterization of space, time, and space--time quantum fractals within a single, hypothesis-free approach.

Characterizing quantum systems by learning their underlying Hamiltonians is a central task in quantum information science. While recent algorithmic advances have achieved near-optimal efficiency in this task, they critically rely on accessing arbitrarily short-time dynamics. This reliance poses severe experimental challenges due to finite control bandwidth and transient pulse errors. In this work, we demonstrate that Heisenberg-limited Hamiltonian learning can be achieved without short-time control. We introduce a framework in which every query to the unknown dynamics has duration at least a prescribed minimum time $T$, and show that this restriction does not preclude Heisenberg-limited scaling. The key ingredient is a method for emulating the continuous quantum control required by iterative learning algorithms using only such lower-bounded evolution times. This reduces the learning task to sparse pure-state tomography. Notably, for logarithmically sparse Hamiltonians, our algorithm achieves the information-theoretically optimal $1/\varepsilon$ scaling in total evolution time for any arbitrary constant minimum evolution time $T$. For many-body (polynomially sparse) systems, we uncover a rigorous quantitative tradeoff, showing that the minimum required evolution time can be significantly relaxed from the standard limit at a polynomial cost in total evolution time. Our results affirmatively resolve a prominent open problem in the field and reveal that high-bandwidth, ultra-short pulses are not fundamentally necessary for optimal quantum learning.

Practical applicability of quantum optimisation on near term devices is constrained by limited qubit counts and hardware noise, which restricts the scalability of quantum optimisation algorithms for combinatorial problems. The simulation of large quantum circuits is also difficult and constrained by memory requirement. The Hamiltonian Auto Decomposition Optimisation Framework (HADOF) addresses this by decomposing large QUBOs into smaller subproblems that can be solved iteratively on quantum or classical backends. This allows the scalability of quantum QUBO algorithms beyond device limits, as well as their simulation on classical devices. In this research, we extend the evaluation of HADOF by benchmarking on real IBM QPUs across sequential, single-QPU parallel, and multi-QPU parallel execution modes, advancing toward High Performance Quantum (HPQ) computing for combinatorial optimisation problems. Experimental results on IBM quantum hardware demonstrate up to 3-4x reduction in wall clock time when utilising four QPUs compared to sequential execution baseline, while maintaining comparable solution quality. Notably, even single QPU execution benefits from parallelised job orchestration and execution, yielding up to 3x speedup. Simulated results predict over 5x speed-up in parallel execution mode. We further validate the practical applicability of the approach on real world genome assembly instances, showing that both sequential and parallel HADOF variants achieve competitive accuracy while significantly improving time to solution. These results highlight the importance of parallelism at both the algorithmic and system levels, positioning HADOF as a viable pathway toward scalable quantum optimisation.

We study hypergeometric functions of nilpotent operators in finite-dimensional settings, motivated by the algebraic structure of exceptional points in non-Hermitian quantum mechanics. Our starting point is the following exact result: if N is a nilpotent operator of index m+1 in an associative algebra over C, then every generalized hypergeometric function pFq evaluated at N reduces to a finite polynomial in N of degree at most m, without any analytic convergence requirement. This "functional collapse" is distinct from the classical parameter-termination mechanism and arises purely from the nilpotent structure of the argument. The main result is a "nilpotent depth criterion" (Theorem 2): if the first non-constant coefficient of a formal series F appears in degree r >= 1, then the nilpotent part F(N) - F(0)I has nilpotency index bounded above by ceil((m+1)/r). We apply this criterion to Hamiltonians at exceptional points, where H = lambda I + N with N^{m+1} = 0. Theorem 3 establishes that a function F analytic at lambda reduces the Jordan depth of the exceptional point from m+1 to at most ceil((m+1)/r), where r is the contact order of F at lambda. As consequences: the time evolution operator e^{tH} preserves the full Jordan depth for all t != 0; a function with a zero of order m+1 at lambda annihilates the entire Jordan structure; and the order of the pole of the modified resolvent is reduced from m+1 to at most m+1-r. Results are illustrated with explicit 3x3 Jordan block computations for 1F1, 2F1, and the time evolution operator, confirming sharpness of the bounds.

We propose a method for constructing multi-qubit entangled quantum states representing weighted tripartite graphs. An expression for the entanglement distance for multi-qubit states corresponding to arbitrary tripartite graph structures is obtained. The entanglement of a qubit with the rest of the system in a quantum graph state is determined by the weights of the edges in the closed neighborhood of the corresponding vertex and by its degree with respect to other sets. We also calculate quantum correlators in the general case of tripartite quantum graph states. We establish a relationship between these quantum properties and the structural properties of the corresponding tripartite graphs, including the number of non-overlapping neighbors, the number of common neighbors of the corresponding vertices, and the number of 4-cycles. As an illustrative example, we consider a tripartite graph forming a triangle and compute the entanglement distance using quantum simulations on the AerSimulator with noise models. The numerical results are consistent with the theoretical predictions. The obtained results demonstrate that quantum graph states provide an effective framework for studying structural properties of tripartite graphs. They open up the possibility of investigating such properties using quantum programming. It is worth highlighting that tripartite graphs have applications in solving practical problems such as resource allocation, scheduling, and database and hypergraph modeling.

Accurately characterizing multipartite entangled states is a critical challenge in quantum information processing. In this work, we focus on applying compressed sensing techniques to efficiently estimate the fidelity of Greenberger-Horne-Zeilinger (GHZ) states. By exploiting the inherent sparsity of these states, our compressed sensing protocol drastically reduces the measurement overhead traditionally required for state verification while maintaining high accuracy. To evaluate the practical performance of this approach, we test the protocol on GHZ states using both quantum simulators and Quantinuum's trapped-ion hardware. Furthermore, we implement error detection techniques during our hardware evaluations, demonstrating the robustness and viability of compressed sensing for fidelity estimation in noisy experimental environments.

We study square-base Calderbank--Shor--Steane (CSS) hypergraph-product codes as a finite-length class for regular high-girth quantum low-density parity-check (LDPC) design. For base matrices of small column weight, we give checkable conditions for regularity, rank deficiency, and short-cycle exclusion, and we present explicit column-weight-three and column-weight-four examples with Tanner girth 6 and 8. We also analyze circulant permutation matrix (CPM) lifts of this class. Using the standard voltage-sum criterion, we identify orthogonality-forced Tanner 8-cycles and show that CPM lifting cannot raise the Tanner girth beyond 8 when these cycles are present. As a representative finite-length instance, a randomized CPM lift of the girth-8 base construction gives a $[[28800,62]]$ girth-8 $(3,6)$-regular CSS-LDPC code. Under degeneracy-aware belief-propagation decoding with optional ordered-statistics-decoding-lite post-processing, this code produced zero decoding failures in $2.993\times 10^8$ independent trials at depolarizing probability $p=0.1402$; the Wilson 95% upper confidence bound is $1.28\times 10^{-8}$.

We investigate spin-induced scalarization of Kerr black holes in an Einstein-scalar-Gauss-Bonnet (EsGB) model that does not admit a linear tachyonic instability of the scalar-free solution. The scalarization mechanism is therefore genuinely nonlinear. We first analyze the decoupled scalar dynamics on fixed Kerr backgrounds and show that sufficiently rapid rotation modifies the Gauss-Bonnet invariant such that a negative near-horizon region develops near the poles. This region provides a geometric trapping mechanism for nonlinear scalar growth, which becomes effective above a threshold spin $χ=0.5$. We then construct stationary scalarized black hole solutions with full backreaction and determine their domain of existence. We find that the solutions occupy a finite low-mass high-spin wedge in the spin-mass plane. This is in contrast to spin-induced spontaneous scalarization, where the scalarized solutions form a narrow band. In this wedge, toward the high-spin end, the scalar hair becomes stronger, and the solutions approach a near-extremal regime, while toward the low-spin boundary, the scalar field is strongly suppressed and approaches a weak-hair limit as $χ\to 0.5$.

In this paper, we employ the Comisso-Asenjo magnetic reconnection (MR) mechanism to investigate energy extraction from a rapidly rotating five-dimensional Kerr black hole (BH) with single- and two-rotation configurations. We analyze the efficiency, phase-space structure of accelerated and decelerated plasma energies, and the extracted power as functions of the spin parameter, reconnection location, plasma magnetization, and magnetic field orientation. We show that MR significantly enhances energy extraction from a five-dimensional BH with a single rotation and that the extraction efficiency is higher in the single rotation configuration than in the two-rotation case. We also evaluate the extraction rate and compare it with the Blandford-Znajek (BZ) mechanism, showing that the extracted power can exceed that of the BZ process in the single-rotation configuration. Our analysis shows that MR can significantly improve energy extraction in five-dimensional Kerr BHs with a single rotation, making them promising candidates for powering high-energy astrophysical phenomena.

This paper addresses the problem of finding the densest $k$-vertex subgraph in an arbitrary graph. This problem is NP-hard and has important applications in social network analysis, fraud detection, recommendation systems, and bioinformatics. We propose two quantum approaches to solve this problem: reduction to Quadratic Unconstrained Binary Optimization (QUBO) and using Grover's quantum search algorithm. For the latter approach, we present an explicit gate-based oracle circuit utilizing Dicke states and Quantum Fourier Transform for edge counting. Numerical simulations demonstrate a quadratic speedup over classical Brute-force search.

Cosmology has entered a precision era in which discrepancies between independent datasets, most notably the $H_0$ and $S_8$ tensions, have become robust and statistically significant. These tensions are no longer isolated anomalies but increasingly appear as global consistency constraints on the underlying cosmological model, defining what we will refer to here as a \emph{consistency triangle} of background expansion ($H_0$), structure-growth amplitude ($S_8$), and the redshift-dependence of growth - summarised by the growth index $γ$, or equivalently the shape of $fσ_8(z)$. The third vertex is non-trivial because in modified-gravity scenarios with a redshift-dependent effective gravitational coupling, growth amplitude and growth shape evolve independently, breaking the rigid coupling characteristic of $Λ$CDM. In this work, we use $f(Q)$ gravity as a test case for this emerging paradigm. By drawing on a focused set of recent Bayesian and dynamical-system analyses of the three best-studied functional families - power-law, exponential, and logarithmic - we show that while $f(Q)$ models can alleviate individual tensions, the requirement of simultaneous consistency across $H_0$, $S_8$ and the growth index severely restricts the viable parameter space. A bulk-viscous extension is then briefly examined as a representative illustration of how additional matter-sector freedom is constrained by the same consistency requirement. Our reading of the current literature supports the view that cosmological tensions should be interpreted as global consistency conditions, and that viable extensions of $Λ$CDM must satisfy this multi-probe constraint \cite{CosmoVerse,DiValentino2025Corfu}. Within this framework, only a restricted subset of $f(Q)$ models remains competitive.

We propose that the size of the universe and its rate of expansion cannot be simultaneously specified with arbitrary precision, a quantum mechanical statement encoded in a deformed commutation relation for the scale factor. The deformation modifies the Friedmann equation by adding a geometric correction to the expansion rate, and the sign and magnitude of a single free exponent determine the cosmological behavior. When the exponent is positive, the model predicts late-time dark energy with $w > -1$, testable with current and next-generation surveys. When the exponent is sufficiently negative, the same deformation produces a non-singular classical bounce that resolves the Big Bang singularity. The model introduces no new particles or fields and preserves a scale-invariant primordial power spectrum. The deformation has a natural interpretation as a horizon-scale phenomenon, with the cosmological horizon, and not the Planck length, setting its characteristic scale. The late-universe regime is then its generic application, with the expansion history as the primary observable signature. Cosmic acceleration may be the macroscopic imprint of quantum gravity at the cosmological horizon.

Photon counting is a cornerstone of quantum optics. Here, we demonstrate precisely counting from 0 to over 9000 photons, beating the Poisson noise limit by at least $4.1~\mathrm{dB}$ across this range. We achieve sub-single-photon precision up to 276 photons per pulse. To do so, we multiplex eight intrinsically photon-number-resolving superconducting nanowire single-photon detectors across 128 temporal modes. We use a model-informed characterization of each of the 1024 detection bins, for optimal precision. We perform quantum detector tomography to reconstruct the positive operator valued measures (POVMs) of the complete device, which consists of $1.38\cdot10^8$ matrix elements. At the repetition rate of our experiment of $80~\mathrm{kHz}$, we can precisely count photons corresponding to an optical power of approximately $71~\mathrm{pW}$, bridging the gap from single-photon measurements to high-sensitivity optical power meters. A photon-number-resolving detector of this size, and the tools used to analyze it, will become increasingly important to characterize large quantum states, as well as tasks in precision metrology and optical power standards.

Dipole-radiation-like deviations from general relativity are most prominent during the early inspiral of compact binaries, making space-ground multiband observations a potential probe of such effects. In the same regime, orbital eccentricity can leave a significant imprint on the waveform and is therefore essential for robust dipole-radiation constraints. For the first time we present a multiband Bayesian inference pipeline for stellar-mass binary black holes that simultaneously incorporates eccentricity and a theory-agnostic dipole-radiation correction. We find strong degeneracies among the dipole parameter, chirp mass, and eccentricity, which substantially weaken the inferred dipole constraints when eccentricity is included. Even so, for a GW231123-like source, one year of TianQin or LISA observation with ground-informed priors from a next-generation detector network can still constrain the dipole parameter to $|b|\lesssim\mathcal{O}(10^{-7})$ under inference with noisy data. Our results show that multiband binary black hole observations provide a promising and distinct channel for testing theory-agnostic dipole radiation, while also highlighting the need for more complete waveform modeling in future precision tests of gravity.

A double-transmon coupler (DTC) enables a fast, high-fidelity CZ gate between two highly detuned, fixed-frequency transmon qubits. Moreover, a recently proposed capacitively shunted DTC (CSDTC) realizes a small residual ZZ interaction over a wide flux-bias range around zero flux, eliminating the necessity of static flux biasing while maintaining high CZ-gate fidelity. However, CZ gates with the DTC and CSDTC require baseband flux pulses with large amplitudes, which are vulnerable to pulse distortion and decoherence due to large qubit-coupler hybridization. To address these issues, we experimentally demonstrate a parametrically driven iSWAP gate operated at zero flux bias between highly detuned, fixed-frequency transmon qubits coupled through a CSDTC. Using a simple flux-drive waveform without predistortion, we realize an average gate fidelity of 99.92(2)% at a total gate time of 112 ns. The observed high-fidelity performance is consistent with small qubit-coupler hybridization and small effective ZZ interaction during the gate. Our numerical simulations reproduce the experimentally observed iSWAP interaction rate and effective ZZ interaction, demonstrating the applicability of the theoretical model not only to spectral information but also to time-domain dynamics such as gate operations. These results boost further progress in the research of superconducting quantum computers.

In multi-core quantum computing architectures, waveguide-mediated interconnects are essential for facilitating fast, high-fidelity quantum state transfer between qubits located in different chips. However, optimizing these systems typically relies on computationally expensive numerical simulations that offer limited physical insight. In this work, we derive exact analytical expressions for the state transfer dynamics of a two-qubit system coupled via a waveguide, modeled through a Jaynes-Cummings Hamiltonian and the Lindblad master equation. We apply the Monte Carlo wave-function method and obtain a closed-form solution for qubit occupation probabilities that accounts for both detuning and dissipative losses. Our analytical framework provides a significant computational speedup compared to standard numerical solvers, enabling large-scale parameter sweeps while maintaining high precision in both fidelity and latency predictions. Furthermore, the model reveals and explains systematic low-fidelity regions arising from destructive interference between internal oscillations and detuning-induced envelopes, which are phenomena that are difficult to characterize through numerical means alone. Finally, we propose a simplified latency model and an efficiency-based function to enable rapid identification of optimal operating points. This analytical approach provides a robust foundation for the design and optimization of interconnects in multi-core quantum processors.

We present a comprehensive theoretical investigation of hyperfine-resolved excitation and detection of the low-energy isomeric state of $^{229}$Th in trapped $^{229}\mathrm{Th}^{3+}$ ions. Using a quantum master equation approach, we analyze the dependence of the isomeric population on laser linewidth, detuning, and irradiation time, showing that their proper matching is essential for efficient excitation. We further propose two nuclear-state detection schemes based on three hyperfine-resolved electronic fluorescence channels at 690, 984, and 1088 nm. Our analysis shows that the 690-nm and 984-nm scheme yields detectable photon rates on the order of $10^4~\mathrm{s}^{-1}$ per ion for each wavelength, whereas the 1088-nm scheme achieves a higher rate on the order of $10^5~\mathrm{s}^{-1}$ per ion. By quantifying the trade-off between irradiation time and scan-step size, we show that the nuclear transition can be located within one month for a 100-MHz uncertainty using currently available vacuum-ultraviolet laser technology. These results provide practical guidance for trapped-ion $^{229}\mathrm{Th}$ spectroscopy and the development of nuclear clocks.

We present a transient quantum sensing framework for cavity-magnon systems that circumvents the inevitable loss of initial-state quantum properties plaguing conventional steady-state protocols. Explicitly incorporating finite-time dynamics and adopting an engineered steady state as the initial condition, we derive the exact transient noise spectrum. We show that residual initial quantum correlations alone can drastically enhance the short-time signal-to-noise ratio (SNR) beyond that achievable with unsqueezed steady-state schemes. Through analysis of the transient spectral density and joint measurements of orthogonal cavity quadratures, we realize crosstalk-free reconstruction of all three magnetic field components, enabling orientation of magnetic signals. In the long-time limit, our theory yields a closed-form stationary noise spectrum and uncovers a resonance condition $g_{am}=\sqrt{κ_aκ_m}/2$, where cavity field quantum noise is fully canceled without requiring strong coherent coupling. Away from this resonance, injected squeezing further suppresses cavity induced noise and broadens the detection bandwidth. Extending the framework to an array of $N$ yttrium iron garnet (YIG) spheres generates a collective bright mode, with magnon-probe noise scaling as $1/N$. Our results establish a unified route to scalable, high precision, multidimensional quantum magnetometry using cavity-magnon platforms.

Within the framework of the post-Newtonian $2\frac12$ approximation theory, a kinetic theory for relativistic gases in the presence of gravitational fields is developed. The Boltzmann equation and the equilibrium Maxwell-Jüttner distribution function are determined up to $1/c^7$--order, which are used to calculate the components of the particle four-flow and energy-momentum tensor and to find the Eulerian hydrodynamic equations for the mass, mass-energy, and momentum densities in the $2\frac12$--post-Newtonian approximation. The energy conservation law follows from the hydrodynamic equation for the total energy density, which is a combination of the hydrodynamic equations for the mass and the mass-energy densities.

The Paneitz operator is a dimension-4 conformally invariant fourth-order differential operator that has recently attracted attention for possible cancellations of the vacuum energy. We show that, in four dimensions, the Paneitz operator acting on a scalar field falls within the class of extended mimetic gravity theories. Thus, it exhibits the usual instabilities of mimetic gravity. Assuming such instabilities are cured by higher derivative terms, we derive constraints on the Paneitz operator from a modified propagation speed of gravitational waves, after including the Einstein-Hilbert action in the mimetic gravity formulation.

Quantum universal invariants of a Gaussian state's covariance matrix, which can be derived from intensity correlation moments, have been adopted to characterize the entanglement properties of Gaussian states via the positive partial transpose criterion, also known as the Peres-Horodecki separability criterion. Such intensity correlation moments enable the extraction of information about the covariance matrix without the need for a coherent local oscillator. Here, we experimentally detect the entanglement properties of multimode Gaussian states using high-order\,(up to sixth-order) intensity correlation moments. These multimode Gaussian states are prepared via spontaneous and cascaded parametric down-conversion pumped by a high-peak-energy pulsed laser. Their intensity correlation moments are measured using a pseudo-photon-number-resolving detector constructed through spatial multiplexing of 32 threshold superconducting nanowire single photon detectors. This method is successfully demonstrated for two-mode and three-mode Gaussian states and can be extended to $N$-mode Gaussian states with $N>3$.

We prove that the Lorentz--FitzGerald contraction is the unique deformation of a resonant cavity moving through a mechanical wave medium that preserves spherical-harmonic phase closure. For a cavity moving at speed $v = βc$ through a medium supporting nondispersive wave propagation at speed $c$, the round-trip phase of an internal ray at angle $θ$ to the motion depends on the boundary radius $r(θ)$ according to $Φ(θ) = 2k\,r(θ)\sqrt{1-β^2\sin^2θ}/(1-β^2)$. Requiring $Φ(θ)$ to be independent of $θ$ -- the necessary condition for retaining a spherical-harmonic eigenstructure -- uniquely fixes the Lorentzian aspect ratio \[ \frac{a_\parallel}{a_\perp} = \frac{1}γ = \sqrt{1-β^2}. \] Substituting this unique boundary into the round-trip time yields the resonant period dilation $T = γT_0$, without additional assumptions. Both results -- contraction and dilation -- follow from a single mechanical constraint: preservation of eigenstructure under motion. This is the missing uniqueness theorem of the constructive relativity program initiated by FitzGerald, Lorentz, and Heaviside: the proof that Lorentzian kinematics are not merely consistent with, but uniquely required by, phase closure in a mechanical wave medium.

We apply the extended Nikiforov-Uvarov method to the non-relativistic limit of the Dirac equation with a Coulomb potential in spaces of constant curvature. In this case, the radial equation reduces to the Heun equation, and the extended Nikiforov-Uvarov method easily yields a quantization condition which leads to necessary condition under which the resulting Heun equation can have polynomial solutions. The energy spectrum implied by the quantization condition is virtually identical to the spectrum of a spinless particle obtained using the Schrödinger equation, except for the absence of the ``geometric potential", confirming the non-commutativity of the naive non-relativistic limit with the ``squaring" of the Dirac equation, first discovered on curved surfaces. However, the necessary conditions for the existence of polynomial solutions cannot be met, and this fact undermines the reliability of the results obtained. This circumstance forces us to conclude that the extended Nikiforov-Uvarov method has limited, if any, value when considering similar problems in quantum mechanics.

Imaginary-time evolution is a standard primitive for ground-state preparation but is nonunitary, precluding direct quantum implementation. We develop Finite Imaginary-Time Evolution (FinITE), a finite-beta construction for diagonal Pauli-Z cost Hamiltonians arising from polynomial unconstrained binary optimization (PUBO) instances, including QUBO and HUBO cases. FinITE uses the linear-combination-of-unitaries (LCU) framework to implement a scaled imaginary-time propagator. The commuting Pauli-Z structure makes termwise block-encodings compose without product-formula error, and higher-order Pauli-Z terms are handled directly without quadratization. The structure yields an exact finite-beta identity between the LCU success probability and the ground-subspace fidelity. Combined with a gap-based fidelity lower bound, the identity yields a closed-form sufficient imaginary-time threshold beta-star for a chosen target fidelity. The threshold depends on estimates of the spectral gap and the initial ground-subspace overlap. Because the LCU success event is flagged by a known ancilla outcome, we integrate fixed-point amplitude amplification with an explicit query-complexity bound. Statevector simulations verify the identity on a five-vertex MaxCut (QUBO) and an eight-qubit cubic HUBO instance, and shot-based simulations on the MaxCut instance illustrate the predicted finite-beta threshold and amplification procedure.

Tracing out a Galilean-invariant Caldeira-Leggett environment breaks Galilean boost covariance of the reduced dynamics, while spatial translations and rotations survive intact. An operator-level analysis of the exact Hu-Paz-Zhang master equation localizes the violation entirely in the dissipative anticommutator term, scaling with the damping coefficient $Γ(t)f(t)$. The fluctuation-dissipation theorem ties this coefficient to the absorptive bath response that drives equilibrium momentum diffusion, so for any non-trivial bath spectral density bilinear-coupled Galilean invariance, the fluctuation-dissipation theorem, and reduced boost covariance cannot hold simultaneously. The stochastic decomposition of the influence functional extends the mechanism beyond the quadratic regime. The dimensionless ratio $\hbarγ/k_\mathrm{B} T$ delineates the crossover: cold atoms in dissipative optical lattices and ultracold molecules sit at its edge. Parametric driving offers a one-directional escape: the squeezing rate that protects nonequilibrium entanglement above the standard quantum limit also suppresses boost-breaking over a driving cycle.

We demonstrate exponential quantum speedup for a restricted-Hamming-weight version of Simon's problem on present-day superconducting quantum processors by introducing a hardware-aware compilation strategy that compiles the quantum part of each Simon query circuit to constant depth. The resulting compiled circuits have $O(1)$ depth and linear connectivity, map directly onto common device layouts, and avoid additional routing and SWAP overhead. Implemented on IBM's $156$-qubit Boston and $120$-qubit Miami processors, the resulting circuits achieve sufficiently high fidelity to exhibit algorithmic quantum speedup without error suppression. Using the number-of-queries-to-solution metric, we observe exponential speedup over the classical lower bound across the full Hamming-weight range studied on Boston and across low-to-intermediate Hamming weights on Miami; at higher Hamming weights on Miami, we still observe polynomial speedup. The same construction also reaches a regime where the original Simon problem is recovered for the problem sizes studied. These results show that careful hardware-aware compilation can make exponential quantum speedup experimentally accessible for a canonical hidden-subgroup problem in the NISQ regime.

Most galaxies have supermassive black holes (SMBH) at their centres, surrounded by stars with binary systems also present in this environment. We use two schemes - post-Newtonian (PN) and a scalar perturbation to a background metric to numerically solve the three-body problem of a binary with a SMBH. We test three different PN formulations for the PN scheme: The Einstein-Infeld-Hoffman equation, pair-wise implementation of two-body PN-terms for three bodies and the Arnowitt-Deser-Misner Hamiltonian. We compare these approaches for one million solar mass and one billion solar mass black holes, and find a statistical match between the two approximations for stellar mass binary interacting with a million solar mass black hole. We also perform a statistical study for encounters with this black hole, and find that the higher order PN formulation matches with metric-with-perturbation scheme. However, we find a decrease in separation of the binary, and eccentricity variations between different schemes around the billion solar mass black hole. This behaviour is not present if binary has a large separation or is further away from the black hole due to decreased general-relativistic effects. We find that the pair-wise PN method results in a decrease in separation at pericentre in all test cases irrespective of the distance from the black hole or mass of the black hole, making this the least reliable method for solving this problem. Our work highlights the need for caution when interpreting the results in different formulations around SMBHs. This also shows that when understanding extreme mass ratio inspirals (EMRIs) using simulations, one should beware as the binary gets closer to the black hole.

Complex quantum systems are often multiscale in nature with strong interactions between different scales. We present a novel idea: iteratively suppressing, rather than tracing out, the fast, high-energy degrees of freedom in strongly correlated quantum systems with multiple energy scales in a non-perturbative way, termed nonadiabatic renormalization group. This leads to a quantum geometric structure of a nested fiber bundle, in which each fiber of a layer is itself a fiber bundle of the next layer. The nonadiabatic renormalization group brings a new type of tensor network states that shares physical legs among ''sites'' and encodes quantum entanglement beyond conventional matrix product states. We demonstrate how to apply the nonadiabatic renormalization group to different types of problems, including an interacting boson model and ab initio quantum chemistry with interacting electrons.

Inspired by Mashhoon's framework connecting black hole quasi-normal modes (QNMs) to bound-state resonances in inverted potentials, V$\ddot{\text{o}}$lkel's recent numerical analysis of asymptotically flat Schwarzschild black holes revealed a counterintuitive phenomenon: highly excited bound states rapidly delocalize, become extremely weakly bound, and exhibit wavefunctions highly sensitive to far-field perturbations. This challenges the conventional interpretation of QNMs as localized excitations of the light-ring region. To analytically explain this phenomenon and extend the investigation to Schwarzschild-de Sitter (SdS) black holes, we derive the characteristic equation for excited bound-state resonances in SdS spacetime and obtain compact closed-form analytical expressions for their resonance energies. In the $Λ\rightarrow 0$ limit, our SdS-derived spectrum aligns perfectly with recent results for Schwarzschild black holes. We analytically demonstrate that the rapid and infinite delocalization of highly excited resonances is a universal feature of asymptotically flat Schwarzschild systems. More significantly, we prove that SdS black holes support only a finite number of bound-state resonance levels -- in sharp contrast to the infinite spectrum of the asymptotically flat case. This finiteness implies an upper bound on the oscillatory domain of the resonance eigenfunctions in SdS geometries, thereby preventing infinite delocalization and offering a fundamental distinction in the resonance structure of black holes in different asymptotic backgrounds.

Understanding quantum phases and phase transitions in the presence of symmetries is a central objective of quantum many-body physics. A powerful modern paradigm for investigating this problem is topological holography, which relates symmetries in $k$ dimensions to "bulk" topological orders in $(k+1)$ dimensions. While conceptually profound, most existing bulk construction methods rely on sophisticated mathematical formalisms and can be difficult to apply to certain symmetry types. In this work, we propose a physically intuitive and versatile method, termed the layered gauging construction, to systematically generate $(k+1)$-dimensional (liquid or fracton) topological orders from $k$-dimensional generalized symmetries. Roughly speaking, the prescription is to stack many layers of $k$-dimensional quantum systems with certain symmetries into a $(k+1)$-dimensional pile, and then sequentially gauge a diagonal symmetry acting on each nearest-neighbor pair of layers. The detailed procedure depends on the specific symmetry types. We have successfully implemented the method in a number of examples in different spatial dimensions, with symmetries that are conventional, higher-form, subsystem, anomalous, nonabelian, or noninvertible. We hence conjecture the method to be very general. For example, from the subsystem symmetry of the $2d$ plaquette Ising model, we derive the X-cube model and also an anisotropic fracton topological order. Additionally, starting from an anomalous $\mathbb Z_2$ symmetry in $1d$, we construct a new square lattice model realizing the double semion topological order.

Fix a finite dimension $d \geq 2$ and a fixed rank-1 PVM $M=\{|e_1\rangle\langle e_1|,\ldots,|e_d\rangle\langle e_d|\}$ on ${\bf C}^d$. Let $P_M:\mathbb{CP}^{d-1}\toΔ^{d-1}$ be a readout map on pure states. We prove that three primitives force the Born rule for this fixed measurement: (i) square-root regularity of $R_M=\sqrt{P_M}$ along Fubini-Study geodesics, (ii) the universal readout Cramer-Rao bound $F_{\rm cl}\leq F_Q$ on smooth pure-state curves, and (iii) operational calibration on basis preparations $P_M([e_i])=δ_i$. The geometric core is a rigidity theorem for Fisher-non-expanding self-maps of the probability simplex: after conjugation by the square-root chart, such maps become round-metric 1-Lipschitz self-maps of the positive spherical orthant, and vertex fixing forces the identity. The main readout theorem is dimensionwise, fixed-PVM, and pure-state only. Escort-class Born uniqueness and the Markov/coarse-graining routes appear as corollaries or alternative routes.

We study the semiclassical Ehrenfest trajectories in open quantum systems. We first derive in explicit form the Fokker-Planck equation that governs the time evolution of the mixing measure for a Gaussian mixture. Then, we embed the generalized Ehrenfest theorem recently obtained for open quantum systems into this phase-space picture to study the time evolution of the expectations of observable with respect to the Gaussian mixture. We show how the coherent and irreversible contributions are microscopically separated. Our work provides a transparent phase-space interpretation of the emergence of classical trajectories in open quantum dynamics.

This work introduces Q3SAT-GPT, a generative model for discovering quantum circuits for the Max-E3-SAT problem. Our method learns from high-performing QAOA-style ansätze to directly generate candidate circuits. To create high-quality supervision, we also introduce Mosaic Adaptive QAOA (MosaicADAPT-QAOA), an adaptive strategy for constructing low-depth QAOA circuits by selecting subsets of mixer operators in each step, rather than inserting operators sequentially. The resulting circuits serve as training data for the generative model, allowing it to learn effective circuit design patterns while eliminating the need for costly variational optimization at inference time. Experiments show that our framework attains strong solution quality with shallow circuits and scales significantly better than both our adaptive construction procedure and conventional variational baselines. Our results establish generative modeling as a high-performance route toward the scalable discovery of quantum optimization circuits, demonstrating that these models can effectively internalize circuit logic while providing a foundation for future, instance-aware inductive biases. Reproducibility: The source code is available at https://github.com/pratimugale/Q3SAT-GPT.

Quantum secret sharing schemes are a family of quantum cryptographic protocols which provide secure quantum encodings, mapping one secret to multiple shares of information such that the original secret cannot be accessed without an authorized set of shares present for decoding. In this work, we describe a protocol that enables sender-anonymity during the secret decoding process. By using permutation-invariant QEC codes along with a set of anonymous quantum transmission algorithms, we construct a quantum anonymous secret sharing scheme that achieves sender-anonymity. We quantify information leakage in ramp quantum secret sharing schemes via the quantum conditional min-entropy, justifying it as a valid measure of leaked information by relating it to the Knill-Laflamme quantum error correction conditions. Finally, we evaluate several permutation-invariant codes using this measure to make observations on the information leakage of intermediate shares for each quantum anonymous secret sharing scheme.

Even simplified models of quantum many-body systems can be difficult to analyse. However, taking inspiration from the foundations of physics, one may wonder whether there are practical advantages to constructing alternative beyond-quantum descriptions of many-body systems. We explore this question in the context of quantum interactions that are diagonal in the computational basis. We construct a hypothetical model of a continuous time dynamical many-body system that is based upon lattices of interacting particles called "cylindrical bits", a concept first introduced in [6]. In the language of [5] our toy model is {\it non-free}, as we need spatial constraints on how the particles interact to ensure valid probabilities. We investigate these constraints and explore the resulting `entangled' states that can exist. Certain pure {\it quantum} entangled systems can be faithfully mimicked by our cylindrical worlds. This allows us to simulate efficiently classically, in the sense of sampling measurement outcomes, a variety of previously unknown quantum systems. Examples include some states created by pure Ising interactions algebraically decaying faster than $\sim 1/r^{3D/2}$, with spatial dimension $D$, under measurements in the $Z$ eigenbasis or eigenbases of $aX+bY$ for $a,b \in \mathbb{R}$. We also explore whether another choice of non-quantum `particle' could expand the applicability of the classical simulation by defining and partially optimising a figure-of-merit that attempts to capture how useful various possibilities may be.

In his 1972 book Science At the Crossroads, Helbert Dingle attacked the consistency of special relativity through a fallacious argument championed by the crank community even to this day. Dingle's affair is a curious chapter in the history of physics and, more generally, science. We briefly review Dingle's case from a historical and didactic perspective.

The chemical bond is a central organizing concept in chemistry, yet it is absent from the molecular Hamiltonian and no "bond operator" exists. Bonding is therefore not a primitive physical entity but a derived descriptor emerging from the quantum state. The logical consequences of this observation are revisited. Statements such as "bonding stabilizes structure" when taken literally risk circular reasoning (petitio principii), whereby bonding is inferred from a stationary structure and then invoked as its cause. The same caution applies to concepts such as steric repulsion, which is also a derived descriptor. Bonding accompanies stable or metastable states and correlates with their properties without constituting their cause. Illustrative examples are drawn from QTAIM, non-covalent interaction (NCI) approach, protein structure, and hydrogen-hydrogen bonding. Causation, language, and the autonomy of chemistry are also briefly discussed. The aim is not at all to diminish the role of bonding, but to place it at the correct logical level, that is, as a powerful, state-dependent descriptor that organizes, classifies, and predicts chemical behavior without serving as its fundamental cause.

Gravitational Wave (GW) data bring an exceptional avenue to test the underlying models of coalescing compact objects. In the regime of strong gravity and high curvature, they allow the exploration of minute deviations from the best-fit models, which are difficult to uncover with other observational modalities. These deviations can stem from departures from General Relativity (GR) or unaccounted astrophysical effects. They may not be explainable within the current description of GW strain data, or may simply be difficult to model. However, they are expected to be correlated between detectors and across the population of observed events. The recently developed SCoRe analysis pipeline leverages these properties by focusing on the correlated power between detectors and combining results from multiple events. In this paper, we apply the framework on the Third Gravitational-Wave Transient Catalog to search for source-dependent deviations. In particular, we explore whether there is evidence for a mass-scale in the observed events, which can act like a line of demarcation in their physical properties by exhibiting a deviation that is different above and below this mass-scale. This mass scale dependency naturally arises in gravitational theories described through effective field theories, due to environmental effects or in scenarios involving exotic compact objects, where the GW signature can differ from the standard binary black holes in GR. Using the 30 highest Signal-to-Noise Ratio events in the catalog, we find Bayes factors ranging from 0.16--0.5 (depending on where the threshold mass is set), thus disfavoring the hypothesis of existence of any mass-scale between $\sim 2.5$ M$_\odot$ and $60$ M$_\odot$. We also compute the distribution of excess cross-correlated power across events and find a Bayes factor of $0.07$, which agrees with expected noise statistics.

As quantum computers continue to increase in size and topological complexity, benchmarking crosstalk becomes more complex and resource-intensive. This limits the ability to obtain relevant crosstalk metrics for applications such as error mitigation, quantum computer security, and circuit transpilation. There applications benefit from accessible metrics on how each gate contributes to crosstalk. However, crosstalk metrics are rarely provided by quantum computer providers and can be expensive to obtain on modern large NISQ devices. In this work, we propose CrossBench, a customizable system that generates crosstalk benchmarks for finitely large NISQ devices with arbitrary topologies. CrossBench uses a custom graph labeling algorithm to generate crosstalk benchmarks for a given quantum topology and gate set. These benchmarks can be used to estimate the average contribution of each gate's crosstalk to qubit error rates. We evaluate the effectiveness of CrossBench by generating and running benchmarks on multiple IBM quantum computers with different topologies. Our results show that CrossBench can identify gates that introduce significant crosstalk across all tested devices, with strong statistical significance (p < 0.05). These promising results show that CrossBench can give simple and accessible crosstalk benchmarks for modern NISQ systems.

We study coordination under restricted information, where classical local models fail to implement certain correlated distributions because agents cannot condition on past history. We show that quantum systems overcome this limitation even when using only separable states. Both classically diagonal encodings (shared latent variables) and separable states with noncommuting local structure (quantum discord) enable the implementation of joint distributions that are unattainable by any classical local rules under the same information constraints. The quantum advantage arises from enabling latent-variable coordination without requiring agents to condition on the latent variable itself -- a construction that succeeds where no classical local model can. Separable states with nonzero quantum discord provide an alternative mechanism for realizing such coordination. At the same time, quantum models remain strictly limited by the information structure: unlike perfect recall, they cannot reproduce fully adaptive dependence on realized past outcomes that are observationally indistinguishable. Thus, quantum correlations serve as a partial substitute for perfect recall.

Trotterization is a standard approach for simulating quantum time evolution on quantum computers, where the Hamiltonian is split into local terms and each term is applied in sequence. The order of these terms affects the fidelity of the simulation when they do not commute, so the choice of ordering directly impacts the accuracy of the simulation. We study this problem for one-dimensional XXZ Heisenberg Hamiltonians using a structured set of 24 candidate orderings derived from colorings of the Hamiltonian's commutation graph and their group permutations. Finding the best candidate for large systems becomes prohibitive because fidelity evaluation is computationally expensive. In this work, we train a transformer encoder on smaller systems to predict the best candidate ordering for larger systems directly from Hamiltonian and Trotter-configuration features, without computing candidate fidelities at inference time. The model is trained on in-range systems of 3 to 14 qubits with 15-qubit systems held out for validation. Experimental results show that the model reaches a mean test fidelity gap of 0.00115 relative to the best of the 24 candidates on out-of-range systems of 16 to 20 qubits. A training-size sweep further shows that generalization emerges once training includes systems up to L=8 qubits, with validation at L=9, and the gap continues to decrease as the training range grows. To our knowledge, this is the first application of a learned model to Trotter ordering, and it motivates future work on AI-guided Trotter ordering with generalization across Hamiltonian families and system types.

For a wide class of bipartite systems with localized couplings, we establish existence of an effective light-cone for propagation of entanglement. This result yields a hard lower bound on the time it takes, under ideal conditions (no loss, no decoherence), to transport entanglement to a distant location (say, a node of a graph-structured quantum network), or to maintain it there.

While current network intrusion detection systems achieve satisfactory accuracy, they often lack explainability. Subgroup Discovery (SD) addresses this by building interpretable rules that characterize feature interactions associated with attack traffic. With large datasets, classical heuristic beam search methods struggle with exponentially scaling search spaces and can prune critical multi-feature interactions. This paper introduces a quantum-enhanced pipeline for SD applied to network intrusion detection using NSL-KDD, formulating SD as quantum optimization for the first time. By encoding feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) and solving it via the Quantum Approximate Optimization Algorithm (QAOA) on IBM Quantum hardware (ibm_pittsburgh), the pipeline identifies subgroups of network features that discriminate normal from attack traffic. A least-squares regression QUBO formulation fits the Weighted Relative Accuracy (WRAcc) landscape over feature subsets, with surrogate sampling for larger QUBOs. Results are benchmarked against exhaustive enumeration and Beam Search using ratios for Hamiltonian quality and WRAcc. Hardware scaling experiments on ibm_pittsburgh (10-30 qubits) reveal that QAOA at depth p = 1 shows WRAcc ratios of 0.983 at 10 qubits, 0.971 at 15 qubits, 0.855 at 20 qubits, and 0.624 at 25 qubits, degrading to 0.039 at 30 qubits as circuit noise dominates, establishing an empirical NISQ scaling boundary. Results demonstrate that QAOA discovers subgroups competitive with classical heuristics and finds multi-feature interaction patterns that greedy Beam Search prunes, with QAOA-unique subgroups achieving up to 99.6% test precision. This work establishes a framework for quantum combinatorial optimization in cybersecurity and characterizes hardware scaling for NISQ devices.

Quantum state tomography provides a fundamental framework for reconstructing quantum states. When the measurement data are not informationally complete, the observed statistics admit multiple compatible density matrices, making the reconstruction problem inherently underdetermined and calling for the selection of a meaningful estimator. Two well-established approaches to address this ambiguity are Maximum Entropy (MaxEnt) and Variational Quantum Tomography (VQT). A variant of VQT, named VQT$_\infty$, has been introduced to reproduce MaxEnt-like solutions. In this work, we generalize this approach by introducing a parametrized cost function that interpolates between the 1-norm and the infinity norm, thereby unifying VQT and VQT$_\infty$ within a single framework. By tuning the associated hyperparameters, the proposed method enables controlled exploration of the set of compatible density matrices. We show that this interplay yields reconstructed states with higher fidelity to the MaxEnt solution than those obtained via VQT$_\infty$ while preserving computational tractability.

Entanglement, a fundamental phenomenon of quantum theory, has recently been observed in processes in high-energy physics. This opens new avenues for probing quantum effects in relativistic regimes, but also poses conceptual and technical challenges. We develop a general framework based on shadow tomography techniques for characterising spin-spin correlations in collider experiments. This improves the analysis of spin-spin entanglement, where relativistic motion couples spin and momentum and the momenta of the investigated particles are not under experimental control. As a proof of concept we illustrate the application of our formalism to top quark pair production at the Large Hadron Collider at CERN. The framework, however, is general and flexible and can be readily applied to more complex final states and systems with more particles.

We show that the operational quantum formalism -- the Born rule, Lüders state updating, quantum interference, non-Markovian effective dynamics, and Bell inequality violation at the Tsirelson bound $2\sqrt{2}$ -- arises within Accessibility Theory (AT) from the Aperture construction together with explicit coherence and locality assumptions stated in the paper. AT is a framework built on real graded spectral triples and a single algebraic selection principle. The Principle of Universal Accessibility Balance requires three independent measures of the complexity of a spectral triple -- its algebraic, gauge-theoretic, and geometric content -- to be exactly equal and minimized, uniquely selecting the algebra $\mathbb{C} \oplus \mathbb{H} \oplus M_3(\mathbb{C})$ and with it the Standard Model gauge group, particle content, four-dimensional Lorentzian spacetime, three generations, and gravitational dynamics. Restriction to a codimension-one geometric boundary reduces this algebra to its commutative center $\mathbb{C} \oplus \mathbb{C} \oplus \mathbb{C}$ -- the Aperture -- which defines a permanent information bottleneck for any embedded observer. Coherence conditions on inference through this bottleneck, together with Gleason's theorem on the 48-dimensional internal Hilbert space, uniquely determine the Born rule; the remaining operational features follow from the same observer-level framework under the stated assumptions. At the ontological level the theory is deterministic and state-realist, while the operational quantum formalism appears at the observer level as a consequence of structurally limited access to the underlying algebraic reality.

Project-based learning is recognized as an effective approach for improving engagement and applied understanding in STEM education. In quantum engineering courses, however, the question is no longer only whether students benefit from projects but how those projects should culminate if the goal is authentic disciplinary preparation. This paper examines the educational role of a conference-style paper requirement embedded within a project-based learning implementation for an introductory quantum mechanics course for engineers. We use post-course survey responses from students in a pilot run of the course. We evaluate perceived effects on conceptual understanding, scientific communication, research readiness, and attitudes toward the writing requirement itself. The results suggest that students viewed the project as beneficial for engagement, confidence, and technical skill development, while the conference-style paper emerged as a demanding but meaningful component of the experience. We argue that once PBL has been established in quantum mechanics education, conference-style writing can serve as an extension of that model, especially for graduate students. The findings support retaining the conference-paper requirement with improved scaffolding.

The early universe need not be described by an incomplete inflationary phase connected to a separate, more exotic prehistory. Recent results show that, within non-static FRW cosmology, only positive spatial curvature permits a nonsingular, geodesically complete universe with ANEC-respecting matter. We construct a geodesically complete closed $k=+1$ bounce-plus-inflation cosmology in ordinary general relativity, sourced by a single canonical scalar field with a positive vacuum offset. The bounce is supported by curvature rather than exotic stress energy: the matter content satisfies the NEC throughout and violates only the strong energy condition, as in any accelerated expansion. The solved branch remains sub-Planckian and evolves onto a curvature-diluted slow-roll phase with inflationary observables consistent with current constraints. The pivot-scale predictions are $n_s=0.9617$, $r=0.0045$ at $N_*=55$ and $n_s=0.9650$, $r=0.0037$ at $N_*=60$. Direct evolution of closed-universe infrared perturbations shows regular tensor and scalar propagation through the bounce and inflationary era, with the physical curvature perturbation freezing in the standard way. This gives a minimal explicit realization of a complete early-universe cosmology in the closed FRW branch selected by completeness and ANEC compatibility.

We introduce a diagnostic -- the quantum noise fraction $β$ -- that determines the maximum sensitivity improvement achievable through quantum enhancement for any gravitational wave detector. Applied to the landscape of proposed high-frequency (kHz-GHz) detectors, this diagnostic reveals that resonant mass detectors operating through tidal coupling are thermally dominated ($β\approx 0$) at all frequencies below ~230 MHz at dilution temperatures, rendering squeezing and entanglement limited in effectiveness. Only above this thermal frontier, defined by $\hbar ω= k_B T \ln 3$, does the quantum regime become accessible. We identify a single concrete realization: a bulk acoustic wave resonator at 1 GHz and 10 mK ($β= 0.98$), and propose a gravitational wave detector employing squeezed phononic states via circuit QED readout. An array of $10^4$ such resonators with 10 dB mechanical squeezing reaches $\sqrt{S_h} = 7.6 \times 10^{-26}/\sqrt{\rm Hz}$ -- still a factor ~$10^9$ above the BBN bound on stochastic backgrounds at 1 GHz, indicating that the sensitivity gap remains predominantly classical in origin and that concurrent advances in classical detector parameters will be required.

The walled Brauer algebras $B_N(m,n)$ govern Schur--Weyl duality for unitary groups $U(N)$ acting on mixed tensor spaces $V_N^{\otimes m}\otimes \overline{V}_N^{\otimes n}$ and play an important role in applications ranging from AdS/CFT to quantum information theory. In the stable regime $N\ge m+n$ the algebra is semisimple and its representation theory is well understood. For $N<m+n$, however, $B_N(m,n)$ becomes non-semisimple. The representation of the algebra on tensor space has a non-trivial kernel and the corresponding quotient algebra is semisimple, with representation dimensions differing from those in the stable regime. We introduce \emph{restricted Bratteli diagrams}, obtained by modifying the standard Bratteli diagrams for $B_N(m,n)$. This construction provides a systematic way to use representation-theoretic data from the stable regime to compute the dimension modifications arising in the non-semisimple regime. In the regime $N=m+n-l$, with $l$ small compared to $m,n$, we show that the restricted diagrams exhibit a stability property and enable an efficient counting of the paths responsible for these dimension corrections. Remarkably, the resulting generating functions are governed by the partition function of an infinite tower of simple harmonic oscillators. We briefly discuss implications for the construction of orthogonal bases of matrix invariants in gauge theory and related applications in quantum information theory.

Entangling operations are at the heart of all approaches to quantum information processing. Parametric gates, in particular, offer a versatile solution to strongly couple off-resonant superconducting qubits with suppressed parasitic crosstalk to spectator qubits due to frequency-selective activation. In this work, we demonstrate a parametric iSWAP gate between two transmon qubits using the recently developed double transmon coupler (DTC). The DTC supports robust internally-defined cancellation point (``off'' state) for static interactions, while simultaneously mediating a fast parametric coupling between data qubits that can be deployed for high-fidelity two-qubit operations. We use robust phase estimation to calibrate non-commuting error terms in the parametric iSWAP gate, and achieve a 99.827% gate fidelity in 40ns without any numerical optimization. The circuit architecture and calibration techniques developed here are extensible to other gate implementations and qubit modalities, paving the way towards resource-efficient quantum information processing.

Exact classical simulation of fault-tolerant quantum circuits remains limited by a tradeoff between exponential state vector scaling, exponential $T$-count scaling in stabilizer-rank approaches, and per-shot tracking overhead in sparse generalized stabilizer simulators. In this work, we introduce Clifft, an open-source simulator that shifts the dominant exponential cost from the total qubit count to a dynamic active subspace by factoring the quantum state into an offline Clifford frame, an online Pauli frame, and a dynamically sized active state vector. This architecture resolves deterministic Clifford coordinate transformations ahead of time, generalizing Stim's compile-once, sample-many execution model to circuits with non-Clifford operations. Consequently, exponential simulation costs are determined by the peak active virtual dimension, which expands during non-Clifford operations and contracts during measurements. Clifft remains within a constant factor of standard tools in the pure-Clifford and non-Clifford limits, while delivering up to orders-of-magnitude throughput gains over GPU-accelerated near-Clifford simulators on low-magic fault-tolerant benchmarks. Executing on commodity CPUs and exposing a Stim-like API, Clifft enables, to our knowledge, the first exact end-to-end simulation of magic state cultivation including the escape stage, over hundreds of billions of shots. These simulations show that escape-stage failures suppress the discrepancy between the true $T$-gate circuit and its $S$-proxy at low decoder-gap thresholds, while at high thresholds the full-protocol behavior approaches the larger discrepancy observed in the cultivation stages alone.

Topological entanglement entropy (TEE) is a key diagnostic of long-range entanglement in two-dimensional gapped phases of matter, but it can suffer from spurious contributions that overestimate the total quantum dimension of the underlying topological order. In this work, we identify the microscopic origin of spurious TEE and introduce a concave partition for computing the Levin-Wen TEE of translation-invariant stabilizer codes of prime-dimensional qudits. We rigorously prove that this prescription is free of spurious contributions. As a complementary probe, we study bivariate bicycle codes on a bipartite cylinder and show that the entanglement entropy depends sensitively on the cylinder circumference, revealing topological frustration of the underlying anyons.

Recent proposals suggest that a notion of generalized complexity, analogous to generalized entropy, may be necessary for understanding the dynamics of holographic complexity in settings where quantum effects are non-negligible, such as evaporating black holes. Beginning with a notion of generalized complexity, we introduce a complexity-based quantum expansion for timelike geodesic congruences, and investigate the consequence of a timelike quantum focusing conjecture. We find that for a suitable class of codimension-0 field theory complexity measures the timelike focusing condition implies a complexity-based quantum strong energy condition as well as a complexity bound which is analogous to the covariant entropy bound.

In this work, we revisit black hole Love numbers from two complementary perspectives. First, we develop a manifestly gauge-invariant framework that directly integrates out the short-distance degrees of freedom of a static black hole in arbitrary spacetime dimensions. This approach yields the effective point-particle action and its associated Love numbers without relying on the standard matching procedure or on the Regge-Wheeler equation and its associated master field. Second, we investigate the role of Love numbers in black hole thermodynamics by analyzing a Schwarzschild black hole subjected to various types of external perturbations. We show that Love numbers govern the induced polarization of the black hole and control the leading corrections to its thermodynamic properties, thereby clarifying their physical significance in black hole thermodynamics.

Superactivation of quantum capacity is the phenomenon whereby two quantum channels, each with zero quantum capacity, can exhibit a strictly positive capacity when used in tandem. In this work, we explore superactivation in the previously unexplored non-asymptotic regime of finitely many channel uses. We give a definition of finite-blocklength superactivation and propose numerical methods that can certify it. Then, focusing on the 50% erasure and positive-partial-transpose channels considered in the original work on superactivation, we show that as few as 17 uses of the joint channel already enable qubit transmission with a fidelity unattainable by any number of uses of either channel alone, demonstrating a strong finite-blocklength form of superactivation and opening the door to experimental demonstration.

Exploiting permutation invariance to reduce the exponential scaling of semidefinite programs in quantum information has emerged as a powerful computational technique. In this work, we develop a systematic framework for using this reduction via Schur-Weyl duality for optimization problems, and establish methods that allow one to work fully inside the permutation invariant subspace while performing operations such as (partially) applying channels and taking (partial) traces, or computing expressions like the quantum relative entropy. We then apply our techniques to the problem of computing efficient lower bounds on the channel fidelity over $n$ parallel uses of a quantum channel. The algorithm, which we call symmetric seesaw method, exploits permutation-invariant codes to yield improved lower bounds on the channel fidelity over $n$ uses of the depolarizing and amplitude-damping channel in the regime of tens of channel uses, and was used in [Parentin, Bergh, Datta, Wilde: Onset of superactivation of quantum capacity, arXiv: today] to demonstrate non-asymptotic superactivation of quantum capacity for $n = 17$. An implementation of our methods, aimed at being suitable for various quantum information theoretic optimization problems, is also available as an open-source Python package.

This paper investigates the properties of Choi polynomials and their fundamental role in the theory of positive linear maps between matrix algebras. By focusing on Hermitian symmetric biquadratic forms, we establish a connection between the positivity of these forms and the structure of positive maps. We specifically explore the construction of indecomposable positive maps in matrix algebras, and their application as entanglement witnesses. Our analysis extends to the detection of Positive Partial Transpose (PPT) entangled states and the classification of edge PPT states in $M_m(\mathbb{C}) \otimes M_n(\mathbb{C})$. Our results provide a refined framework for identifying non-separable states that escape the standard PPT criterion, contributing to the broader understanding of entanglement distillation and quantum information theory.

We present a hardware-native gadget framework for solving constraint satisfaction problems on Rydberg quantum computing architectures. Our approach introduces a compact $xor_1$ gadget that enforces exactly-one constraints, ubiquitous in combinatorial optimization, directly through geometric embedding and blockade interactions. A key advantage of the $xor_1$ gadget is its fixed, problem-size-independent detuning requirements: enforcing constraints through blockade interactions eliminates the need for large penalty terms, thereby substantially reducing the detuning range compared to Quadratic Unconstrained Binary Optimization (QUBO) formulations and improving experimental feasibility. By tailoring the construction to the geometric connectivity of Rydberg atom arrays, the framework bypasses the all-to-all physical couplings often assumed in logical encodings. This enables embeddings compatible with planar layouts and avoids highly connected arrangements. We develop scalable implementations that reduce atom count and connectivity overhead while avoiding extensive classical preprocessing, making them compatible with near-term neutral-atom hardware. As illustrations, we apply our framework to the gate-assignment and $N$-queens problems, highlighting its practicality, resource efficiency, and hardware compatibility. In these examples, we observe reductions in detuning range of up to $99\%$ and savings in atom count and connectivity overhead of up to $54\%$ compared to the QUBO method. These results establish a route toward implementing large-scale combinatorial optimization on Rydberg platforms beyond the limits of existing encodings.

We prove a low-regularity version of Hawking's singularity theorem for Lorentzian metrics in $W^{1,p}$ with Riemann curvature in $L^p$, where $p>2n$ and $n$ the dimension of spacetime. This extends previous results beyond the Lipschitz regime. Under suitable lower Ricci bounds and upper mean curvature assumptions, expressed in terms of temporal functions, we establish both the globally hyperbolic version of Hawking's theorem, in the form of an upper bound on the time separation from a spacelike Cauchy hypersurface, and the version with a compact achronal spacelike hypersurface, yielding timelike RT-geodesic incompleteness. The proof combines regularisations, based on the elliptic RT-equations, to raise the regularity of the metric by one derivative, with a refinement of the previously used manifold convolution. We introduce a new smeared-out notion of mean curvature adapted to the low metric regularity before, and the $W^{2,p}$-hypersurfaces arising after regularisation. As further consequences, we show that $W^{1,p}$-Lorentzian metrics with $L^p$-bounded curvature are causally plain, and we prove a corresponding low-regularity version of Myers's theorem in the Riemannian setting.

We study a projection-type gradient flow for equality-constrained maximisation of a smooth bilinear control objective on $\mathcal{H}=L^2(0,T;\mathbb{R})$, eliminating Lagrange multipliers through an $(M{+}1)\times(M{+}1)$ moving Gram matrix $Γ(s)_{\ell\ell'}=\int_0^T S(t)\,c_\ell(s,t)\,c_{\ell'}(s,t)\,\mathrm{d}t$. The flow generates monotonic ascent in continuous time but becomes unstable on discretisation; existing implementations rely on heuristic step-size safeguards lacking rigorous justification. We close this gap by replacing $Γ$ with $Γ_{\varepsilon}:=Γ+\varepsilon^{2}I$ and prove: (i) an exact spectral identity giving $κ(Γ_{\varepsilon})=(σ_{\max}^{2}+\varepsilon^{2})/(σ_{\min}^{2}+\varepsilon^{2})$; (ii) objective monotonicity $\mathrm{d}J/\mathrm{d}s\ge 0$ for all $\varepsilon\ge 0$; (iii) constraint drift $|h_{m}-C_{m}|=\mathcal{O}(\varepsilon^{2})$ with a computable prefactor; (iv) convergence of the regularised trajectory to the unregularised one in $L^{2}(0,T)$ at rate $\mathcal{O}(\varepsilon^{2})$ under uniform invertibility of $Γ$; and (v) a discrete CFL criterion $Δs\,G\,\|Γ_{\varepsilon}^{-1}\|\leα<2$ guaranteeing objective monotonicity of the forward-Euler scheme up to $\mathcal{O}(Δs^{2})$ local truncation error. The theory is validated on a three-level bilinear benchmark for all-optical Bell-state preparation, where $κ(Γ)\in[10^{9},10^{11}]$, the predicted $\varepsilon^{2}$ rate is confirmed over eight decades, and moderate regularisation eliminates step rejections and reduces constraint drift by more than an order of magnitude at unchanged final fidelity.

In this article, we derive a rigorous lower bound on the ground-state energy for a class of one-dimensional quantum systems in deformed space with minimal coordinate and momentum uncertainties, representing the absolute minimum energy that is physically attainable. We consider a harmonic oscillator in such a space and calculate its ground state energy. We generalized the problem to an arbitrary potential, deriving an equation for the coordinate uncertainty corresponding to the minimal energy, which can be solved numerically. Using a linear approximation in the deformation parameters, we obtained a general expression for the ground-state energy. We determined the domain of existence of solutions for the anharmonic oscillator potential with respect to the deformation parameters.

Limited connectivity makes nonlocal quantum gates expensive on near-neighbor hardware, where compilation typically relies on SWAP transport, inheriting both depth overhead and path congestion. We present a swap-free routing framework in which higher levels of a qudit act as orthogonal spectral buses that transport control information without moving the computational state. We show that exact congestion relief in nearest-neighbor architectures requires local Hilbert-space expansion. In this model, a nonlocal operation over a path of length $L$ requires $2L+1$ logical routing primitives, compared to the $3L$ baseline. Overlapping routes remain distinguishable through bus labels encoded in the same physical qudits. This routing algebra extends to Boolean fan-in at a common target: multiple controls arriving on distinct buses trigger a local unitary based on an arbitrary Boolean function of bus digits, yielding multi-control operations of depth $2L + D_g + O(1)$ for fan-in size $K$ and target-synthesis cost $D_g$. We prove decodability, reversibility, and correctness for CNOT and Boolean fan-in, along with a state-count lower bound $d \geq 2^{K+1}$ for exact overlap routing. Cirq simulations confirm single-control correctness and zero crosstalk. Compiler-level benchmarks on QFT, QAOA, and mirror-interaction circuits verify the predicted congestion law and transport reduction. Noisy QuTiP simulations show that the architectural advantage depends on higher-level coherence and speed. These results identify spectral qudit routing as a congestion-relief architecture that separates nonlocal control delivery from local target-side aggregation, providing a minimal mechanism for overcoming qubit routing limitations.

Quantum annealing targets low-energy solutions of Ising/QUBO problems, but reliable assessment requires more than best-energy comparisons. This dissertation develops a benchmarking framework for D-Wave quantum annealers that combines strong classical baselines, sampling and diversity metrics, and thermodynamic cost. Its first contribution, SpinGlassPEPS$.$jl, is a topology-aware tensor-network heuristic for optimization and sampling on Pegasus/Zephyr-like graphs. It maps Ising instances to local Potts clusters, represents the partition function with PEPS, and performs branch-and-bound search in probability space. Benchmarks show that it is a physically interpretable reference solver, though approximate contractions limit its competitiveness on the largest instances. The second contribution treats quantum annealers as effective thermal machines, relating success probability and solution quality to dissipation, entropy production, and effective temperature. Carefully placed pauses can improve performance while reducing thermodynamic cost, although longitudinal fields may become harmful in paused schedules. The thesis also introduces reinforcement-learning post-processing to improve returned samples and exact small-system simulations to probe annealing dynamics. Overall, it argues for quantum-annealing benchmarks that jointly measure algorithmic performance and physical expenditure.

We develop a geometric-phase framework for time-bin photonic qudits and propose a practical calibration and feed-forward algorithm for separating and compensating geometric (Pancharatnam-Berry), dynamical, and technical phase contributions. Working directly in the time-bin basis, we use a parallel-transport gauge so that geometric phases appear as experimentally identifiable interferometric offsets, while all phase contributions enter a bin-resolved diagonal transformation. We model state preparation by cascaded unbalanced Mach-Zehnder interferometers and give closed-form amplitudes for arbitrary splitting ratios and phases, noting that single-port monitoring requires post-selection and renormalization. We then give an interferometric tomography recipe based on adjacent-bin scans, with a Fourier-basis cross-check, and a multi-mode numerical case study that separates total, dynamical, and geometric phases and demonstrates feed-forward compensation. The protocol uses standard components, including tunable UMZIs, phase shifters or EOMs, and single-photon detectors, together with routine phase sweeps. It is intended for small to moderate dimensions, approximately d up to 10, and provides a scalable route toward phase-stable high-dimensional temporal encoding for quantum communication and photonic processing.

Slater-type orbitals (STOs) provide the physically correct description of atomic wavefunctions but have been largely replaced by Gaussian-type orbitals in computational chemistry due to the lack of closed-form multi-center integrals. We present a systematic study of amplitude encoding of STOs on quantum computers using matrix product states (MPS). For one-dimensional orbital functions of the form $p_d(x) e^{-ζx}$, we derive analytical MPS constructions with constant bond dimension $χ= d + 1$, requiring $O(n)$ classical and quantum resources for $n$-qubit registers with no grid sampling. We demonstrate a complete one-electron integral pipeline -- overlap, kinetic energy, and nuclear attraction -- in one dimension, validating the overlap and kinetic energy on IBM Heron processors at 5~qubits with 0.67\% hardware-induced error using Zero-Noise Extrapolation. In three dimensions, we compute multi-center overlap integrals between 1s and 2s orbitals in Cartesian coordinates with 0.02\% discretization error at 18~qubits. A systematic entanglement analysis reveals that the MPS bond dimension of three-dimensional STOs in Cartesian coordinates saturates with increasing grid resolution -- reaching $\sim$138 for the hydrogen 1s orbital at 12~qubits per coordinate -- establishing bounded encoding complexity rather than the exponential scaling initially expected. The SVD truncation threshold provides a practical resource parameter, reducing the bond dimension to 39 at threshold $10^{-6}$ with negligible accuracy loss. These results map the entanglement landscape for amplitude encoding of atomic orbitals and establish MPS-based state preparation as a viable path toward exact STO basis sets on quantum computers.

Quantum simulation is a cornerstone application for quantum computing, yet standard methods face a trade-off between circuit depth and accuracy: Trotterization depth scales with the number of Hamiltonian terms $L$, while sampling-based qDRIFT is restricted to $O(t^2)$ error scaling. Here, We introduce qSHIFT, an adaptive sampling protocol that overcomes these limitations. By adaptively updating sampling distributions, qSHIFT maintains $L$-independent gate complexity while achieving an improved error scaling of $O(t^{1+r})$ for an adjustable parameter $r$. This performance is enabled by a classical subroutine solving $L^r$ linear equations per sampling round. Numerical demonstrations confirm the $O(t^{1+r})$ scaling, showcasing qSHIFT as a resource-efficient framework for high-precision quantum simulation. Furthermore, the protocol's reduced circuit depth enhances its compatibility with physical error mitigation, making it a promising candidate for implementation on near-term quantum devices. In addition to its role as a standalone algorithm, qSHIFT can provide a high-precision foundation for modular quantum frameworks such as qSWIFT or Krylov quantum diagonalization.

Precision measurements of top-quark decays provide powerful probes of physics beyond the Standard Model (SM). While the impact of dimension-6 operators in the SM Effective Field Theory (SMEFT) has been extensively studied, the role of dimension-8 contributions remains largely unexplored, despite their potential importance as experimental precision improves. In this work, we present a combined analysis of dimension-6 and a representative subset of dimension-8 SMEFT effects using the W-boson helicity fractions in top-quark decays. We compute the leading-order contributions of these operators to the top-quark decay width and helicity fractions, and perform one-parameter and selected two-parameter $χ^{2}$ fits to the combined ATLAS and CMS measurements at a reference scale $Λ=1$~TeV. From the fit results, we find that the inclusion of dimension-8 contributions affects the allowed parameter space of several dimension-6 coefficients through non-trivial correlations and degeneracies. Since the leading dimension-8 contributions enter at the same order $\mathcal{O}(Λ^{-4})$ as the squared dimension-6 terms retained in our analysis, this highlights the importance of a consistent treatment of the EFT expansion when interpreting SMEFT constraints.

The Faddeev-Hopf model [1] supporting Hopfions was shown to emerge in the low-energy limit of four-dimensional scalar quantum electrodynamics (QED) with two charged scalar fields [2, 3]. Faddeev and Noemi conjectured that the Hopfions and Hopf-like solitons -- vortons -- can be based on a twisted toroidal structure inherent to QED [4-6]. This conjecture was discussed in detail in [2] in the approximation of negligibly small extrinsic curvature. Qualitative and semi-quantitative arguments were used to demonstrate the validity of the Faddeev-Noemi hypothesis. Here we further enhance the proof by applying a numerical analysis which confirms that large-size Hopf-like solitons exist as local energy minima in the full QED theory (in the Faddeev-Skyrme model they become topological solitons representing the global minima in the given topological sector).

We present a comprehensive theoretical study of the masses of possible baryonic and mesonic configurations containing a single top quark. Our analysis includes the baryons $Λ_t$, $Ξ_t$, $Σ_t$, $Ξ'_t$, $Ω_t$, $Ω_{tcc}$, and $Ω_{tbb}$, together with the pseudoscalar and vector mesons $T_{t\bar n}^{\mathrm{Ps}}$, $T_{t\bar n}^{\mathrm{V}}$, $T_{t\bar s}^{\mathrm{Ps}}$, $T_{t\bar s}^{\mathrm{V}}$, $T_{t\bar c}^{\mathrm{Ps}}$, $T_{t\bar c}^{\mathrm{V}}$, $T_{t\bar b}^{\mathrm{Ps}}$, and $T_{t\bar b}^{\mathrm{V}}$. Motivated in part by recent experimental indications of a pseudoscalar enhancement near the $t\bar t$ threshold reported by the CMS and ATLAS collaborations, this study is carried out within the framework of two-point QCD sum rules to determine the corresponding ground-state masses by including perturbative contributions and nonperturbative condensates up to dimension eight. For several channels, including the $Λ_t$, $Ξ_t$, $Σ_t$, $T_{t\bar b}^{\mathrm{Ps}}$, and $T_{t\bar b}^{\mathrm{V}}$ states, the extracted central masses lie slightly below the corresponding sums of constituent quark masses, which may indicate nontrivial binding dynamics or near-threshold multiquark configurations within the uncertainties of the method. Moreover, when the full theoretical uncertainties are taken into account in a conservative manner, a larger subset of the investigated states exhibits a consistent tendency toward weak binding behavior, suggesting that the possibility of loosely bound configurations cannot be excluded for most of the considered baryonic and mesonic channels. These results provide useful first-principles theoretical benchmarks for possible top-containing hadronic systems, which may support future searches at the LHC, along with sensitivity analyses for next-generation facilities such as the FCC.

The color structure of two-loop gluon amplitudes is examined both from a color trace basis expansion and an alternative based upon structure constants. We use use this as a vehicle for systemising relations between the partial amplitudes of the color trace formalism.

We present a comprehensive new global QCD analysis of unpolarized parton distribution functions (PDFs) based upon proton, deuteron and $A\!=\!3$ data, including the latest inclusive deep-inelastic scattering (DIS) measurements from Jefferson Lab at high Bjorken-$x$. Using the JAM Bayesian Monte Carlo framework, we systematically explore the stability of the PDFs with respect to variations in the cuts on the invariant mass $W$ of the DIS final state, the implementation of target mass and higher twist corrections, as well as on the nuclear wave functions for the $A\!=\!2$ and 3 data. We find the $u$ and $d$ quark PDFs (and the $d/u$ ratio) are relatively stable up to $x \approx 0.8$, and able to describe DIS data down to $W^2=3.5$ GeV$^2$ and $Q^2=m_c^2$. Within the collinear factorization framework, the fitted higher twist corrections to DIS are found to be positive, and largely isospin independent. The description of the nuclear data also requires nonzero isoscalar and isovector nucleon off-shell PDF contributions, which gives specific predictions for the ratio, $R_D$, of deuteron to isoscalar nucleon structure functions.

We provide a detailed formulation for calculating the angular power spectrum of the cosmic background photons arising from the dark matter decay or annihilation in a comprehensive manner. We pay particular attention to the case of dark matter decaying or annihilating into line photons. It is pointed out that taking account of the energy resolution of the detector is essential to correctly evaluate the angular power spectrum. We apply our formulation to the observational data from infrared, optical, X-ray and gamma-ray telescopes.

A first observation of the rare decay $η$ $\to$ $μ^+μ^-$e$^+$e$^-$ is reported by the CMS Collaboration at the CERN LHC. The result is based on a proton-proton collision data sample at $\sqrt{s}$ = 13.6 TeV corresponding to an integrated luminosity of 38.0 fb$^{-1}$, acquired in 2022 using a high-rate dimuon trigger. Using the $η$ $\to$ $μ^+μ^-γ$ decay channel for normalization, the branching fraction is measured to be $\mathcal{B}$($η$ $\to$ $μ^+μ^-$e$^+$e$^-$) = (2.4 $\pm$ 0.8)$\times$ 10$^{-6}$, with the uncertainty including statistical and systematic sources as well as the $\mathcal{B}$($η$ $\to$ $μ^+μ^-γ$) uncertainty. This result is close to two orders of magnitude smaller than the existing limit, and is consistent with recent theoretical predictions.

We study reflection symmetry and Atiyah-Patodi-Singer (APS) boundary conditions for twisted Dirac operators on a finite warped cylinder. For a complex line twist with holonomy parameter $A$, we show that the reflection lifts to a unitary symmetry of the twisted Dirac setting if and only if $2A\in\mathbb Z$. In the resulting reflection-compatible fixed-holonomy case, reflection pairs opposite shifted angular modes, and the paired APS blocks are unitarily equivalent. The reflection trace on the APS harmonic space localizes to the unique self-paired zero-mode sector. We then turn to parameter-dependent versions of the model. For fixed gauge-trivial holonomy, the family remains pointwise \(O(2)\)-equivariant, and its spectral flow admits an \(RO(O(2))\)-valued decomposition. For genuinely varying holonomy, pointwise \(O(2)\)-equivariance is lost along the path. The representation-ring-valued invariant is then replaced by a residual sign-level invariant: the mod-two parity of the APS crossing events.

Black holes are expected to end their lifetime in a burst of Hawking radiation, emitting all Standard Model particles at ultra-high energies. The evaporation of a nearby primordial black hole (PBH) has been proposed as an explanation for the high-energy neutrino-like event reported by KM3NeT. Such a scenario requires the source to be extremely close to Earth, implying detectable gamma-ray and cosmic-ray emission. Accounting for the time-dependent field of view of gamma-ray observatories, we find that current experiments should have observed a pre-burst signal, while neutrino telescopes would also detect lower-energy events before the burst. The absence of such multimessenger signals strongly disfavors a PBH origin of the KM3-230213A event in the minimal 4D Schwarzschild scenario.

The mass spectra of $ρ$ mesons ($ρ_{Q=\pm 1}^{s_z=0,\pm 1}$ and $ρ_{Q=0}^{s_z=0,\pm 1}$) at finite magnetic field and temperature are studied in frame of the two-flavor Nambu-Jona-Lasinio model. Fully considering the breaking of translational invariance induced by external magnetic field, the analytical form of $ρ$ meson propagators have been derived in the Ritus scheme and Schwinger scheme, which gives the same algebraic formula. When solving the pole equation of $ρ$ meson propagators, multiple solutions of the meson mass appear due to the dimension reduction of their constituent quarks in magnetic fields. At vanishing temperature, we focus on the $ρ$ meson masses $M_ρ$ corresponding to the lowest value solution of the pole equation. $M_{ρ^{-}_+}$, $M_{ρ^{0}_+}$ and $M_{ρ^{\pm}_0}$ increase with magnetic field. $M_{ρ^{+}_+}$ firstly decreases and then becomes saturated with increasing magnetic field. $M_{ρ^0_0}$ is not sensitive to magnetic field. These results are consistent with the available LQCD simulations. At finite temperature, we discuss the lowest four/five solutions of $ρ$ meson masses $M^{i=0,1,2,3,4}_ρ$. With fixed magnetic field, they decrease with temperature, and approach the mass sum of their constituent quarks at high temperature. The mass solution $M^{i}_ρ$ for different mesons $ρ_+^{0,\pm}$ and $ρ_0^{0,\pm}$ may become degenerate at finite magnetic field and temperature.

We perform a complete one-loop renormalization analysis of CPT-odd Lorentz-violating scalar quantum chromodynamics with adjoint scalar matter. Working to first order in the preferred background vector and treating the corresponding operators as perturbative insertions, we compute the ultraviolet-divergent parts of the relevant two-, three-, and four-point Green's functions for the gauge, scalar, and ghost fields. We show that the gauge sector develops the expected Carroll-Field-Jackiw-type correction, which generically turns out to be divergent in our theory, although the divergence vanishes in a certain gauge, while the scalar sector displays the corresponding CPT-odd single-derivative term proportional to the background vector. We further demonstrate that several of the Lorentz-violating corrections the three- and four-point function are ultraviolet finite. All one-loop divergences may be absorbed into counterterms already allowed by the classical Lagrangian, providing an explicit proof of the multiplicative renormalizability of the theory at this order. We also obtain the associated renormalization constants and one-loop $β$-functions for the gauge coupling, the Lorentz violation parameters, and the scalar self-interaction.

Limits on spin-0 axion-like-particles (ALPs) coupling to photons are reinterpreted as constraints on massive spin-2 graviton-like-particles (GLPs) with universal coupling $α_\text{G}/M_\text{P}$ (where $M_\text{P}$ is the reduced Planck mass) to the Standard Model fields. A minimally model-dependent recasting is performed, exploiting the formally analogous production and detection mechanisms for both particle types, based on the Primakoff and Gertsenshtein effects, i.e., photon-axion/graviton conversion. Constraints originally derived in the ALP mass vs. photon-coupling plane ($m_\text{a}, g_{\text{a}γ}$) are translated into the corresponding bounds in the GLP ($m_\text{G}, α_\text{G}/M_\text{P}$) parameter space over the full mass range, $m_\text{a,G} \approx 10^{-20}$--$10^{14}$eV probed in current and future experimental setups including cavity-based detectors (haloscopes and resonant upconversion devices), helioscopes, magnetometers, optical interferometers, beam dumps, fixed-target, and collider experiments, as well as astrophysical and cosmological constraints. Generic scenarios are considered in which GLPs are a dark matter candidate and not. Whereas current ALP searches do not set stronger bounds on massive spin-2 particles than fifth-force tests, future magnetometers, two-beam interferometers, and upconversion experiments have the potential to provide very strong sensitivity, down to $α_\text{G}/M_\text{P} \approx 10^{-32} \text{GeV}^{-1}$, for light graviton-like particles with $m_\text{G}\lesssim 10^{-8}$eV. These future detectors exhibit comparatively greater sensitivity to massive gravitons than to axions. For massive gravitons at the TeV scale, exclusive diphoton decay searches, employed in ALP studies, offer a complementary approach to standard searches for spin-2 resonances in other inclusive final states.

We introduce a method for matching the neutral-current deep inelastic scattering process with parton showers at first order in the strong coupling. This multiplicative matching is achieved by reweighting leading-order Born-level events and requires that the first parton-shower emission is distributed according to the real matrix-element. The method is implemented as an internal matching strategy in the Pythia 8 event generator applicable with both currently available parton shower algorithms, the default one and Vincia. The validity of higher-order corrections is verified with comparisons against existing next-to-leading order simulations. The strategy is used to describe reduced cross-sections measured at the HERA collider, and we find better overall agreement and reduced uncertainties with the matching.

We study exclusive vector meson production in ultra-peripheral collisions (UPCs) of a wide range of nuclei, and assess the potential of measurements to constrain the small-$x$ structure of oxygen and neon nuclei. We employ an impact-parameter-dependent color glass condensate framework incorporating JIMWLK evolution, with parameters constrained by a recent global Bayesian analysis of $γ+p$ and $γ+\mathrm{Pb}$ data. We present predictions for coherent and incoherent $\mathrm{J}/ψ$ production in $\mathrm{O}+\mathrm{O}$ and $\mathrm{Ne}+\mathrm{Ne}$ UPCs at LHC energies, and quantify theoretical uncertainties using posterior samples from the calibration. We employ several nuclear structure models and find that $t$-differential observables are sensitive to the chosen model. We further study the mass-number dependence of saturation effects through nuclear suppression factors for coherent and incoherent vector meson production. Saturation-induced suppression increases systematically with both nuclear mass number and energy. Our results provide a unified framework for the systematic study of the onset of gluon saturation and nuclear structure at high energy, accessible in future UPC measurements at the LHC and at the Electron-Ion Collider.

We investigate constraints on the high-density equation of state (EOS) of neutron star matter by analyzing the probability distributions of the endpoints of mass-radius M(R) sequences within a Bayesian weighting framework. Starting from two representative hadronic baseline EOSs, SFHo and DD2, matched at higher densities to an extended linear sigma model description and constrained to approach perturbative QCD (pQCD) results, we construct families of causal hybrid EOSs spanning a broad range of stiffness at supranuclear densities. Observational constraints from the binary neutron-star merger GW170817, mass-radius measurements from the Neutron Star Interior Composition Explorer (NICER), and candidate low-mass and mass-gap compact objects are incorporated through Bayesian likelihood weighting. This approach allows us to determine probability distributions for the maximum neutron-star mass M$_{\rm TOV}$ and the corresponding radius R$_{\rm TOV}$, i.e., the endpoints of the M(R) sequences. We find that the maximum-mass distributions are largely determined by observational constraints and show only weak sensitivity to the choice of baseline EOS, favoring values around 2.2-2.3 M$_\odot$ when the most robust constraints are applied. In contrast, the corresponding radius distributions exhibit a stronger dependence on the underlying hadronic EOS, with typical preferred values near $12\pm 1$ km. Additional tidal-deformability constraints further restrict the allowed parameter space and disfavor very stiff EOS realizations when interpreted together with the possible mass-gap neutron-star candidate. Our results demonstrate that endpoint distributions of M(R) sequences provide a sensitive and complementary diagnostic for constraining the high-density behavior of the neutron-star EOS within a multimessenger Bayesian framework.

In a spin-1 hadron, tensor-polarized parton distribution functions (PDFs) exist. The twist-2 function is $f_{1LL}$ and a twist-3 one is $f_{LT}$. Because an experiment is under preparation at the Thomas Jefferson National Accelerator Facility (JLab) to measure the cross section of electron-deuteron deep inelastic scattering with the tensor-polarized deuteron target, these PDFs need to be understood theoretically. Especially, measurements will be done in a relatively low-$Q^2$ region at JLab, so that twist-3 contributions could become sizable in the cross section. In a previous work, a twist-2 relation was derived for $f_{LT}$ in terms of $f_{1LL}$ by using a nonlocal operator, and it corresponds to the Wandzura-Wilczek (WW) relation between $g_1$ and $g_2$. In addition, another relation similar to the Burkhardt-Cottingham (BC) sum rule was obtained. It is known that a formal way to derive the WW relation and the BC sum rule is to use the operator product expansion (OPE) with local operators. In this work, the WW-like relation and the BC-like sum rule for $f_{LT}$ are derived by using the local OPE method as a reliable independent way to establish these relations.

The strong interaction is the fundamental force that holds quarks and the gluon force carriers together to form protons and neutrons and also binds the atomic nucleus. The theory governing quark-gluon interactions is Quantum Chromodynamics (QCD). A wide variety of experimental data teaches us that quarks and gluons cannot be observed in isolation, a phenomenon known as confinement that is unique to QCD. But no one has used QCD to mathematically prove confinement. Here we show how to define and measure the force on quarks in the proton using available experimental data. Direct evidence for confinement is obtained because the force is found to be attractive and constant for a wide range of quark positions. This work guides future experimental efforts at future Electron-Ion Colliders aimed at obtaining a rigorous quantitative understanding of confinement and the origin of nuclear mass.

We construct and compute a homotopy-theoretic model for the Bott spiral of symmetry-protected topological phases (SPTs) studied by Queiroz--Khalaf--Stern. We model free and interacting fermionic SPTs using K-theory and reflection-positive invertible field theories (IFTs), resp., and define a twisted generalization of the Atiyah--Bott--Shapiro orientation to produce a free-to-interacting map. We also define and compute spiral maps of IFTs to model dimensional reduction in this context, answering a question of Hason--Komargodski--Thorngren. Our analysis highlights two general aspects of homotopical free-to-interacting maps. First, IFTs are more sensitive than K-theory is to the input symmetry data; in particular, the specification of an Altland--Zirnbauer class is insufficient information to define symmetry type for an IFT. Second, the remnant of Bott periodicity on the interacting side relies on an isomorphism of two extraspecial groups of order 32. Our computations use a novel 4-periodic description of a sector of the twisted ko-homology of elementary abelian 2-groups.

The KM3NeT research infrastructure instruments a large volume of seawater using photomultiplier tubes, which are sensitive to the Cherenkov radiation stimulated by the products of neutrino interactions in the water, as well as that stimulated by atmospheric muons which penetrate the sea depths. The KM3NeT/ARCA and KM3NeT/ORCA detectors are situated at different depths in the Mediterranean Sea, with different extension and densities of the photo-detection elements. Although operating independently, taken as a whole the two detectors provide a wide energy coverage for the atmospheric muons flux. Through the detection and analysis of these atmospheric muons, a variety of physics studies are possible with the KM3NeT telescope. A measurement of the atmospheric muon neutrino flux has been carried out with data from the initial six detection units of the KM3NeT/ORCA detector. Relatedly to the atmospheric muon flux, the recent atmospheric lepton model `Daemonflux' has been incorporated into the KM3NeT Monte Carlo event generator for atmospheric muon bundles. This has resulted in a stark alleviation of the atmospheric muon data-Monte Carlo simulation discrepancy - a systemic issue in cosmic ray experiments referred to as the `Muon Puzzle' - and a comprehensive description of the atmospheric muon data in KM3NeT. These atmospheric muons are also used in the calibration of the detectors, as well as constraining systematic uncertainties in the detectors such as the optical properties of the instrumented seawater. An overview of these topics, and other cosmic ray analyses, is presented.

Ratios of production fractions of $b$ hadrons are a dominant source of uncertainty in many LHC analyses, in particular in measurements with $B_s$ mesons. The currently used value for $f_s/f_d$ is based on a combination of hadronic and semi-inclusive semi-leptonic decays, and relies in part on assumptions about the underlying decays that are hard to quantify. We propose an independent alternative method to obtain this quantity by measuring ratios of the exclusive semi-leptonic decays $\bar B_{(s)} \to D_{(s)}^{(*)} \ell \barν$. This method benefits from significant cancellations of both experimental and theoretical uncertainties, as well as robustness against potential contamination from heavy physics beyond the Standard Model. As a proof of principle, we show that current measurements constrain $f_s/f_d$ with an uncertainty of $7\%$, dominated by present experimental uncertainties. This method can also be applied to other ratios of production fractions involving heavier $b$ hadrons, such as $B_c$ or $Λ_b$.

We analyze the charged particle multiplicity distributions in $p-p$ collisions and discuss the violation of the Koba--Nielsen--Olesen (KNO) scaling. We extract the deviations from the leading exponential behavior of the KNO scaled probability and identify a reciprocal symmetry $z\leftrightarrow 1/z$ in the KNO violating corrections observed in the ATLAS and CMS data at $\sqrt{s}=7,\,8,\,13$~TeV. The symmetry imposes a local constraint on the multiplicity distribution at $n=\langle n\rangle$, namely $P'(\langle n\rangle)=-P(\langle n\rangle)/\langle n\rangle$, which we verify directly in the data. We use this constraint to extract the entanglement entropy from the well-measured region $n\simeq\langle n\rangle$, avoiding the large uncertainties associated with the distribution tail.

Axion-like particles (ALPs) are typically identified as pseudoscalars whose couplings are shift-symmetry invariant with the exception of their couplings to gauge bosons and their mass term. Additionally, the ALP mass $m_a$ is usually assumed to be (much) smaller than the ALP decay constant $f_a$. The latter condition is conservative, at best, and excludes part of the ALP parameter space that is presently viable. We revisit the interpretation of the $m_a\ll f_a$ and perform an analysis focussing on leptophilic ALPs. In particular, we explore regions of the parameter space still uninvestigated, where $m_a>f_a$, thus providing a phenomenological study of the ALP-lepton couplings complementary to the existing literature. We point out that a leptophilic ALP may explain the $-3.8σ$ tension in the anomalous magnetic dipole moment of the electron for the Caesium determination in a large region of the $m_a\times f_a$ parameter space, testable in the near future through studies on $μ\to e$ conversion in nuclei.

The growth of adiabatic density perturbations during an era of early matter domination induces $\mathcal{O}(1)$ fluctuations in pre-inflationary QCD axion dark matter across a broad, string-theory-motivated parameter space. Remarkably, at $Λ$CDM matter-radiation equality the scale of these perturbations coincides with the quantum Jeans scale, so they collapse to solitonic ``axion stars''. These axion stars have densities up to $10^4\,\mathrm{eV}^4$, and, including their surrounding halos, they contain as much as $50\%$ of dark matter. Direct searches for a smooth axion background can be suppressed, but transient enhancements or indirect astrophysical signals at axion masses $m_a\lesssim 10^{-5}\,{\rm eV}$ would point to a non-standard cosmological history.

The 2HDM+S is a singlet extension of the Two-Higgs-Doublet Model (2HDM), which offers rich collider phenomenology. In this paper, we parametrize the 2HDM+S with the Higgs masses and mixing angles, which provide a model-independent framework to study the collider signature. Under five benchmark scenarios, we obtain the 95\% C.L. exclusion regions in the Type-II 2HDM+S parameter space by incorporating the SM-like 125~GeV Higgs precision measurements, beyond the Standard Model Higgs direct searches, $Z$-pole precision measurements and $B$-physics observables. We present the results in the Higgs boson masses vs $\tanβ$, Higgs boson masses vs mixing angles, $\tanβ$ vs mixing angles and doublet Higgs boson masses vs singlet Higgs boson mass parameter space. We explore the complementarity between direct and indirect Higgs searches, as well as conventional Higgs search channels and exotic Higgs search channels. Compared to the 2HDM scenarios, we find that exotic channels such as $A/H \rightarrow Z h_S/ZA_S$ can probe large part of the parameter spaces, especially for moderate $1<\tanβ<7$ region where the conventional channels in the 2HDM cannot contribute much.

We present an explicit model of leptogenesis via annihilogenesis in which two right-handed Majorana neutrinos couple to the Standard Model lepton doublets and Higgs, and acquire a large mass shift during a strong first-order phase transition of an additional scalar singlet. As bubbles of true vacuum expand, the $χ_a$ are reflected off the walls and confined to shrinking pockets of false vacuum, where the density grows and the dominant CP-violating process is the $2 \to 4$ annihilation $χ_1 χ_1 \to L_1 L_1 Φ^* Φ^*$. Interference between tree-level $W$ and $B$ exchange and one-loop diagrams containing the heavier $χ_2$ produces a CP asymmetry $ε$, which we evaluate numerically and find to lie in the range $|ε| \sim 10^{-9}$--$10^{-7}$ for $\mathcal{O}(1)$ Yukawa couplings. Electroweak sphalerons convert the resulting lepton asymmetry into a baryon asymmetry $Y_{ΔB}$ that reproduces the observed value across a broad region of parameter space, with little sensitivity to the bubble-wall velocity or initial pocket size. The Majorana mass that controls $ε$ is the residual mass of $χ_1$ inside the collapsing pockets rather than its post-transition value, so the usual relation between the singlet mass and the Standard Model active neutrino masses is relaxed. As a result, the upper bound on $|ε|$ from the largest light-neutrino mass that constrains standard thermal leptogenesis does not apply, and the lower limits on the right-handed-neutrino scale and the reheating temperature are relaxed.

It is usually assumed that a healthy EFT should not allow superluminal propagation. In the presence of gravity, however, the notion of superluminality becomes subtle, since there is no invariant way to compare with an underlying Minkowski light cone. One can instead resort to an asymptotic criterion: whether the EFT can induce signal propagation faster than what allowed by the asymptotic structure of spacetime. In this work we study the asymptotic causal structure of gravitational EFTs by analysing signal propagation in black-hole backgrounds in the presence of higher-derivative operators. We show that in spacetime dimensions D>4 the effective light cones can lead to genuine asymptotic superluminality, which can be used to constrain the regime of validity of the EFT. By contrast, in D=4 the asymptotic causal structure is universally identical to that of Schwarzschild: prompt null curves remain insensitive to higher-derivative corrections and no asymptotic time advance is possible. We first study the representative operator $R_{μνρσ}F^{μν}F^{ρσ}$, then show that this conclusion is true for any EFT, as it relies only on the asymptotic behaviour of the metric. Finally, we discuss two ways to define superluminality in D=4 spacetimes: introducing a covariant cut-off by putting the theory in an asymptotically-AdS background, or imposing a hard cut-off by working at finite distance.

In this review, we discuss the relevance and impact of studying Calabi-Yau threefolds in the context of global model building in string phenomenology. First, taking a phenomenologist-friendly approach, we review how the topologies of the various divisors and curves of the compactifying CY threefolds play a crucial role for generating the various ``suitable" classes of effective scalar potentials, within the framework of the popular moduli stabilization schemes such as KKLT and LVS. Subsequently, we discuss the impact of the specifics of the CY threefold geometries in the minimal LVS inflationary models such as fibre inflation, in particular, along the challenges such as the inflaton field-range bound. In this regard, we discuss a multi-field approach in which several fibre moduli assist to drive successful inflation having a sufficient number of efolds, without getting close to their individual Kähler cone boundaries.

Deeply virtual meson production (DVMP) is among the most prominent channels to extract the nucleon's generalized parton distributions (GPDs) at $ep$ scattering facilities such as {\tt JLab} and the upcoming {\tt EIC/EicC} experiments, which plays a vital role in unravelling the three-dimensional internal structure of nucleon. In this work we calculate for the first time the next-to-next-to-leading order (NNLO) QCD radiative corrections to the DV$π$P processes $γ_L^* p\to π^+ n$ and $γ_L^* p\to π^0 p$ in the generalized Bjorken limit $Q^2\gg \vert t\vert, Λ_{\text{QCD}}^2$, accurate at the leading twist within collinear factorization framework. The impact of the two-loop QCD corrections appears to be positive and substantial, including which considerably improves the agreement between the perturbative QCD prediction and the available {\tt JLab} data. In addition to the differential longitudinal DV$π$P cross section, we also study the impact of the two-loop QCD corrections on the transverse single-spin asymmetries (TSSA) in some benchmark kinematics at {\tt JLab}, {\tt EIC} and {\tt EicC}.

Global QCD analyses provide the primary framework for extracting hadron structure from experimental data, yet the mechanisms by which data constrain non-perturbative functions remain difficult to interpret due to the high dimensionality and complexity of these fits. Here we develop a framework based on linear response and influence functions, which are gradient-based sensitivity measures that directly quantify how experimental information propagates to fitted quantities and observables. These quantities cleanly expose how data locally determines the central values and uncertainties of quantum correlation functions, as well as the correlations between them, providing a transparent and general framework for diagnosing information flow in inverse problems in QCD.

DAMSA (DArk Messenger Searches at an Accelerator) is a novel short-baseline accelerator/beam dump experiment aimed at probing short-lived physics processes, including searches for evidence of a dark sector of particle physics and well-motivated rare Standard Model signals. Motivated by open questions in neutrino physics and the absence of conclusive evidence for conventional weakly interacting massive particles, DAMSA targets MeV-to-sub-GeV dark-sector messengers with feeble couplings that can be produced in abundance at a beam dump/target. By employing an ultra-short baseline, DAMSA is uniquely positioned to overcome the beam-dump "ceiling" that limits sensitivity to fast decaying particles in longer-baseline experiments. The conceptual design emphasizes a beam-dump production scheme combined with a compact detector optimized for rare decays while mitigating intense neutron-induced backgrounds, inherent to high-power proton beams. To validate the experimental strategy and detector technologies, the DAMSA Path-Finder (DPF) proof-of-concept experiment is also proposed, focusing on axion-like particles decaying to two photons, as the benchmark physics case and operating with 8 GeV electron beams at SLAC Linac-to-ESA (LESA) facility. Successful realization of DPF will establish the feasibility of the DAMSA approach, enabling a broad and powerful program to explore short-lived new physics and precision Standard Model processes in a previously inaccessible regime. This paper outlines the technical details of DAMSA's physics goals, key experimental challenges, and how to overcome them.

We develop a novel strategy for accessing the transversity parton distribution function (PDF) of the nucleon within collinear factorization using near-side energy-energy correlators in the dihadron fragmentation framework. We show how this removes the complications of previous approaches that must model either intrinsic parton transverse momentum or resonances in the invariant mass distribution of a final-state dihadron. We present leading-order analytical results for transverse-spin observables in semi-inclusive deep-inelastic scattering and electron-positron annihilation, highlighting their close similarity to the expressions one uses in extracting (un)polarized PDFs and (single-hadron) fragmentation functions in collinear factorization. We make predictions for kinematics relevant for existing and future facilities that demonstrate the feasibility of an energy-energy correlator program in extracting the transversity PDF.

We review the quantization of scalar field theory on $λ$-Minkowski space using the Batalin--Vilkovisky (BV) formalism. We consider $φ^3$-theory in two different quantization schemes: standard and braided. While standard BV quantization is based on an ordinary $L_\infty$-algebra, braided BV quantization is based on a braided $L_\infty$-algebra. We compare the tree-level three-point and four-point correlation functions in the two approaches. For the four-point function, standard quantization leads to two inequivalent classes of diagrams with different noncommutative contributions, whereas braided quantization yields only a single class of diagrams with noncommutativity entering solely through an overall phase factor depending on the external momenta.

We investigate the photoproduction of the tensor meson $f_2(1270)$ on the proton within a Regge-based framework, focusing on the reaction $γp \to p f_2(1270)$ in the few-GeV energy region. The production mechanism is modeled through the exchange of vector-meson Regge trajectories in the $t$-channel, including both $ρ$ and $ω$ exchanges with phenomenologically motivated couplings. The scattering amplitudes are derived using Reggeized propagators and effective hadronic vertices, allowing for the calculation of differential cross sections in the narrow-width approximation. We further extend the analysis to the $π^+π^-$ invariant mass distribution by incorporating a relativistic Breit--Wigner description of the $f_2(1270)$ resonance.

Baryons, as three-quark bound states, require a covariant treatment in the intermediate-energy regime where perturbative QCD is no longer applicable and where nonperturbative correlations dominate. This article reformulates the content of the CERN Baltic Conference 2025 presentation on baryonic bound states in the non-local Nambu--Jona-Lasinio (NJL) model. We review how the relativistic Faddeev approach reduces the three-body quark problem to an effective quark--diquark bound-state problem, describe the scalar and axial-vector diquark channels, and show how the resulting quark--diquark Bethe--Salpeter equation can be written as an eigenvalue problem for the baryon mass. The non-local NJL framework, motivated by QCD-based nonlocal interactions and Dyson--Schwinger considerations, provides a compact description in which baryon masses and form factors are extracted from the numerical solution of coupled integral equations.

Core-collapse supernovae provide natural laboratories for the production of new light particles. In particular, axion-like particles (ALPs) can be constrained via SN1987A cooling arguments. However, significant astrophysical and nuclear uncertainties imply that such bounds may vary strongly depending on modeling choices, even when expensive simulations are employed. In this context, semi-analytic methods offer a simple and fast alternative for deriving new-physics constraints. Building on a previous semi-analytic framework, in which proto-neutron star (PNS) observables are expressed in terms of six global PNS parameters, we include a finite ALP mass in the calculation and derive bounds in the axion-nucleon coupling versus mass plane. The obtained bounds are in good agreement with previous results from numerical simulations, demonstrating the robustness of the method. We also illustrate the sensitivity of the bounds to different PNS parameter calibrations, nuclear effects and cooling exclusion criteria.

In this chapter we provide a pedagogical introduction to the main theoretical aspects related to topology and $θ$-dependence in Quantum Chromo-Dynamics (QCD), and to their phenomenological relevance in the Standard Model ($η^\prime$ physics, neutron electric dipole moment) and beyond (strong CP problem and the axion solution). We then provide an overview of the main analytic predictions for $θ$-dependence obtained using several different approaches (chiral effective theories, large-$N$ arguments, semiclassical methods) and their regimes of validity, as well as a selection of the most recent numerical results about QCD topology obtained via Monte Carlo simulations of the lattice-discretized theory.

Electroweak radiative corrections form a crucial ingredient in modern precision calculations for particle processes at high-energy colliders such as the Large Hadron Collider. The salient features of electroweak corrections as well as currently used techniques and concepts for their calculation are reviewed. Recent progress in this enterprise is illustrated in a discussion of electroweak multi-gauge-boson production processes: massive di-boson production, vector-boson scattering, and massive tri-boson production.

We study semileptonic sum rules for $b \to c τ\overlineν$ transitions involving orbitally excited charm hadrons. Starting from the amplitude-level relation implied by the heavy quark symmetry, we construct sum rules relating these decays. We then examine deviations from the small-velocity limit. Our numerical analysis shows that the deviations generally increase once excited hadrons are involved, with tensor contributions often inducing sizable effects. At present, however, the relevant form factors are not yet sufficiently constrained. Further improvements in the hadronic inputs are essential for these sum rules to yield robust predictions for the corresponding lepton-universality ratios.

A thermal spectrum is often taken as a signature that the emitted probe has reached detailed balance with the surrounding medium. We show that this interpretation is not generally valid by studying how the microscopic emission kernel determines the macroscopic spectrum. In $3+1$ dimensions, a simple thermal spectrum can be generated without probe thermalization when the relevant kernel belongs to a thermally degenerate class. A representative case is realized when the differential cross section depends on the scattering angle but carries no additional dependence on the Mandelstam variable $s$, as in low-energy Thomson scattering. Our results provide a kernel-based criterion for distinguishing genuine probe--medium exchange equilibrium from thermal spectra produced by the structure of the emission kernel itself.

Degenerate spinor Bose gases with repulsive density-density interaction and anti-ferromagnetic spin-spin coupling in one spatial dimension are shown to be described by a quantum integrable matrix extension of the nonlinear Schrödinger model, whose fundamental fields are described by an $m\,\times\,n$ matrix of bosonic field operators. The eigenstates of this model are constructed for arbitrarily sized matrix field operators by means of algebraic Bethe-ansatz techniques, and the corresponding Bethe equations governing the spectra of conserved quantities are derived. The approach thus generalizes previously chosen techniques to account for arbitrary spin multiplets and their spin-spin interaction. Focusing on the specific case of the $2\times2$ model, which is shown to correspond to a spin-$1$ Bose gas, a set of integral equations is derived, which describe its equilibrium thermodynamic properties. From these, the ground state phase diagram is computed both, numerically and analytically in the parameter plane spanned by the chemical potential and an external magnetic field. Furthermore, the existence of paired bound states is shown to modify the Pauli exclusion principle for interacting bosons in one dimension. In particular, it is found that no two quasiparticle rapidities can coincide, provided that the Lieb parameter satisfies $γ>4/3$.

An innovative path for the detectors at future colliders to achieve higher performances is to use a Particle Flow approach, which requires highly granular calorimeters to image individual showers. The silicon-tungsten electromagnetic calorimeter (SiW-ECAL) aims at fulfilling all the expected physical and technical requirements. SiW-ECAL has been developed by the CALICE and ILD collaborations for more than two decades and is now reaching maturity, for linear machines. However, with the tendency towards circular machines, the progress of electronics and the rapid advancement of machine learning (ML) techniques, the SiW-ECAL design needs to be reoptimised to enhance its performance. This study develops ML-based reconstruction approaches for SiW-ECAL, achieving an approximate 20% improvement in energy resolution in the low-energy range and effectively correcting energy leakage in the high-energy range. Subsequently, the SiW-ECAL design is reoptimized based on this method.

The 'Species Scale' has proved to be an important concept when studying consistent effective actions in Quantum Gravity. This is a short summary of my contribution to the Corfu Summer Institute in September 2025, in which I covered two topics, both related in different ways to the fact that the Species Scale is moduli dependent. In the first, based on work done in collaboration with C. Aoufia and A. Castellano, we show how the one-loop Wilson coefficients $\mathcal{F}_n^{(d)}$ multiplyiing BPS protected ${\cal R}^{2n}$ operators obey Laplace-like eigenvalue differential equations of the form $\mathcal{D}^2_{\bf {\cal M}} \mathcal{F}^{(d)}_n = η_d\, \mathcal{F}^{(d)}_n$. This is true both for $n=2$ with 32 and 16 SUSY generators in 10,9,8 dimensions and theories with 8 SUSY generators in 6,5,4 dimensions $(n=1)$. We argue that this fact is at the root of some Swampland conjectures put forward in the past, like bounds on the dumping rates for the tower scales and the exponential behaviour in the Swampland Distance Conjecture. For the second topic, based on work done in collaboration with G.F. Casas, we discuss the one loop potential of the no-scale moduli in GKP-like Type IIB 4d orientifolds. To compute this potential we sum both over light and heavy (tower) modes using the Species Scale as a UV cut-off. We find a generic form $V_{1-loop}\sim g^2m_{3/2}^2M_p^2(g^{i{\bar i}}(\partial_iΛ)(\partial_{\bar i}Λ))/Λ^2$, with $Λ$ the Species Scale. This has minima at the $desert$ $points$ in moduli space and exponentially decreases at large moduli, with a dS hill in between. We argue that this potential may lead to the stabilisation of some or all Kahler moduli at the desert points in 4d Type IIB orientifolds of phenomenological interest.

An asymptotically flat spacetime in $D=4$ can be mapped via Couch-Torrence conformal inversion to the geometry around an extremal non-expanding and non-rotating horizon. At the linearized level, an infinite tower of conserved Newman-Penrose charges can be found at null-infinity, while infinitely many Aretakis charges are conserved in the near-horizon. Couch-Torrence inversion allows one to establish a matching between the two sets of asymptotic charges. In this work we construct the Newman-Penrose and Aretakis scalar charges in higher-dimensional geometries of D3-branes in $D=10$ and D3-brane bound states in $D=4$ and $D=5$ and establish a precise matching between them through the inversion. By exploiting the residual unbroken supersymmetry of Type IIB supergravity, we demonstrate that it is possible to relate scalar (complex dilaton) charges to higher spin charges. In particular, we determine infinite towers of conserved asymptotic spinorial charges associated with the dilatino fluctuations, and determine the map through inversion.

We investigate the charge transport properties of a relativistic drifting plasma using the kinetic theory within the relaxation time approximation. The collective drift induced by electromagnetic fields is described in terms of a suitably modified distribution function. The analysis is done for both constant and time dependent field configurations. For constant electromagnetic fields, we obtain the Hall drift current that arises from the transverse motion of charged particles in electric and magnetic fields. Extending the framework to time dependent electric fields, we show that their temporal variations give rise to polarization drift, which significantly alters the structure of the induced current and introduces additional components along both the conventional drift and polarization directions. We present a quantitative estimate of the Hall drift and polarization induced contributions in the quark gluon plasma and study the temperature dependence of the associated charge transport coefficients in the QCD.

A search is presented for a light charged Higgs boson H$^\pm$ in top quark pair production ($\mathrm{t\bar{t}}$), where one of the top quarks decays to an H$^\pm$ and a bottom quark, while the other decays to a W$^\mp$ boson and a bottom quark. The H$^\pm$ is assumed to decay into a charm and a strange quark, whereas the W$^\mp$ boson decays into a charged lepton (electron or muon) and a neutrino. Results are reported based on proton-proton collision data at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. The analysis probes H$^\pm$ masses in the range 40 to 160 GeV using the invariant mass spectrum of the two light jets, where light jets are defined as jets that do not satisfy the bottom quark tagging requirement. The observed yield is found to be consistent with standard model predictions. Upper limits are set on the branching fraction $\mathcal{B}$(t $\to$ H$^\pm$ b), with values in the range of 0.07$-$1.12% at 95% confidence level, under the assumption that $\mathcal{B}$(H$^\pm$ $\to$ cs) = 100%. These are the first direct limits on charged Higgs bosons produced in top quark decays for masses between 40 and 50 GeV, and the most stringent limits to date in the 70$-$110 GeV range.

We develop the ODE/IM correspondence for the higher-order Mathieu equation arising from the quantum Seiberg-Witten curve of the pure $SU(r+1)$ ${\cal N}=2$ supersymmetric Yang-Mills theory. From the subdominant solutions, we construct the Q-/Y-systems and derive the corresponding TBA equations. The dependence of the moduli parameters is found to be encoded in the boundary conditions of the Y-functions at $θ\to -\infty$. From these boundary data, we derive an analytic expression for the effective central charge, which also governs the subleading contribution in the large-$θ$ expansion of the TBA equations. Finally, we compare the large-$θ$ expansion of the Q-function derived from the TBA equations with that obtained from the WKB method, which yields analytic agreement at subleading order and precise numerical agreement at the higher-order corrections.

We propose a novel and generalized mechanism, dubbed stochastic axion mixing. In a multi-axion framework, this mixing occurs naturally provided that the masses of all ultra-light axion-like particles (ALPs) are distinct and lighter than the zero-temperature mass of the QCD axion. Crucially, this mechanism is independent of the relative magnitudes of the axion decay constants. In contrast to the conventional maximal mixing scenario -- which strictly relies on specific decay constant hierarchies -- stochastic mixing represents a significantly broader formalism. Notably, maximal mixing emerges as a specific subset of stochastic mixing under restrictive conditions. This new mechanism offers profound implications for axion cosmology.

Measurements at LHC show an increased production of strange hadrons with charged multiplicity in pp collisions, which is not described by the Lund String Model (with the Monash tune) implemented in PYTHIA. This work investigates string closepacking, a mechanism invoked during hadronization where overlapping strings create a background field that increases the effective string tension. This reduces strangeness suppression, effectively enhancing production. The model also incorporates an option for "popcorn destructive interference", which suppresses baryon production, to address the non-strange $p/π$ ratio, utilizing color algebra arguments; and an option for "strange junctions", which enhances strangeness specifically within the baryon sector. The Trieste tunes of this model to LHC data are presented. The closepacking model is in qualitative agreement with many of the salient particle ratios, although the $Ξ_c/D$ ratio and the shape of $p_\perp$ spectra remain challenging to account for. Overall, the closepacking model with the Trieste tunes provides a competitive description of enhanced strangeness production in pp collisions, improving upon existing PYTHIA models while avoiding excessive proton yields.

We study the celestial three-gluon amplitude in a dilaton background through the Mellin-Liouville formulation proposed by Stieberger, Taylor and Zhu (STZ). The original map contains an ambiguity in the identification of Liouville and Mellin variables; we resolve it by requiring global conformal covariance and compatibility with the semiclassical expansion of Liouville theory. This uniquely fixes the operator normalization and the parameter dictionary, and leads to a controlled expansion in the Liouville coupling $b$. Starting from the full Liouville DOZZ three-point function, we derive the leading and first subleading terms in the $b^2$ expansion. The leading term reproduces the tree-level Yang-Mills amplitude in the small total momentum limit, as anticipated in the STZ proposal. The one-loop correction can be written in closed form using modified Bessel functions, and its soft limit exhibits a clear separation into geometric and logarithmic contributions. The resulting framework extends the STZ proposal to finite-$b$ corrections in a consistent and computable way.

We consider non-equilibrium evolution of non-Gaussian fluctuations in a hydrodynamic system undergoing a boost-invariant expansion described by Bjorken flow. We derive the evolution equations for two- and three-point velocity correlators using the effective field theory framework and present analytical solutions for them. We show that the average Landau frame is better suited for studying non-Gaussian fluctuations of velocity when relativistic effects are important. In the Bjorken background, the average Landau frame corresponds to the density frame. We demonstrate that the three-point correlators depend nonlinearly on the non-equilibrium dynamics of the two-point functions, and exhibit non-trivial effects such as memory. The importance of these effects in the context of the search for the QCD critical point via fluctuations is discussed.

We study the dimension-six SMEFT four-lepton operators in the $μ$--$τ$ sector. These operators control both charged-lepton scattering and neutrino self-interactions, the latter being weakly constrained by direct laboratory probes despite their importance for cosmological tensions. We compare three classes of constraints on the Warsaw-basis coefficients $[C_{\ell\ell}]_{2222}$, $[C_{\ell\ell}]_{2233}$, and $[C_{\ell\ell}]_{2332}$. We use perturbative unitarity from $2\!\to\!2$ partial-wave analysis, spin-summing positivity sum rules, and the experimental bounds from NA64$μ$ and the global fit~\cite{Falkowski:2017pss}. We find that the global fit dominates for $[C_{\ell\ell}]_{2222}$ and $[C_{\ell\ell}]_{2332}$, while NA64$μ$ provides the leading bound on $[C_{\ell\ell}]_{2233}$, with the unitarity line for this direction entering the range of collider energies near $200~\mathrm{GeV}$. Renormalization-group running between $1~\mathrm{GeV}$ and $30~\mathrm{TeV}$ modifies these coefficients by up to $10\%$. Translating these bounds onto the effective four-neutrino coupling $G_\mathrm{eff}$, we find values many orders of magnitude smaller than the strongly interacting regime motivated by the Hubble tension; this excludes heavy-mediator UV completions of strong $ν_μ$--$ν_μ$ and $ν_μ$--$ν_τ$ self-interactions within the validity of the dimension-six SMEFT and in the absence of tuned cancellations between operators, while leaving the cosmologically motivated light-mediator scenarios unconstrained by this analysis. Finally, we comment on the bounds these coefficients place on a leptophilic $L_μ- L_τ$ $Z'$ UV completion. Our SMEFT-based current and projected NA64$μ$ bounds reproduce the dedicated $Z'$ analyses already available in the literature.

The dark photon plays an important role as a portal to dark matter and has been extensively studied on both experimental and theoretical frontiers. However, as no signals have been observed, the whereabouts of the dark photon remain a long-standing open question. With the proposal of a future $τ-c$ facility, this proceeding reviews and summarizes the current experimental status of the dark photon in the $τ-c$ energy region, which is expected to provide a reference for future searches. The experimental status indicates that the dark photon in the $τ-c$ energy region remains highly promising, highlighting the requirement for larger data samples or new methods to further investigate the benchmarks related to the observed cosmic dark matter.

In recent years, radio detection of ultra-high-energy cosmic rays (UHECRs), with energies above $10^{18}$ eV, has become an established technique. The radio emissions can be simulated with high accuracy using Monte Carlo codes such as ZHAireS and CoREAS. These simulations are essential but are computationally intensive. In this work, we present a machine-learning-based emulator that reproduces radio signal simulations with high accuracy in milliseconds rather than hours. Primary particle properties can then be reconstructed by comparing measured signals to emulated traces using a Markov Chain Monte Carlo approach. Using ZHAireS simulations carried out over the GRANDProto300 experiment layout, the method achieves an 8.9\% resolution on electromagnetic energy and a 0.08° angular resolution, matching state-of-the-art reconstruction performance. Finally, we apply the method on real data, successfully reconstructing cosmic-ray candidates detected by the GP300 prototype.

We propose a novel mechanism to explain the naturally small vacuum expectation values (VEVs) of exotic multi-Higgs fields by employing non-invertible symmetries. Specifically, we introduce an $SU(2)_L$ quartet $H_4$ and a quintet $H_5$ within the framework of the minimal Fibonacci fusion rule (FFR). This non-invertible symmetry strictly forbids the generation of tree-level VEVs for these exotic fields. However, once the symmetry is broken, these VEVs are generated radiatively at the one-loop level.This mechanism is highly minimal, as it requires no additional loop-inducing particles. We demonstrate that for various benchmark energy scales, the resulting VEVs are naturally suppressed to the order of $10^{-3}-10^{-2}$ GeV, satisfying experimental constraints from the $ρ$ parameter. Finally, we illustrate the phenomenological viability of this setup by applying it to three representative neutrino mass models: the type-III seesaw, the Dirac neutrino seesaw, and the inverse seesaw mechanisms.

I briefly discuss the search for low mass scalars at Higgs factories as well as available models that render such scalars feasible, where I focus on new developments since the review presented in arXiv:2205.09687 (see also arXiv:2504.11969 for a recent update).

In this paper we introduce the energy efficiency as a new metric for evaluating both hardware platforms based on Graphic Processor Units (GPU), and algorithm optimisations at High Energy Physics (HEP) experiments. We develop a method to compute the energy efficiency for the case of the first high level trigger (HLT1) of the LHCb experiment, relating the throughput with GPU specifications such as the number of cores, clock frequency, memory bandwidth and thermal design power. The model can be extended to other HEP experiments to make decisions and reach sustainable computing ecosystems.

We revisit the production of primordial black holes (PBHs) by a U(1) gauge field with a pseudo-scalar coupling to the inflaton. We improve upon the existing literature by working in the homogeneous backreaction regime with numerically computed gauge mode functions, adopting state-of-the-art PBH abundance calculations, and incorporating the uncertainty in the statistics of $δρ$. We find that PBHs can account for all of the dark matter in the asteroidal mass range, even when the inflaton gradient energy density is highly subdominant ($10^{-4}$--$10^{-3}$ of the kinetic energy), supporting the validity of the backreaction scheme. This mechanism also unavoidably generates a stochastic gravitational wave background with an amplitude that will be measured at LISA and that will allow to indirectly discriminate between different statistics of $δρ$.

We present a systematic study of the transverse momentum ($p_{\rm T}$) spectra of charmonium (J/$ψ$, $ψ(2S)$) and bottomonium ($Υ(nS)$) states in Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV within an analytical relativistic hydrodynamics framework. The medium evolution is described assuming cylindrical symmetry with boost-invariant longitudinal expansion and Hubble-like transverse flow. Quarkonium spectra are evaluated using the Cooper-Frye formalism on a constant-temperature freeze-out hypersurface, supplemented by a core--corona approach to include both thermal and non-thermal contributions. The model describes the measurements from ALICE and CMS over a broad $p_{\rm T}$ range. For charmonium, both the spectra and the $ψ(2S)$/J/$ψ$ ratio are well reproduced, while deviations at high $p_{\rm T}$ for J/$ψ$ indicate additional hard production mechanisms. In the bottomonium sector, the $Υ(nS)$ spectra and their yield ratios are successfully described, consistent with the expected sequential suppression pattern. These results demonstrate that an analytical hydrodynamic approach combined with a core-corona framework provides a unified and transparent description of quarkonium production in heavy--ion collisions at LHC energies.

We investigate the phenomenology of the Standard Model extended by a real gauge-singlet scalar field, focusing on exotic Higgs decay channels. For a light scalar mass in the range \(0 < m_φ < 40\) GeV, the Higgs boson can decay to both two and three scalar final states. We derive analytical expressions for these decay rates and impose a global constraint on the model parameters by requiring that their sum does not exceed the total Standard Model Higgs boson decay width. This requirement translates into a fourth-order inequality with respect to the singlet vacuum expectation value, \(v_φ\). We demonstrate that satisfying this inequality imposes an upper bound of \(\cos θ< 0.12 - 0.13\) across the entire mass range, providing a complementary constraint to existing direct search limits. Utilizing stronger independent constraints on the mixing (e.g., \(\cos θ< 0.1\)), we then predict upper bounds on the individual exotic decay rates as a function of \(m_φ\) as \(Γ_{h \rightarrow φφ} < 0.06\) MeV and \(Γ_{h \rightarrow φφφ} < 5 \times 10^{- 6}\) MeV, respectively.

We study Carrollian limits of Schwarzschild-AdS black-hole thermodynamics using covariant phase space. Allowing the cosmological constant to vary, we derive the extended Iyer-Wald identity and identify the renormalized bulk term proportional to \(δΛ\) with the generator-normalized thermodynamic volume contribution \(V_ξ\,δP\). We show that the Carroll limit contracts the full thermodynamic phase space together with the metric. For fixed Newton constant, the Lorentzian generator \(\partial_t\) collapses to a zero-norm direction as \(c\to0\), yielding a degenerate sector with vanishing Hamiltonian variation, temperature and volume. Introducing \(ξ_λ=c^{-α}\partial_t\) and \(G=c^γG_C\), we find that the extended first law scales as \(c^{1-α-γ}\), so finite phase-space contractions require \(α+γ=1\). The endpoint \((α,γ)=(1,0)\), obtained by \(τ=ct\), is the ordinary non-degenerate Lorentzian finite-clock normalization. Carrollian finite first laws lie on the segment \(α<1\), hence \(γ=1-α>0\), and give \(T\to0\), \(S\to\infty\), with finite \(T\,δS\) and \(V_ξ\,δP\). We test the scaling principle for fixed-charge and fixed-rotation AdS black holes, and extend it to arbitrary spacetime dimension within the Schwarzschild-AdS family.

Bilocal holography provides a constructive approach to the higher-spin gravity theories dual to vector-model conformal field theories. Its central advantage is that it is completely gauge fixed and formulated entirely in terms of physical degrees of freedom. We derive a remarkably local bulk reconstruction formula and demonstrate its agreement with standard bulk reconstruction, after the same boundary data and gauge-fixed variables have been identified. We further clarify how subregion duality is realized in this framework.

Optical time projection chambers (OTPCs) are well suited for applications that require the highest spatial resolution for particle track reconstruction. The MIGDAL experiment uses a glass GEM-based OTPC and observes a systematic excess in both the intensity and width of particle tracks in its optical readout, when compared with charge readout simulations. One hypothesis is that scintillation light produced inside a GEM hole during the avalanche propagates through the GEM substrate and exits neighboring holes. We present lab measurements testing this hypothesized optical broadening effect in three types of GEM substrates: glass, ceramic, and FR4. Our observations quantify this optical broadening and demonstrate it to be strongest in glass GEMs. Additionally, we use Geant4 simulations to both reproduce our observations and quantify optical broadening effects in realistic charge avalanches. Applying our glass GEM effects to simulated particle tracks yields increases of track intensity and widths by up to around 26% and 31%, respectively. This may explain the larger than expected intensity and track widths observed in the MIGDAL OTPC and is expected to be an observed effect in all GEM-based OTPCs.

We propose a novel mechanism for electroweak baryogenesis in which collapsing domain walls formed by an axion-like field replace the bubble walls in a strong first-order electroweak phase transition. The axion-like particle coupling to the Higgs mass term allows domain walls to separate regions with distinct electroweak phases, while the electroweak crossover induces a potential-energy bias that triggers their collapse. The directed wall motion, through the axion-like particle coupling to the electroweak topological term, acts as an effective baryon chemical potential and generates an asymmetry via electroweak sphaleron processes. We show that the observed baryon asymmetry can be obtained from either late-time entropy injection or sphaleron suppression in a weakly broken electroweak domain. The wall collapse also produces a stochastic gravitational-wave background with features distinct from standard electroweak-scale first-order-transition spectra.

For the two-loop next-to-leading-order electroweak (NLO EW) corrections to $gg \rightarrow ZH$, the light-fermion contributions can be classified into eight distinct topologies. Using the canonical differential-equations method, we perform an analytic computation of the master integrals (MIs) associated with the four planar topologies. Canonical bases are constructed using the Magnus-expansion method, and the resulting alphabets consist of algebraic symbol letters involving nontrivial radicals. We develop a systematic framework for identifying the radical structures of the canonical MIs, enabling their organization into suitable subsystems and, whenever possible, their representation in terms of Goncharov polylogarithms (GPLs) up to $\mathcal{O}(ε^4)$. Only a few MIs at $\mathcal{O}(ε^3)$ and $\mathcal{O}(ε^4)$ are instead represented as one-fold integrals over GPLs, due to the presence of nested square roots that obstruct the simultaneous rationalization of all radicals.

The death of massive stars produces central accreting compact objects and sometimes relativistic jets. Not all jets escape the stellar envelope: unsuccessful, or choked, jets dissipate their energy into a pressurized cocoon, which expands and may break out as a mildly relativistic outflow. We investigate the plasma physics of collapsing massive stars hosting choked jets through relativistic, non-resistive magnetohydrodynamical simulations. We delineate the parameter space for jet choking and quantify the acceleration rate and efficiency of charged particles at strong shocks, which are potential sources of high-energy neutrinos and electromagnetic transients. Our study focuses on blue and red supergiant progenitors, both promising candidates for jet choking.

Species-resolved azimuthal anisotropy scaling functions are constructed from identified particle $v_2$ and $v_3$ obtained from event-by-event iEBE-VISHNU simulations for Pb+Pb collisions at $\sqrt{s_{NN}}=2.76$ and $5.02$~TeV. The scaling functions exhibit a robust collapse across transverse momentum, centrality, particle species, and beam energy, indicating a common and tightly constrained scaling structure. High scaling fidelity yields quantitative agreement with the data-defined reference through an energy-dependent attenuation baseline $β_0$ in central to mid-central collisions and a centrality-dependent modification of the effective attenuation in more peripheral collisions, with only a weak dependence on $\sqrt{s_{NN}}$. The multiplicity dependence of the extracted scaling parameters reflects the interplay of EOS-driven collective expansion, finite system lifetime, and hadronic re-scattering. These results demonstrate that the scaling framework provides a quantitative, constraint-driven probe of the hydrodynamic response, enabling the disentanglement and constraint of the coupled contributions to azimuthal anisotropy.

Rotating calorons were introduced in the context of rotating quark-gluon plasmas. They are anti-self-dual gauge fields on $\mathbb{R}^4$ that are invariant under a glide rotation. We formulate a Nahm transform which identifies rotating calorons with solutions of a delayed-differential equation. Using this transform, we prove existence of an eight-parameter family of charge 1 rotating calorons with nontrivial holonomy and rotational angle $π$, which we construct and visualise using a numerical implementation of the Nahm transform.

We compute exact finite-rank BPS generating functions for the fermionic matrix model with single-trace supercharge $Q_p=\operatorname{tr}(Ψ^p)$ at $(p,N)=(5,3),(5,4),(5,5),(7,4)$, together with partial data at $(7,5)$. In all complete computed cases, the charge-resolved spectrum exhibits an overdetermined factorization -- a power of $p$, times an onset monomial $x^{q_{\min}}$, times $(1+x)^N$, times a palindromic reduced polynomial -- despite the loss of Casimir solvability at $p\ge 5$. We prove rank palindromicity $r_R=r_{N^2-p-R}$ from the exterior top-degree pairing; at $(5,5)$, the ten low-charge ranks and the minimal divisibility condition $(1+x)\mid\mathcal{R}_{5,5}$ determine the remaining middle rank, and direct computation confirms the full generating function. For fixed $p$, the mod-$p$ Witten indices give a closed-form index floor; together with the trivial Hilbert-space upper bound, this places any accumulation point of $N^{-2}\log Z_{\mathrm{BPS}}^{(p,N)}$ in the window $[\log(2\cos\fracπ{2p}),\,\log 2]$. A rank-projection tower gives rigorous lower bounds on the projection-fortuitous cohomology. In matched $\mathcal{N}=2$ SYK examples at $N_f=16$, the BPS count saturates the index floor, whereas the single-trace matrix model has nonzero index excess and broader charge support.

We present quantitative evidence that high-quality internucleon forces derived from $χ$EFT exhibit a striking dominance of Wigner's supermultiplet symmetry, without invoking the large-$N_c$ limit of QCD or assumptions about specific nuclei. We trace the manifestation of this symmetry in nuclear structure using the \textit{ab initio} Symmetry Adapted Model (SAM) and identify suppressed spin-isospin polarizability. Our calculations show that a majority of $\rm ^4He$, $\rm ^6Li$, and $\rm ^6He$ wave functions is concentrated in a few $\rm U(4)$ irreducible representations, without imposing any \textit{a priori} constraints on the model space. This emergent feature points to a strategy for reducing explosive many-body bases of the NCSM while retaining physically important configurations needed to compute observables.

The variational method is used to study the energy levels of muonic helium $(μ^{-} \, e^{-} \, He)$ with an electron in the ground state and a muon in an excited state with principal and orbital quantum numbers $n \sim l+1 \sim 14$. The variational wave functions are chosen in the Gaussian form. The matrix elements of the Hamiltonian in the nonrelativistic approximation, as well as corrections for the vacuum polarization and relativism, are calculated analytically. A series of energies of the Rydberg muon states is obtained, which can be studied experimentally.

Mesogenesis provides a path for generating the baryon asymmetry of the Universe, using only the CP violation furnished by the Standard Model in the decay of $B$ mesons. While this is an intriguing possibility, it is largely constrained by the data on $B$ meson branching fractions into baryons and missing energy carried into the dark sector. We point out that it is possible to make this branching fraction dominant only in the early Universe, through an ultralight scalar coupled to the dark sector and the Standard Model leptons. A scenario is examined where the thermal density of muons in the early Universe temporarily lowers the mass of a dark fermion, allowing for efficient $B$ meson decays. This `dark' decay channel is shut off later when the muon number density falls, making the scenario compatible with flavor data. Our model can be consistent with the LHC constraints on color-charged heavy bosons required to implement Mesogenesis; such states may be discovered in the future runs as their masses cannot be far above the current bounds. We also outline other possible signals, which can arise in future flavor data, long range force searches, and observations of neutron star binary mergers.

The inclusive cross section for top quark pair ($\mathrm{t\bar{t}}$) production in lead-lead (PbPb) collisions is reported for the first time at a center-of-mass energy per nucleon pair of 5.36 TeV. The analysis uses data corresponding to an integrated luminosity of 1.58 nb$^{-1}$ collected by the CMS experiment at the CERN LHC in 2023. The $\mathrm{t\bar{t}}$ production cross section, $σ_{\mathrm{t\bar{t}}} $ = 3.42$^{+0.54}_{-0.51}$(stat)$^{+0.50}_{-0.43}$(syst) $μ$b, is measured in dilepton final states using a fit to a multivariate discriminator that combines the decay electron and muon kinematic properties with the multiplicity of bottom quark jets. The result is consistent with perturbative quantum chromodynamics calculations at next-to-next-to-leading order (NNLO) accuracy employing several nuclear parton distribution functions. In addition, the Drell$-$Yan production cross section ($σ_\text{DY}$) for dilepton masses above 10 GeV and the ratio of $\mathrm{t\bar{t}}$ to DY cross sections ($R_{\mathrm{t\bar{t}}/\mathrm{DY}}$) are found to be compatible with the NNLO predictions. The observables $σ_{\mathrm{t\bar{t}}}$, $σ_\text{DY}$, and $R_{\mathrm{t\bar{t}}/\mathrm{DY}}$ are measured separately for central and semicentral PbPb collisions to investigate for the first time the dependence of top quark production on the collision impact parameter.

We provide a holographic bulk realization of Carrollian free-field structures arising in three-dimensional asymptotically flat (higher-spin) gravity. We construct a class of boundary conditions that generalizes the diagonal gauge of Anti-de Sitter to flat spacetimes. We show that the associated asymptotic symmetries decompose into genuine physical transformations and pure gauge redundancies, the latter being generated by Carrollian screening charges. This structure leads to a bulk-born realization of Carrollian Miura transformations, expressing physical observables in terms of celestial free scalars. Our results establish a concrete link between flat space (higher-spin) gravity and a Carrollian Coulomb gas description, thereby providing a promising route toward the quantization of flat holography.

We find the perturbative potential on the 3d $\mathcal{N}\!=\!2$ Coulomb branch arising from a chiral 4d $\mathcal{N}\!=\!1$ gauge theory on $\mathbb{R}^3 \times S^1$, zeta-regularizing the D-term couplings generated by the Kaluza-Klein modes. This fills a significant gap in the literature on circle-compactified SUSY gauge theories. Unlike earlier indirect approaches to the circle reduction of chiral theories, our formula provides a global view of the Coulomb branch, necessary for capturing holonomy saddles and for systematic implementation. The zero locus of the potential identifies perturbative SUSY vacua, and we show how data-analysis techniques (such as RANSAC hyperplane detection) numerically extract the structure of the moduli space when this locus is extended. Our formula yields new results even in abelian theories, and offers a new perspective on several earlier observations in the context of the Cardy limit of the superconformal index. In particular, circle reductions (of interest in the SCFT/VOA correspondence) found earlier from limits of the index can now be reproduced on $\mathbb{R}^3 \times S^1$. An appendix shows how our 3d $\mathcal{N}\!=\!2$ potential is related to a function arising in the Cardy limit of the index analogously to how the 4d $\mathcal{N}\!=\!2$ prepotential arises in a limit of the Nekrasov partition function.

Neural network field theory (NNFT) represents fields as neural networks and samples field configurations by drawing network parameters from a probability distribution. We identify a previously unexplored architectural freedom in NNFT, parameterized by $α$, that leaves the infinite-width theory invariant but dramatically affects finite-width errors in the calculation of correlation functions. For a massive scalar field, we show that $α=0$, corresponding to propagator-weighted neuron momenta and constant neuron amplitudes, is optimal: it minimizes finite-width variance and uniquely removes IR-sensitive corrections in the interacting theory. Even at $α=0$, relative errors from both bias and variance grow exponentially with distance beyond the correlation length. The bias can be removed by extrapolating to infinite width, which we demonstrate numerically, while the variance imposes a fundamental bound on the achievable signal-to-noise ratio as in lattice field theory. These results chart a path toward developing NNFT into a practical tool for the numerical study of field theories.

Measurements of Higgs boson pair (HH) production in the four bottom quark (4b) final state are presented using proton-proton (pp) collision data at $\sqrt{s}$ = 13.6 TeV collected by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 62 fb$^{-1}$. Events in which the Higgs boson decays, H$\mathrm{t\bar{t}}$, are separately reconstructed as pairs of small-radius jets (resolved), as well as those where they are reconstructed as single large-radius jets (merged), are studied exclusively. Benefiting from new methods in trigger selection, event selection, and signal extraction, the combination of analyses in the resolved and merged topologies gives an observed (expected) upper limit on the HH signal strength, $μ_\mathrm{HH}$, of 4.4 (4.4) at 95% confidence level (CL). Compared to previously published LHC results in the 4b final state, the expected limit with an equivalent integrated luminosity is improved by more than a factor of two in the resolved topology and is better in the merged topology as well. An updated analysis of the resolved topology using 138 fb$^{-1}$ of 13 TeV pp collision data yields an observed (expected) 95% CL upper limit on $μ_\mathrm{HH}$ of 10.0 (5.9), an improvement of about 25% in the expected limit compared to the published results using the same data. Results in the 4b final state with 13 and 13.6 TeV are combined, resulting in an observed (expected) 95% CL upper limit on $μ_\mathrm{HH}$ of 4.7 (2.8). The allowed ranges for the Higgs boson trilinear self-coupling and quartic coupling between two Higgs bosons and two vector bosons are also reported. These are the most stringent constraints achieved in the 4b final state to date.

Based on a sample of $(10087 \pm 44) \times 10^6$ $J/ψ$ events collected with the BESIII detector at BEPCII, we report the first observation of the decay $J/ψ\rightarrow K^- Σ^0 \barΞ^++c.c.$. A partial wave analysis is performed to investigate the involved excited states. In addition to the well-established $Ξ(1690)$, a new doubly-strange hyperon $Ξ(1720) $ is observed decaying to $K^- Σ^0$ with a mass of $1721.0 \pm 5.2_{\rm stat.} \pm 3.4_{\rm syst.} ~{\rm MeV}/c^2$ and a width of $31.3 \pm 18.3_{\rm stat.} \pm 15.4_{\rm syst.} ~{\rm MeV}$, with a statistical significance exceeding $10σ$. The spin-parity hypothesis testing across various quantum number configurations reveals that the spin-parity of $Ξ(1720)$ favors $J^P = {\frac{3}{2}}^+$. Furthermore, the branching fraction of $J/ψ\rightarrow K^- Σ^0 \barΞ^++c.c.$ is determined to be $(2.68 \pm 0.04_{\rm stat.} \pm 0.17_{\rm syst.}) \times 10^{-5}$.

In this work, we construct a double O(3)-sigma model minimally coupled to a Maxwell field in (2+1)-dimensional spacetime and investigate the existence of self-dual magnetic vortex solutions. An analysis of the Bogomol'nyi-Prasad-Sommerfield (BPS) property reveals that both sigma fields belong to the same topological sector and that the potential assumes a periodic cosine-like form. Furthermore, the theory supports the emergence of magnetic vortices with quantized flux, described by two nonlinear O(3)-sigma sectors that effectively combine into a single topological sector in the BPS regime. In addition, we analytically verify the consistency of the BPS structure and its asymptotic behavior. Within this framework, numerical vortex solutions confirm that the field profiles are smooth and spatially localized, with both the magnetic field and the energy density remaining regular and localized.

In this thesis, we study the collective modes induced by the chirality of elementary particles in magnetized media and their implications for neutron star phenomenology. We theoretically predict that the chiral magnetic wave can arise in quark matter inside neutron stars, resulting in the emergence of novel types of seismic oscillations and associated gravitational waves in the context of asteroseismology. We also investigate the response to dynamical electromagnetic fields and find that a dynamical screening, distinct from the conventional Landau damping, occurs due to the chiral anomaly.

We show that in a self-consistent preon model, where Standard Model quarks and leptons are three-body composites confined at a metacolor scale Lambda_cr ~ 10^14 GeV, both leptoquarks and the Standard Model gauge group SU(3)_c x SU(2)_L x U(1)_Y emerge as structural predictions rather than inputs. Combining the preon content of a quark with that of a lepton gives exactly four distinct six-body bosonic leptoquark composites per generation, with electric charges Q = +2/3, -1/3, -1/3, -4/3 and a universal B-L = -2/3 fixed by the preon charge assignments. Their mass is of order Lambda_cr rather than of order the SM fermion masses, because the dynamical near-cancellation that produces light fermion masses is specific to three-body fermionic composites and does not extend to six-body bosonic ones. The fractional B-L = -2/3 forbids proton decay via single leptoquark exchange, requiring a dimension-12 operator and giving tau(p -> e+ pi0) ~ 10^58 years, consistent with the experimental bound > 2.4 x 10^34 years. It is shown that the SM gauge group emerges as a low-energy symmetry consistent with the Planck-scale boundary condition, and leptoquarks are required -- not merely permitted -- by the matching. The anomaly-matching argument extends to a tower of 3n-body composites, each level imposing consistency conditions on the next; we call this the vertical bootstrap and advance it as the organizing principle of the preon program.

We present the results of AGN--host-galaxy decomposition using JWST/NIRCam, HST/ACS, and HST/WFC3 imaging of the North Ecliptic Pole Time Domain Field (NEP-TDF). The light-profiles of 36 NIRCam-selected AGN candidates are modeled for measurement of their point sources, and point source-subtracted host-galaxy emission is used in SED modeling for star formation rate (SFR) estimation. Offsets from the canonical star-forming main sequence (SFMS) show that the host galaxies form two distinct groups distinguished by their star formation: a ``bridge'' between the moderate SFRs of radio sources and low SFRs of X-ray sources, and a cleanly-separated ``branch'' above $Δ\rm SFMS = -1$ whose SFR trends positively with AGN fraction. Branch galaxies include late-type galaxies with X-ray and radio detections and more dominant point sources that are most certainly AGN, while bridge galaxies have predominantly early-type morphologies with weaker point sources that may be due to compact stellar bulges. Both groups show evidence of recent transition between star formation and quiescence, but neither group shows preference for higher or lower stellar mass or redshift, suggesting that star formation in NIRCam-selected AGN-hosts is more strongly determined by AGN activity than by stellar mass.

We present a framework for generating lightcone simulations tailored to the analysis of Stage-IV cosmic shear data using Higher-Order Statistics (HOS). We revisit key design choices from previous simulation campaigns and re-optimize several internal parameters, benchmarking accuracy through changes in $χ^2$ of cosmic shear statistics under survey conditions mimicking 10 years of observations from the Legacy Survey of Space and Time (LSST). We find that discretizing the lightcone uniformly in scale factor yields higher accuracy than commonly adopted schemes such as uniform spacing in redshift or comoving distance. While $N_{\rm part} = 1024^3$ simulation particles (corresponding to a mass resolution of $m_{\rm part} = 2.08\times10^{10}M_\odot$) is sufficient to model two-point statistics up to $\ell = 5000$, we observed significant instabilities on our full suite of HOS as the number of mass shells used in the lightcone construction, $N_{\rm shells}$, is varied. In contrast, simulations with $N_{\rm part} = 2048^3$ particles ($m_{\rm part} = 2.60\times10^{9}M_\odot$) robustly reproduce all statistics considered. In this higher-resolution configuration, $N_{\rm shells}$ can be reduced to $\sim50$ with only minor deviations, no larger than $0.1-0.3σ$ relative to our highest-resolution case ($N_{\rm shells}\sim100$). This has been explicitly verified through a comparison between our fiducial lightcone production mode based on slicing particle snapshots and an exact lightcone mode where individual particle trajectories are solved for at runtime. We further show that the particle density per pixel can be downsampled by a significant amount for $z>1.5$, saving large computational resources with no impact on the resulting statistics. These results guide the design of upcoming simulation campaigns geared towards forward-modeling and emulation-based analyses of Stage-IV data.

We investigate the impact of baryonic feedback on two-point and three-point correlation functions (2PCFs and 3PCFs hereafter, respectively) involving galaxy density fields (g) and weak lensing shear fields (G), from simulated photometric catalogs of galaxies. Specifically, we baryonify high-resolution simulation using a baryonic correction model (BCM) and explore the consequences down to sub-arcminute (arcmin) scales, varying two model parameters with the largest impact on our probes: $M_{\rm c}$, which governs the amount of gas expelled beyond the halo boundary, and $θ_{\rm ej}$, which encodes the maximal ejection radius relative to halo boundary. We create lensing maps and galaxy catalogs assuming survey properties of the upcoming Year-10 data for the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), and investigate the impact of baryonic feedback on the observed correlations, including the galaxy--galaxy--shear (ggG) and the galaxy--shear--shear (gGG) 3PCFs, which are measured, for the first time from simulations, with \textsc{TreeCorr}. Focusing on equilateral 3PCFs, we find that small scales are more heavily affected by baryonic effects than the corresponding 2PCFs, by up to 90 percent depending on the probe, redshift and BCM model. The galaxy--galaxy--galaxy (ggg) 3PCF is significantly affected at scales smaller than about 4 arcmin; a similar effect occurs at 10 arcmin for the ggG 3PCF, at 40 arcmin for the gGG 3PCF, and at about a degree for the shear--shear--shear (GGG) 3PCF. These four three-point statistics, which are collectively referred to as the $4\times3$PCFs, can be used at large scales to robustly constrain cosmological parameters. At smaller scales, their enhanced sensitivity to baryonic effects provides valuable leverage for constraining the BCM parameters and supplying informative priors. [Abridged]

This study performs a multi-wavelength astrometric and photometric examination of a high-confidence sample $(N = 120,418)$ derived from a parent population of 2.36 million unique WDSS-seeded systems. By establishing an empirical polynomial ridge line for the broader Gaia-2MASS-Pan-STARRS subset, we calculated magnitude residuals $(ΔG)$ to probe the systemic limits of the Gaia single-star model. Results reveal a distinct "Detection Gap" manifested as a tri-modal distribution: 14,705 stars $(12\%)$ were identified as overt Astrometric Discordance failures $(\mathrm{RUWE} > 1.4)$, while a significant subset of candidates exhibits signs of Astrometric Suppression -- where dual-flux profiles are absorbed into a stable single-star solution $(\mathrm{RUWE} < 1.4)$ despite the physical presence of a companion. Crucially, while the raw failure rate reaches $12\%$ globally, we identify an asymptotic Intrinsic Binary Residual (IBR) of $\approx 7.0\%$ $(\approx 8,429$ sources) that persists independently of stellar density. Utilizing a "Triple Constraint" framework -- astrometric noise (RUWE), photometric excess $(ΔG)$, and the absence of official Non-Single Star (NSS) classification -- we identify a $5.9\%$ Detection Gap subset within the Gaia-2MASS audit chain that is consistent with a population of "orphaned" binaries clustered near the theoretical -0.75 magnitude "Binary Ridge." This $7\%$ floor is interpreted here as an apparent global sensitivity limit in the Gaia pipeline, suggesting that local stellar mass density models -- which rely on single-star mass-to-light ratios -- may require a quantifiable correction to accurately reflect the local baryonic mass budget.

We investigate the physical origin and location of high-energy neutrino emission in active galactic nuclei (AGN) using the blazar GB6 J1542+6129 as a case study, testing whether neutrinos are produced in compact regions near the black hole or in parsec-scale jets. This question is central to understanding the conditions under which hadronic processes become efficient in AGN environments. We perform a multimessenger analysis combining ~17 years of Fermi-LAT gamma-ray data, including a 5% adaptively binned light curve and Bayesian block decomposition, with ~14 years of VLBI/MOJAVE observations to derive the Doppler factor evolution of the radio core. These are compared to the temporal properties of a suspected IceCube neutrino flare with a duration of $147^{+110}_{-25}$ days. We find that the suspected neutrino flare precedes both a gamma-ray flare and a pronounced increase in the VLBI core Doppler factor by ~1 year. This delay is consistent with the propagation time of a disturbance from the central engine to the 15GHz radio core. The duration of the post-flare gamma-ray activity is similar to that of the neutrino flare. The broadband gamma-ray spectral energy distributions remain consistent in shape across the full, flare, and post-flare intervals, indicating stable particle acceleration conditions. The temporal ordering favors neutrino production upstream of the VLBI core. GB6 J1542+6129 provides evidence for spatially separated neutrino and gamma-ray/radio emission regions in AGN. The observations are consistent with a disturbance-driven, multi-zone scenario in which neutrinos are produced in a compact, photon-rich region near the central engine, while the same disturbance later enhances Doppler-boosted leptonic emission at the parsec-scale VLBI core. These results demonstrate the power of multimessenger observations in constraining the origin of astrophysical neutrinos.

Supermassive black holes (SMBH, $M_{\rm{BH}} > 10^8 M_\odot$) powering luminous quasars already exist one billion years after the Big Bang, yet their connection to their star-forming host galaxies, their relation to the general galaxy population and their contribution to Reionisation remains deeply enigmatic. JWST is finding numerous Active Galactic Nuclei (AGN) in high-redshift galaxies with black hole masses that appear to be over-massive compared to their host's stellar mass, but rarely as massive as those found in luminous quasars. Here we report JWST/NIRSpec observations revealing overmassive SMBH in two ultra-luminous Lyman-$α$ emitters at $z\sim6.6$ that exhibit rare double-peaked Lyman-alpha profiles. The broad Balmer lines indicate black hole masses $M_{\rm{BH}}\simeq 2\times10^8 M_\odot$, matching that found in faint $z\sim 6-7$ quasars, and very high BH-to-stellar-mass ratio ($\sim 0.1-0.2$) that exceed the local relation by a factor $\sim$400-800. Stellar population modelling favours young ages ($<50$ Myr), inconsistent with the sustained average Eddington-rate accretion required to reach the observed BH masses by $z=6.6$. The double-peak Lyman-$α$ profiles require a large ionised bubble and high photoionisation rate that is consistent with the ionising output of quasars powered by black holes of similar mass, thus constraining the cessation of the last quasar episode to $<1$ Myr. We interpret both systems as post-quasar galaxies in which AGN feedback has delayed stellar mass assembly, and propose that episodic quasar activity partially explains the unexpected prevalence of large ionised bubbles deep into the Epoch of Reionisation.

We present the discovery of a second coherent radio burst from the thermally emitting neutron star 2XMM J104608.7$-$594306 in our follow-up observations with the Murriyang Ultra-Wideband Low receiver. This burst shows complex morphology with multiple components and wideband emission spanning from 704 to 4032MHz. We measured a steep spectral index of $α=-2.18\pm0.16$. Our polarimetric analysis demonstrates that the burst is highly polarised with a linear and circular polarisation fraction of 54% and 22%, respectively. We identified an orthogonal jump in the polarisation position angles of the burst, resembling those seen in radio pulsars. We compared this burst with the first radio burst detected from the source with MeerKAT. These two bursts detected in a total of 40 hours on source with MeerKAT and Murriyang, combined, show that 2XMM J104608.7$-$594306 can emit sporadic radio emission with luminosity jumps comparable to those seen in the bright bursts from SGR 1935+2154. This suggests that previously thought radio-quiet neutron stars such as X-ray dim isolated neutron stars and central compact objects could exhibit rare radio bursting activity.

A kinematic analysis of the young stellar association TWHya has been performed. The components of the displacement matrix in the Ogorodnikov-Milne linear model have been estimated both graphically and by solving the basic kinematic equations. The association's volume expansion with a coefficient of $K_{xyz}=103\pm9$ km s$^{-1}$ kpc$^{-1}$ was confirmed, which yields a dynamical age estimate of $t=9.7 \pm0.8$ Myr. Using the graphical method, estimates of the association's proper rigid-body rotation parameters $ω$ around the galactic axes x and y have been obtained for the first time, with velocity values in the range of 50-70 km s$^{-1}$ kpc$^{-1}$ and errors in their determination of 14-19 km s$^{-1}$ kpc$^{-1}$. However, these values are not confirmed by another method. For example, when solving kinematic equations only using proper motions, all three components of rigid body rotation do not differ significantly from zero, $(ω_x,ω_y,ω_z)=(4,7,11)\pm(5,5,5)$ km s$^{-1}$ kpc$^{-1}$.

We present the final 1.2mm and 2mm source catalogs and the redshift distribution of the mm-selected population from the NIKA2 Cosmological Legacy Survey (N2CLS) in the COSMOS field. Our aim is to provide a comprehensive dataset for studying the physical properties and evolution of high-redshift DSFGs. N2CLS covers ~1070 arcmin2 with median noise levels of 315$μ$Jy and 91$μ$Jy at 1.2 and 2mm, respectively. Sources are extracted with a S/N threshold of 3.9, ensuring >80% purity. Multi-wavelength counterparts are identified using high-resolution interferometric (sub-)mm data (NOEMA, ALMA) and radio observations (VLA, MeerKAT). Redshifts are compiled from spectroscopic and photometric catalogs (e.g., COSMOSWeb). The N2CLS master catalog includes 323 sources detected at >80% purity, with 104 sources detected in both bands, 197 only at 1.2mm, and 22 only at 2mm. Multi-wavelength identifications are secured for ~89% of the sample. The redshift distribution of 1.2mm sources peaks at 2.8$\pm$0.1, consistent with the epoch of peak cosmic star formation. In the total sample, we lack redshift for ~2% of the identified galaxies, plus 34 sources for which no accurate positional proxy is available, preventing the identification of a multi-wavelength counterpart. We identify 66 galaxies at z>4. The observed redshift distribution agrees well with the SIDES simulations, while four other galaxy evolution models are statistically inconsistent with the data. N2CLS is the largest contiguous deep survey to date with this depth and homogeneous coverage. This homogeneous coverage is important, as 25% of N2CLS sources lack a SCUBA2 850 mic counterpart, likely because the strongly non-uniform noise distribution of the SCUBA2 map results in lower sensitivity in parts of the field.The released data products provide a legacy dataset for studying dust-obscured galaxy evolution.

Using ''color cut'' method we obtained from SDSS DR16 catalog 733 red quasar candidates which amounted to approximately 4% of the objects from the initial sample. Then we estimated the radiative efficiency, spins, inclination angles, and corresponding new SMBH masses for all 733 objects using three theoretical models. Obtained spin distributions contain a large percentage of objects with retrograde rotation. It may indicate that these are either very young objects or objects that formed as a result of mergers. The dependencies of the estimated spin values on SMBH masses show strong correlation with linear fit slope 0.9-1.0 which allows us to assume that red quasars are likely to contain both Seyferts and NLS1, and that the main mechanism of SMBH mass growth in these objects is disk accretion.

In this work, we report on the high-resolution infrared spectrum of CCH$^+$ ($^3Π$) recorded in the range $3066-3184$~cm$^{-1}$ by means of leak-out spectroscopy. This spectral range covers the fundamental of the CH stretching mode and a highly excited bending vibrational mode. Based on this data (385 ro-vibrational lines), accurate spectroscopic descriptions of the ground and the two vibrationally excited states of CCH$^+$ were obtained. Besides the band origins, spin-orbit coupling constants, rotational constants, centrifugal distortion constants and $Λ$-doubling constants for the ground and excited vibrational states have been derived. This effective Hamiltonian analysis allowed a search for pure rotational lines of CCH$^+$ in its electronic and vibrational ground state using a two-color millimeterwave - infrared scheme. We observed all rotational transitions from $J^{\prime\prime} = 2$ up to $J^{\prime\prime} = 6$ within the $Ω= 2$ lowest energy fine structure component with resolved hyperfine splittings. This data has already guided the first detection of CCH$^+$ in space toward the Orion Bar photo-dissociation region, and has the potential to support further astronomical searches for CCH$^+$ either through radio or infrared spectroscopy, for example with the James Webb Space Telescope.

We aim to characterize the thermodynamic and dynamical conditions leading to condensation in cluster cores, and to assess the role of magnetic fields. We implement a Monte-Carlo tracer particle algorithm in the GPU-accelerated code AthenaPK, and run a purely hydrodynamical and a magnetohydrodynamical (MHD) simulations of an idealized cool-core cluster. We identify the subset of hot ICM tracers that undergo a transition to the cold phase and reconstruct their histories over a lookback time of $300\,\mathrm{Myr}$ prior to condensation. In both runs, the large majority of tracers transitioning to the cold phase follow a thermodynamic pathway driven by mixing, whereby hot ambient gas is entrained onto low-entropy seed clumps that subsequently grow into larger clouds and filaments. In the hydrodynamical run, these seeds form mainly via in-situ cooling at the edges of AGN cavities. In the MHD run, the cold gas cycle is more complex: AGN outflows occasionally shred portions of existing filaments into fragments which are then uplifted, seeding further condensation. In the MHD run, the properties of condensing tracers begin to diverge from the background ICM significantly earlier than in the hydrodynamical run (${\sim}150\,\rm Myr$ before the cooling transition versus ${\sim}30\,\rm Myr$), with vorticity and magnetic energy growing together. The turbulent Mach number at condensation is also systematically lower than in the hydrodynamical run. We examine the post-condensation evolution of individual cold structures in the MHD run, namely a massive core filament and two isolated clouds in quiescent regions. We find that magnetic tension dominates over ram pressure as the primary drag force, significantly reducing the clouds' terminal velocity. Our results demonstrate that magnetic fields substantially impact the assembly history and kinematic properties of the cold phase in cool-core clusters.

The large-scale clustering of galaxies encodes both geometric and dynamical information about the Universe. The Baryon Acoustic Oscillations (BAO) phenomenon provides a standard ruler that constrains the cosmic expansion history, while Redshift Space Distortions (RSD) probe the growth of structure through the peculiar velocity field. In this work, we present a joint analysis of BAO and growth rate parameter, $fσ_{8}$, in the Local Universe out to $z = 0.1$, using the $65,331$ galaxy distances of CosmicFlows-4++ database. A distinctive property of this catalogue is the availability of real space galaxy positions in addition to the redshift space coordinates. Fitting an empirical model to the measurements we obtain $r_{\rm{BAO}}^{\rm{real}} = 132\pm 8\,h^{-1}\,{\rm Mpc}$ in real space, and $r_{\rm{BAO}}^{z} = 139 \pm 7\,h^{-1}\,{\rm Mpc}$ in redshift space, at redshift $z = 0.07$. Modeling the enhancement of the correlation function within the Kaiser formalism, we derive a constraint on the growth rate parameter $fσ_8 = 0.344 \pm 0.105$. This analysis demonstrates how the combination of real and redshift space clustering measurements enables a simultaneous probe of important observables of the large-scale structure. Their joint detection in the same dataset, therefore, provides a self consistent view of the structure and evolution of the Local Universe. This study may be used for consistency analyses of upcoming surveys, as DESI and 4MOST, that will also provide data in both real and redshift space.

Fast radio bursts (FRBs), highly polarized, mostly have a nearly constant polarization position angle (PA) during each burst. Their PAs are observed to vary from burst to burst, with the statistical properties remaining stable across different observation sessions. We found that the intrinsic PAs of repeating FRBs are approximately Gaussian distributed, suggesting that the emission likely originates from a localized region within the neutron star's magnetosphere. A periodicity search of the PA time series using the Lomb-Scargle periodogram reveals no credible periodic signal in the period range from 10 ms to $10^7$ ms, and similar analyses of several active observations also yield null detections. We interpret these properties by extending the rotating vector model to include a dynamically evolving magnetosphere, in which the effective magnetic axis varies from burst to burst due to stochastic perturbations. In this framework, the observed PA distributions can naturally arise from geometric projection effects, and the absence of periodicity reflects the random wandering of the magnetic axis within a confined region. This scenario provides a natural explanation for both repeating and apparently non-repeating FRBs.

Precession is a very common phenomenon for small-scale astronomical objects. However, the precession of galactic disks, occurring on a scale larger than kilo-parsec, has barely been studied in the literature. Quantifying this precession in observations remains challenging due to the lack of high-resolution dynamical data. Cosmological simulations, where gravitational interactions are self-consistently modeled, offer a unique avenue for investigating disk precession. Leveraging the IllustrisTNG simulations, we trace the evolution of spin orientation in Milky Way-like galaxies over cosmic time. We find that disk precession is ubiquitous in galaxies and significantly affects galaxy evolution. The precession is driven by the external tidal torque originating from the anisotropic matter distribution within $30\ \mathrm{kpc}$, and is violent at $\mathrm{z} > 1$ and becomes gentler but significant at $\mathrm{z} \sim 0$, when the disks are considered dynamically settled. Disk precession can induce significant cold gas warp, which is often observed in the Milky Way and nearby galaxies. We predict that the Milky Way is precessing at a rate of $\simeq3-10$ degrees per billion years at current epoch based on its observed warp. Violent precession can heat the orbits of stars, which may eventually produce prolate elliptical galaxies. The tidal torque from central galaxies can cause the precession of nearby satellite galaxies and causes their disks to point towards the centrals, which explains the observational radial alignment. We also find that the precession of accreted cold gas stream, regulated by the galaxies' torque, is crucial for the evolution of disk galaxies.

We have examined the stellar structure of 10 nearby, low stellar mass (10^6 to 6 x 10^7 Msolar) dwarf irregular galaxies by fitting ellipses as a function of surface brightness on ultra-deep V images. These are compared to far ultraviolet images as tracers of the star formation. We find that the often asymmetrical distribution of large patches of star formation activity in dwarfs, even out to low disk surface brightness levels, skews the broad-band optical isophotes in these galaxies. We also looked for evidence of the presence of a stellar halo. Possible hints of such are found in several galaxies from irregularities in the ellipses, but a stack of seven of the galaxies shows a pure exponential out to a V surface brightness of 32.3 mag/arcsec^2 where the stellar surface density is 0.0013 +/- 0.0011 Msolar/pc^2. The extended stellar component, most likely a disk structure, is probably due to internal evolutionary processes rather than external accretion. The UBVI colors of the annuli are consistent with ages of 1-6 Gyr for the far outer stellar disk.

The Nuclear Bulge of the Milky Way harbors stellar populations that provide crucial insights into galaxy formation processes and serve as a nearby analog for understanding bulge formation in external galaxies. However, detailed studies of this region are severely hampered by extreme and highly variable interstellar extinction, which obscures the intrinsic stellar properties and impedes accurate stellar mass determinations. Our goal is to measure the extinction law towards the Nuclear Bulge and to estimate its stellar density. We developed a method to determine the extinction law towards the Nuclear Bulge by kinematically selecting red clump stars belonging to this region. We created a high-spatial resolution reddening map, and computed stellar mass with completeness-corrected red clump star counts, scaled from empirical measurements. We find a total-to-selective extinction ratio of $\mathrm{A_K/{E_{H-K}} = 1.259 \pm 0.074}$, and an extinction ratio of $\mathrm{A_H/A_K = 1.794 \pm 0.046}$, which are consistent with previous works. The high-spatial resolution reddening map shows clear filamentary structures, and a gradient in the extinction over the giant molecular cloud G0.253+0.016 (i.e., the Brick). From the star counts, we measured a stellar mass of $\mathrm{12.2~\pm2.6\times10^8~M_{\odot}}$ for the Nuclear Bulge, in agreement with other mass estimates.

We explore the theoretical viability of modeling a decaying dark matter sector through a unified scalar field approach. Using exact analytical solutions of the Friedmann constraints, we map the fluid phenomenology onto a scalar field potential. Our analysis reveals that physical viability, specifically the existence of a well-defined potential minimum; inevitably forces the dark energy equation of state into the phantom domain. To resolve the kinetic pathologies at late times, we propose reinterpreting the framework within a complex scenario, mapping the imaginary transition to the angular dynamics of a $U(1)$ phase. This mapping naturally yields an ultra-light mass scale of $m_φ\sim 10^{-33} \ \text{eV}$, classifying the model as a unified dark fluid. Finally, we employ a dynamical approach to study the effects of non-minimal coupling, proving that the phantom-dominated epoch acts as a stable, late-time cosmic attractor in this kind of cosmological scenario.

We analyze our new 144 ks deep Chandra observation of the sloshing cold front cluster RXJ2014.8-2430. Previous observations of RXJ2014.8-2430 with XMM-Newton shows evidence of a large scale, sloshing cold front around 800 kpc away from the cluster core. Previous shallow Chandra data also shows evidence of two younger cold fronts closer to the core. Our new deeper Chandra data allow us to analyze the fine, small scale structure of these three cold fronts. Using both beta model subtraction and Gaussian Gradient Magnitude filtering, we confirm the locations of the three cold fronts, as well as discover a large concave structure southeast of the cluster core near the outermost cold front, which could be a large Kelvin-Helmholtz instability or a gas cavity from AGN activity. Analyzing the three cold fronts, we measure the widths of the cold fronts and find them to be consistent with or lower than the Coulomb mean free paths within error, signifying that diffusion is suppressed across the cold fronts. If the concave feature is the inner rim of a cavity, we find that it has a radius in the range 200-330kpc, and would have $PV$ values in the range of $5.7 \times 10^{60}$ - $2.7 \times 10^{61}$ erg. These values would make it consistent with the some of the most powerful bubbles observed.

We report the discovery of two metastable neutral helium (He I*) absorbers in the Milky Way, and use the upgraded CRyogenic InfraRed Echelle Spectrograph on the Very Large Telescope to determine the helium isotope ratio, $^{3}$He/$^{4}$He, along these sightlines. We have also obtained deeper observations of a third sightline to report a $\lesssim4\%$ precision measure of $^{3}$He/$^{4}$He in the Orion Nebula. These data have allowed us to place a $2σ$ limit on the time-variability of He I* absorption in the Orion nebula, ${\rm d}\log_{10} [N({\rm He\,I}^{*})/{\rm cm}^{-2}]/{\rm d}t\leq7.2\times10^{-4}~{\rm dex~yr}^{-1}$ ($<0.17\%~{\rm yr}^{-1}$), suggesting that these absorbers are in radiative equilibrium. We compute new galactic chemical evolution models of the Milky Way, and use our observations to infer the primordial helium isotope ratio and a scaling factor for the yields reported by nucleosynthesis calculations. Based on the data and models that we report here, we infer a best-fit value ($^{3}$He/$^{4}$He)$_{\rm P}=(1.15^{+0.24}_{-0.21})\times10^{-4}$, which agrees with Big Bang nucleosynthesis calculations that assume the Standard Model of particle physics in combination with the baryon density inferred from the cosmic microwave background temperature fluctuations. We infer the stellar yield scale relative to the solar metallicity, $y/Z_{\odot}=2.12^{+0.31}_{-0.29}$, which is somewhat higher than previously found. Finally, we note that the forthcoming extremely large telescopes are poised to determine $^{3}$He/$^{4}$He in more metal-poor environments, to secure a model-independent determination of the primordial value.

Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of thousands of clusters. We present a study using Convolutional Neural Networks (CNNs) to infer cluster structural parameters from weak gravitational lensing observations. Three architectures (VGG-Net, Inception-v4, Inception-ResNet-v2) were implemented in PyTorch and trained on 75,000 synthetic reduced shear maps generated with MOKA, simulating galaxy clusters at $z = 0.25$. The networks simultaneously predict five parameters: virial mass, NFW concentrations, substructure count, and substructure mass fraction. Tests on 5000 clusters show high accuracy for primary properties. With realistic noise ($n_{\rm gal}=30$, $σ_ε=0.3$), mass predictions remain robust (RMS $\sim 1.02 \times 10^{14}$ M$_\odot$/h, $\sim20$\% deviation). Concentration estimates are stable, with VGG-22 achieving the lowest RMS. Substructure properties are more challenging, with systematic underestimation across models. Comparison with traditional shear profile fitting shows improved CNN performance. VGG-22 achieves near-unbiased mass estimates and significantly better concentration recovery, reducing systematic errors. These results demonstrate that CNNs provide an effective and scalable alternative to traditional methods, particularly suited for large survey datasets.[Abridged]

In the hierarchical paradigm of galaxy formation, central massive black holes (MBHs) are expected to coalesce after the merger of their host galaxies. One of the main goals of the Laser Interferometer Space Antenna (LISA) is to constrain the origin and growth of MBHs through their merger rates and mass distribution. Predicting MBH merger rates requires not only tracing their statistical population from large to small physical scales (kpc to sub-pc) but also modelling their formation, accretion, dynamics, mergers, and their galactic physical processes across cosmic time. This project is the result of a large collaborative effort undertaken by the LISA Astrophysics Working Group, bringing together its collective expertise on MBH formation, evolution, and modelling, to build a comprehensive understanding of MBH merger rates across cosmic time. The project compares various theoretical predictions of MBH merger rates, quantifies the spread, and evaluates the global astrophysical uncertainties of the LISA event rates. To build a unique and complete view, our work is based on about 20 semi-analytical models and cosmological simulations from the literature, all employing distinct approaches to modelling MBH and galaxy physics. To compute the merger rates, we also incorporate delays arising from the dynamical phase of MBH hardening to coalescence. We present the expected LISA merger rates given current galaxy formation models and discuss how the merger rate depends on model assumptions, such as the seeding model and the resolution of cosmological simulations.

$ω$~Centauri, the most massive globular cluster in the Milky Way, exhibits a level of stellar population complexity that has long resisted a unified chemical characterisation. We exploit high-resolution near-infrared spectroscopy from the Milky Way Mapper survey (MWM DR19) to construct one of the largest homogeneously analysed samples of $ω$~Cen members to date. Applying Ward-linkage hierarchical clustering in a seven-dimensional chemical abundance space, without prior assumptions on population number or boundaries, we identify ten chemically distinct stellar populations. Their nucleosynthetic signatures trace four enrichment channels: iron-peak, $α$-element, CNO-cycle, and high-temperature proton-capture processes. The populations organise into two dominant groups separated by a large light-element spread at a modest iron baseline, consistent with AGB-driven self-enrichment. This dichotomy reflects distinct enrichment pathways: core-collapse supernovae establish the iron baseline, while AGB stars dominate light-element and $s$-process enrichment. A decoupled rise in $s$-process abundances relative to iron-peak elements, together with sub-dominant Type~Ia contributions across all metallicities, indicates evolution on timescales shorter than the characteristic Type~Ia delay time. One intermediate-metallicity population retains a primordial composition, providing evidence for spatially segregated enrichment within the progenitor. The most metal-rich component may trace star formation continuing after accretion into the Milky Way halo. All populations lie in the accreted regime of the $[\mathrm{Al/Fe}]$--$[\mathrm{Mg/Mn}]$ plane, supporting an ex-situ origin. These results reinforce the interpretation of $ω$~Cen as the remnant nucleus of an accreted dwarf galaxy and provide a framework for future chemo-dynamical studies.

The WISE source, J141046.00+740511.2, has been recently observed from radio to $γ$ rays. Although the optical spectrum is consistent with a BL Lacertae (BL Lac) object, the source displays unusually weak radio emission, which challenges standard interpretations. Our aim is to understand the origin of the broadband emission from J141046.00+740511.2, using a leptonic model of an extended jet. To obtain the distribution of electrons along the conical jet, we solved a steady-state convective transport equation. Emissivities were computed along the jet and integrated over the cone volume to obtain the observed flux. Our model successfully reproduces the observed multiwavelength spectral energy distribution from radio to $γ$ rays and naturally accounts for the source's low radio flux without invoking extra emission zones. We also reproduce the mid-IR emission within the same framework. These results demonstrate that extended jet leptonic models can robustly describe the broadband physics of radio-weak BL Lacs.

The long-standing tension in the Hubble constant $H_0$ has motivated extensive explorations of both new physics and observational systematics, for example, the late-time systematics in measuring the B-band absolute magnitude $M_B$ of type Ia supernovae, which is degenerated with $H_0$ via an intercept $-5a_B=M_B+5\lg (c/H_0/\mathrm{Mpc})+25$ in the linear relation $m_B=5\lg d_L(z)-5a_B$ between the apparent magnitude $m_B$ and logarithmic dimensionless luminosity distance $\lg d_L(z)$. Therefore, this intercept can be evaluated directly from pure observational quantities ($m_B$ and the redshift $z$) for a given model of $d_L(z)$ without knowing underlying systematics in $M_B$-$H_0$ degeneracy. Hence, the constancy of this intercept across different supernova datasets and different redshift bins within the same dataset for a given late-time model serves as a powerful diagnostic for disentangling late-time new physics from local supernova systematics. In this mini-review, we will show that: (1) there is a local $a_B$ tension in PantheonPlus around $z\sim0.01$, and the elimination of it leads to a $H_0$ measurement consistent with both SH0ES typical three-rung and first two-rung measurements; (2) there is a late-time $a_B$ tension in DES-Y5 around $z\sim0.1$, and the elimination of it largely reduces the preference for dynamical dark energy. We also update the late-time $a_B$-tension analysis for both DES-Y5 and DES-Dovekie supernovae, and find that this $a_B$ tension around $z\sim0.1$ is mainly driven by the inter-data tension between DES supernovae and DESI+Planck constraint, and the dynamical dark energy is preferred as a compromise of this tension. Finally, we briefly mention an interacting dark energy model that resolves this tension among DES, DESI, and Planck, and point out a crucial difference between the effective and apparent equations of state of dark energy.

Fast radio bursts (FRBs), thought to originate from magnetars, exhibit diverse polarization properties that constrain their emission physics and local magneto-ionic environments. The polarization position angle (PPA) is particularly sensitive to magnetic-field geometry in the emitting region and propagation effects in the magnetosphere and beyond. In hyper-active repeaters, PPAs are typically stable within bursts and over timescales of hours to days. Here, we present observations of the repeating source FRB~20240114A, which show significant burst-to-burst PPA variations. Using full-Stokes, high-time-resolution observations from the Nançay Radio Telescope (1.1--1.8\,GHz) and the Effelsberg 100-m telescope (1.3--1.5\,GHz) over $\sim1$~year, we measure rotation measures (RMs), polarization fractions, and time-resolved PPAs across 12 epochs. The RMs remain stable, and the emission is predominantly highly linearly polarized, with $\sim81\%$ of bursts showing $L/I > 0.8$, while circular polarization is weaker ($\sim16\%$ with $|V/I| > 0.1$). We find no evidence for Faraday conversion. The PPA exhibits rapid, stochastic variations from milliseconds to hours, spanning $\pm90^\circ$ during two active periods and $\pm50^\circ$ in a third. The distribution of PPA jumps shows that (1) there is no difference in the distribution of jumps on timescales shorter or longer than 1\,s; (2) positive and negative jumps are equally likely; and (3) a jump of $\pm90^\circ$, as expected from, e.g., orthogonal mode jumps, is not more common than any other value. This combination of stable RM, high linear polarization, and extreme PPA variability is not seen in other hyper-active repeaters. These results disfavor emission from a single fixed region and instead suggest multiple emission regions and/or strong magnetospheric and foreground propagation effects, such as plasma lensing.

Despite the diversity and energy dependence of $γ$-ray pulse morphologies in Crab, Vela and Dragonfly, the phaseograms of these three canonical young pulsars can be organised within a single four-beam geometric template. Using \textit{Fermi} Large Area Telescope data, we fit the 60~MeV--3~GeV phaseograms with a mechanism-agnostic, geometry-first parametric model that incorporates phase-dependent Doppler shifts and constrains the three-dimensional locations and bulk motions of four emission sites. In each pulsar, the phaseogram admits a decomposition into two altitude-separated beam pairs. The lower-altitude pair is produced by plasma with bulk motion close to azimuthal corotation, sharpening the main peaks. The higher-altitude pair shows a radially outward bulk-motion component, suggestive of inertial effects in a toroidally dominated magnetic field, and contributes bridge/shoulder emission and ripple-like modulations overlapping the main peaks. As a posteriori, the lower-altitude pair is consistent with curvature-dominated outer-magnetospheric emission, while the higher-altitude pair is consistent with synchrotron-dominated emission from a current-sheet-like outflow. Higher-altitude site heights vary from $\simeq 0.7$ (Crab, $\approx 1$~kyr) to $\simeq 1.1$--$1.4$ light-cylinder radii (Vela and Dragonfly, $\approx 10$~kyr). This unified four-beam, observation-driven geometry maps an altitude-dependent azimuthal tilt of pulsed $γ$-ray emission, providing an observationally anchored framework amenable to systematic tests and readily extensible to other young pulsars.

Type Ia supernova (SN Ia) standardisation parameters exhibit evidence for systematic variation across the host galaxy star-formation rate - stellar mass (SFR$-M_\star$) plane, motivating the incorporation of galaxy SFR information in cosmological inference. SFRs are commonly estimated via spectral energy distribution (SED) fitting with far-infrared (FIR) measurements to account for dust-obscured star formation. Such FIR coverage will, however, be limited for upcoming time-domain surveys such as the Rubin Observatory Legacy Survey of Space and Time (LSST), necessitating the use of alternative SFR tracers. Here, we reconstruct the SFR - $M_\star$ plane using 1.4 GHz diagnostics, to test the consistency of host classifications against FIR-constrained SED-based estimates. Within this plane, SN Ia host galaxies are divided into three regions: Region 1 (low-mass), Region 2 (high-mass star-forming) and Region 3 (high-mass passive). We find that ${\sim}84$ per cent of SN hosts retain identical region assignments when using radio versus FIR-constrained SED-derived SFRs. Measuring SN Ia nuisance parameters ($α,β, M$) within each subregion, we find consistent values between the two SFR - $M_\star$ plane reconstructions, indicating limited sensitivity to SFR estimator choice, with the largest deviations in Region 3 at ${\sim}1.1σ$. Across the three 1.4 GHz SFR - $M_\star$ subregions, we confirm the region-dependent variation in SN Ia standardisation parameters - particularly $β$ - reported in our earlier SED-based analysis. With near-complete radio coverage of the LSST footprint anticipated from current and forthcoming radio continuum surveys (e.g., Square Kilometre Array), radio SFR calibrations will become an increasingly useful and scalable approach to host galaxy classification, supporting the construction of robust SN Ia subsamples for precision cosmology.

In this work, we extend the standard pre-inflationary misalignment mechanism for axion-like particles (ALPs) by introducing a pre-oscillatory phase with constant equation of state $ω_φ\in[-1,1]$, generated by a tracking potential. During the radiation-dominated era, the potential undergoes a rapid transition to the conventional cosine potential. The resulting change in the potential energy across the transition can drive the ALP into a kinetic misalignment phase ($ω_φ=1$) prior to the onset of oscillations. Motivated by persistent cosmological tensions, such as those in $H_0$ and $S_8$, we also investigate an ALP coupling to a dark radiation sector (DR), allowing for its decay. Using a Bayesian analysis, we constrain the ALP parameter space with current cosmological data. Our analysis shows that ALP-induced DR does not resolve the existing tensions. Instead, the data place robust constraints on the model, favoring negative values of $ω_φ$ and constraining the symmetry-breaking scale to $f_φ\in[80,1.5\times10^{10}]~\mathrm{TeV}$, corresponding to ALP masses in the range $m_φ\in[10^{-20},10^{-2}]~\mathrm{eV}$.

Archival XMM and ROSAT X-ray data are used to investigate the structure of the Abell 548 - Abell 3367 region. Based on previous optical studies, this is a region likely to be rich in structure though studies are in disagreement regarding the connection between Abell 3367 and Abell 548. We use the available archival X-ray data together with kinematic data of counterpart galaxies to address this question and to determine the structure in this region. The region is particularly rich in X-ray structure elongated along a SW-NE axis consisting of numerous extended X-ray sources. In general, the structure consists of many galaxy groups and clusters which appear segregated in X-ray luminosity with the least luminous $\sim$ 30% toward the outer region of the clusters, possibly tracing a filament. We find evidence to suggest a supercluster of 3 clusters at redshifts: $\sim$ 0.04, 0.045, and 0.06. Some of the X-ray sources coincident with Abell 3367 have counterpart galaxy redshifts consistent with Abell 548 and others are significantly higher. This supports that Abell 548 and Abell 3667, form a supercluser and the higher redshift X-ray source is a background object. They are part of a larger structure consisting of a previously identified cluster at redshift 0.04, and two groups at redshift $\sim$ 0.06. In addition, there is a filamentary structure at z $\sim$ 0.103. The ubiquity of groups in the large scale structure suggests that they provide an environment where galaxies are in close proximity and evolution via interaction can proceed well before the galaxies make their way into the dense central region of a cluster.

Galaxy physical properties are influenced by their environments, but the processes responsible for mass and environmental quenching and structural transformations remain debated. Galaxy clusters are ideal laboratories for investigating galaxy formation and evolution, offering a full range of galaxy properties and environments. Observations of large-scale structures, particularly filaments in cluster outskirts ($r \sim5r_{200}$), are currently constrained to the low-redshift Universe. To explore galaxy evolution at intermediate redshifts, deep photometric data, ideally combined with spectroscopic redshifts, are essential. Abell S1063 cluster ($z$ = 0.346) is observed within the Galaxy Assembly as a function of the Mass and Environment program with the VLT Survey Telescope (VST-GAME) combined VISTA Public Survey program Galaxy Cluster At Vircam. We investigate galaxy evolution across a wide range of stellar masses and environments. We release a multiwavelength photometric catalog with photometric redshifts for 64394 sources in $1x1 deg^2$. The analysis of overdensity regions provides insights for future studies on galaxy properties in cluster outskirts. The dataset is obtained through deep ($r<$24.65 mag) and wide optical ($u$, $g$, $r$, $i$, VST) and near-infrared ($Y$, $J$, $K_s$, VISTA) observations. The photometric catalog includes all detected sources, excluding nearby or overlapping objects, saturated stars, and image artifacts. The multiwavelength catalog enabled photometric redshift estimates and identification of cluster members. The density field allowed comparison of galaxy properties, colors, and masses across environments. We detect a very dense structure near the cluster center, and with such a large field of view, we find another dense region to the north-west, in the opposite direction to the cluster elongation. Filaments connecting the regions are also visible.

Star-forming galaxies (SFGs) are a subclass of $γ$-ray emitters and a correlation between their $γ$-ray luminosity ($L_{\rm γ}$) and the total infrared (IR) luminosity ($L_{\rm IR}$) has been established based on the Fermi Large Area Telescope (LAT) data. NGC 2403 and NGC 3424 have been reported as outliers in the $L_{\rm γ}$-$L_{\rm IR}$ correlation with light curves showing significant variability, which contrasts with the temporally stable $γ$-ray emission in other SFGs, originating primarily from cosmic rays interacting with interstellar medium. In this study, we reanalyze the $γ$-ray emission in the directions of NGC 2403 and NGC 3424 using more than 16.5 yr Fermi-LAT data. NGC 3424 is found to be spatially coincident with the detected $γ$-ray source, while NGC 2403 is significantly offset from the nearest $γ$-ray source, suggesting an implausible association. We confirm the previously reported variability of both $γ$-ray sources and the significant deviation from the $L_{\rm γ}$-$L_{\rm IR}$ correlation when assuming an association of both $γ$-ray sources with the two galaxies. Our findings lend further support to the interpretation that their $γ$-ray emission is driven primarily by alternative radiative processes-rather than by star formation activity-such as the ejecta of the Type IIP supernova SN 2004dj in NGC 2403 interacting with a surrounding high-density shell and an obscured active galactic nucleus in NGC 3424.

In this work, we investigate a classical cosmological test - the dependence of galaxy surface brightness on redshift z (the Tolman test). We analyzed 6 860 galaxies with reliably determined spectroscopic redshifts from the ASTRODEEP-JWST photometric catalogue. We find that (a) the mean surface brightness of galaxies indeed decreases with increasing distance, and (b) the observed trend shows a significant departure from the prediction of the standard cosmological model, which expects the mean surface brightness to decline as ~ (1 + z)^-4.

Using the SIMBA, EAGLE, and IllustrisTNG-100 galaxy formation simulations, we examine the anisotropy of the satellite distribution and its dependencies on central galaxies, host halos, and cosmic filaments. We find that in all simulations the satellite anisotropy is robustly aligned with the halo/central galaxy major axis. This correlation is both redshift- and halo-mass-dependent and also extends to filamentary structures outside the halo to several virial radii. The alignment persists up to $z=1.5$ at high redshifts, and the mass dependence remains down to $M_\mathrm{200c} \approx 10^{11}M_{\odot}$. We identify a clear $3σ$ scale-dependent transition in the structural tracers of satellite anisotropy: satellite distributions correlate with central galaxy morphology at small scales ($<0.3R_{\rm 200c}$), are governed by host halo triaxiality at halo scales ($0.3$-$2R_{\rm 200c}$), and align with cosmic filaments beyond $2R_{\rm 200c}$. By tracing satellite trajectories in SIMBA, we uncover the kinematic origin of this transition, demonstrating that satellites prefer halo major-axis aligned regions because their trajectories intersect this axis far more frequently and stay in it for a longer time under the host's gravitational potential. This dynamical processing effectively erases primordial filament-related signals upon accretion ($<2R_{\rm 200c}$), explaining the shift in dominant structural tracers across scales.

Open clusters are fundamental laboratories for investigating stellar and Galactic evolution, and serve as important benchmarks for asteroseismic analyses. Using a boutique method to analyze TESS photometry, we study red giants in two old metal-rich open clusters: NGC 188 & NGC 6791. By comparing Kepler and TESS observations for NGC 6791, similar oscillation mode frequencies are recovered, however we find a systematic offset of 2.2% with a scatter of 9% in the $ν_{\text{max}}$ measurements. We attribute this discrepancy to the lower signal-to-noise of the TESS data for these relatively faint stars. For the brighter cluster NGC 188, we present new seismic measurements in 17 red giants. We estimate average seismic masses for the RGB of $M_{\text{RGB,NGC188}} = 1.13\pm0.04$(rand)$^{+0.12}_{-0.19}$(sys) $M_{\odot}$ and RC of $M_{\text{RC,NGC188}} = 1.11\pm0.01$(rand)$^{+0.11}_{-0.19}$(sys) $M_{\odot}$, consistent with independent mass estimates for this cluster and with similar precision to previous Kepler studies. From the difference between the average evolutionary phase masses, we estimate an integrated RGB mass loss of $ΔM = 0.02 \pm 0.04$(rand)$\pm0.01$(sys) $M_{\odot}$, supporting the evidence for lower mass loss at higher metallicities. Using asteroseismology and chemical abundances, we identify three binary interaction candidates: two under-massive stars and one over-massive star potentially exhibiting dipole-mode suppression. Finally, we derive an average seismic cluster age of $7.0\pm0.9$ Gyrs, in good agreement with previous literature ages. Our analysis demonstrates the strong potential of TESS asteroseismology for open clusters, and motivates extending this investigation to other TESS clusters that span a wider range of ages and metallicities.

Unraveling the growth of supermassive black holes and their connection to host galaxies requires disentangling the Active Galactic Nuclei (AGN) emission from that of the stellar populations. When an AGN spectrum is observed at different activity phases, if the spectral decomposition properly recognizes the nuclear and stellar components, key physical properties - such as black-hole mass, stellar mass, and stellar velocity dispersion - should remain consistent. We present a novel optical spectral-fitting approach that combines pPXF and PyQSOFit to robustly decompose spectra into stellar and AGN components. We apply this technique to three SDSS samples with repeated optical spectra of the same objects at z<0.55: 32 changing-look AGN in bright and dim states, and 15 quasars and 15 galaxies with three single-epoch and one stacked spectrum each. To compare with the literature, we use SDSS spectra and photometric data from AGN in the eFEDS field, as well as Gemini and VLT observations of some of our changing-look AGN. We evaluate the reliability of stellar mass, velocity dispersion, and black-hole mass measurements, especially in relation to the AGN-to-total continuum contribution (fAGN). For host-derived properties, especially when fAGN<0.8, our method yields consistent results. For single-epoch black-hole mass estimates from Ha and Hb, 3-sigma confidence in the broad-line flux and FWHM provides effective criteria for selecting reliable measurements. After applying these quality cuts, measurements across different epochs agree within uncertainties, and their reliability is confirmed by the alignment with previously established scaling relations. Many changing-look AGN in our sample exhibit "breathing" broad-line regions, as determined from Ha analysis, while some deviate significantly, suggesting non-virialized systems across the spectral transition.

Cygnus X-3 is a microquasar composed of a compact object of unknown nature closely orbiting around a Wolf-Rayet star. The particularities of this source make it a unique case among microquasars. This fact, together with its recent establishment as a PeV particle accelerator, makes Cygnus X-3 a very interesting target for the investigation of the physical processes leading to gamma-ray production. In this work, the TeV and GeV gamma-ray emission of Cygnus X-3 is studied in order to determine its origin and constrain the properties of the system. For that purpose, a point-like analysis of 130 h of data taken with the MAGIC telescopes between 2013 and 2024 was performed, which represents the largest available sample for Cygnus X-3 at $\sim$TeV energies. Additionally, contemporary data from Fermi-LAT were also analysed to better contextualize the MAGIC observations. For a more detailed investigation of the source physics, the data were divided into three subsets according to the flaring state of the source and orbital phase. No significant detection of Cygnus X-3 is found between 0.1 and 7 TeV for any of the datasets, and differential and integral flux upper limits are reported over the long-term monitoring of the source. The Fermi-LAT fluxes can be considered compatible with previous results, taking into account the different data samples used across studies. The MAGIC upper limits presented in this work represent the most constraining ones up to date at $\sim$TeV energies. An eventual detection of Cygnus X-3 at these energies would significantly constrain the source properties, and is not unreasonable to expect given that the source has already been detected in both the GeV and PeV regimes during flaring states. Further observations of Cygnus X-3 at energies above tens of GeV would be valuable for this purpose.

The blazar 3C 279 is well known for its rapid and large-amplitude variability. On 20 December 2013, the source exhibited an orphan γ-ray flare characterized by a flux-doubling timescale of a few hours, a very hard spectrum, a time-asymmetric light curve with a slow decay, and no significant optical variability. We propose a new interpretation of this event based on a two-zone scenario in which a stationary emission region produces the quiescent emission, while a second zone accelerates within the broad-line region(BLR). We compute the time-dependent radiative output of both zones with the EMBLEM code, including synchrotron, synchrotron self-Compton, and external inverse-Compton processes, as well as bulk acceleration, adiabatic expansion, and a Fokker-Planck treatment of the electron distribution. This is the first attempt to precisely model the asymmetric γ-ray flux evolution during this flare. A model with a stationary region outside the dusty torus and an accelerating plasma blob reproduces the main features of the event: a short and intense γ-ray flare with a hard spectrum and no optical counterpart. The flare results from the variation of the external photon field in the blob frame as the blob crosses the BLR and reaches its terminal Lorentz factor not far from the inner radius of the BLR. Bulk acceleration and the propagation of a plasma blob within the jet provide a natural mechanism for producing high-energy flares and asymmetric light curves without requiring an ad hoc time-dependent particle injection. The model predictsa delayed EUV/X-ray enhancement once the blob exits the BLR. No very-high-energy data are available for this event, but if γ-rays were emitted in this band, a delay would be expected with respect to the Fermi-LAT flare.

The long-standing assumption of symmetric radio emission beams from the two magnetic poles of pulsars is challenged by observational evidence of asymmetry and underfill. Direct testing of this symmetry remains difficult for most pulsars. As an indirect test, we collected polarization profiles of 11 interpulse pulsars observed with the Five-hundred-meter Aperture Spherical radio Telescope, MeerKAT, and Parkes. We developed a rotating vector model incorporating aberration and retardation effects to fit the position angle swings of selected pulsars, thereby determining the intrinsic emission region corresponding to the observed pulse windows. Based on both the conal and fan beam models, we compared three key parameters-beam radius, magnetic azimuth width, and emission intensity-between the intrinsic emission regions of the main pulse and interpulse. Among the eight pulsars with a confirmed double-pole geometry, none exhibits similarity in the azimuth width. Only two show potentially similar beam radii, while six demonstrate comparable emission intensities within specific parameter spaces. These results indicate that the emission beams from the two magnetic poles of a pulsar may be generally dissimilar in size, suggesting that the physical conditions governing pair production and particle acceleration differ between the two poles. The random distribution of active emission regions further implies inhomogeneity within the polar cap, which may originate from the differences in local magnetic field structure or surface properties.

Extended gamma-ray sources provide significant information about particle propagation. 4FGL J1626.0-4917 was labeled as an unassociated source in 4FGL catalog without known counterparts at other wavelengths. We report an analysis on 4FGL J1626.0-4917 with 17 years Fermi Large Area Telescope data and archival Chandra X-ray Observatory data. We find extended GeV emission around this source, which can be modelled by a Gaussian disk of 0.28 degree radius with a significance of the extension of 7.2 sigma. The gamma-ray spectrum of 4FGL J1626.0-4917 has a photon index of 2.73. The gas content, including molecular, neutral and ionized gas, was investigated and the potential hadronic origin is discussed. The diffuse GeV gamma-ray emission may likely originate from the interaction between accelerated protons in 4FGL J1626.0-4917 and the target proton in surrounding gas, although the leptonic process cannot be ruled out. The X-ray spectral analysis was performed, which reveal a point source inside 4FGL J1626.0-4917. We investigate potential counterparts, including the stellar cluster NGC 6134 and the supernova remnant G335.2+0.1. Our results highlight the complexity of unidentified extended gamma-ray sources and the need for further observations.

We present a comprehensive analysis of dense cores and filamentary structures in the M16 Eagle Nebula using high-resolution ($11.7^{\prime\prime}$) surface density and temperature maps derived from \textit{Herschel} observations. Using the \textit{hires} algorithm for map construction and the \textit{getsf} method for source and filament extraction, we identified 233 cores and 111 filaments in this massive star-forming region. The filaments exhibit a median width of 0.4\,pc -- and a median linear density of 61\,$M_\odot$\,pc$^{-1}$, with 76\% being supercritical for gravitational fragmentation. Our radial analysis of the $\sim$60\,pc diameter shell driven by the central NGC 6611 cluster reveals strong enhancements in structure formation: filament formation efficiency (FFE) is 2.3 times higher within the shell (peaking at 22\%), while core density shows a concurrent 1.5-fold enhancement. The moderate correlation between core density and FFE ($r=0.67$) indicates coupled formation processes. Theoretical analysis demonstrates that observed surface densities exceed the critical threshold for fragmentation by a factor of $\sim$8, with a fragmentation timescale ($\sim$1.5--2.0\,Myr) comparable to the shell's dynamical age ($\sim$1.0--1.3\,Myr), indicating we are observing fragmentation in progress. These results reveal a hierarchical fragmentation sequence -- shell compression $\rightarrow$ filament formation $\rightarrow$ core formation -- providing clear observational evidence for positive feedback where massive star formation triggers secondary structure formation in the surrounding molecular cloud.

We present a model-independent, photometry-only framework for identifying strongly lensed supernovae when multiple images are unresolved and blended into a single point source. Building on the simulation-based methodology of Bag et al. (2021), we apply this approach to real Zwicky Transient Facility (ZTF) data using a validation sample of spectroscopically confirmed Type Ia supernovae. The method models the observed flux as a superposition of two time-shifted components, and Bayesian inference is used to estimate the relative scaling and time delay. Applying this framework to 445 well-converged supernovae, we find that only a single object satisfies the selection criteria when adopting a conservative threshold of $Δt \ge 12$ days, corresponding to a false positive fraction of $1/445 \approx 0.22\%$. A laxer threshold of $Δt \ge 10$ days yields fourteen objects, for a false positive fraction of $3.15\%$. The method provides a scalable and model-independent first-stage filter for identifying lens-like candidates in large time-domain surveys such as the Rubin Observatory's Legacy Survey of Space and Time (LSST).

We analyzed 19 years of $R$-band data of the blazar 3C 454.3 from the Whole Earth Blazar Telescope (WEBT) archive, along with new data from its members and from public archives such as those provided by the Small and Moderate Aperture Research Telescope System (SMARTS) and the Steward Observatory projects to search for quasi-periodic oscillations (QPOs). We detected a QPO of $\sim$ 433 days using Lomb-Scargle periodogram, which lasted from MJD 54980--58450 as detected by the weighted wavelet Z-transform technique, making it one of the most persistent QPOs ever detected in the optical regime. The phase dispersion minimization technique was also performed to further validate this QPO claim. We detected this signal at a global significance of $2.53σ$ across all methodologies. To explain the observed QPO, we have considered both models focused on the accretion disk around the super-massive black hole (SMBH), and those based purely on jet emissions. Plausible jet-based models involve a shock moving down the jet in a helical magnetic field, whereas the SMBH models could involve Lense-Thirring effect-induced jet precession or dual jets in a binary SMBH system. We introduce a novel approach to distinguish genuine QPOs from spurious signals arising from annual seasonal gaps, a common limitation of ground-based observations.

We present an extensive X-ray spectral variability study of the TeV photon-emitting high-energy-peaked BL Lacertae object PG 1553+113, using the data from EPIC-PN camera of XMM-Newton, which observed the source during its operational period from Sep 2001 to Nov 2024. X-ray spectra in this energy range, $0.6-7.0$ keV, were fitted with absorbed Power-law (PL) and absorbed Log-Parabola (LP) models. We found with 99$\%$ confidence that 14 of them were fit well by LP models having parameters in the range $α\simeq2.13-2.80$, and $β\simeq0.04-0.18$, one spectrum favours a LP model with $β<0$, while simple PL models with $Γ\simeq2.53-2.69$ were sufficient to describe the X-ray spectra of the remaining 15. Two of these 30 observations showed strong signatures of an additional inverse Compton component, while one showed weaker indications. On fitting joint Optical Monitor and EPIC-PN data with LP models, we found synchrotron peaks in the energy range of $ν_s\simeq4.59-48.61$ eV. This indicates that the spectral evolution is probably caused by variations in particle acceleration or cooling conditions within the jet.

We report the detection of linear polarization in the radio afterglow of GRB 260310A, representing the first centimeter-wavelength polarization detection of a gamma-ray burst (GRB) afterglow and the first measurement of Faraday rotation in a GRB environment. We detect linearly polarized emission across $11-25$ GHz, with a polarization fraction decreasing monotonically from $(3.18 \pm 0.18)\%$ at 25 GHz to $(0.69 \pm 0.22)\%$ at 11 GHz. Interpreting the radio data as emission from a reverse shock in a structured, relativistic jet, the observed depolarization toward lower frequencies is consistent with suppression by synchrotron self-absorption, while the low observed polarization at high frequencies relative to the theoretical maximum suggests a patchy magnetic field in the jet with a coherence scale, $θ_{\rm B}\approx10^{-2}$ rad. We identify a frequency-dependent rotation of the polarization angle consistent with Faraday rotation, with a rotation measure of ${\rm RM} = -(8300 \pm 90)~\rm{rad/m^2}$ at the GRB redshift. The magnitude of the rotation measure is consistent with propagation through a dense, magnetized environment, such as a progenitor HII region. These findings demonstrate that GRB afterglows exhibit measurable linear polarization at centimeter wavelengths, and that their polarimetric properties probe both intrinsic jet magnetization and the surrounding medium. Future multi-frequency polarimetric monitoring over timescales of days to weeks will enable detailed studies of the evolution of magnetic field structure and provide new constraints on the role of magnetic fields in GRB afterglows.

We investigate the physical origin of critical mass, a threshold where many galaxy properties and scaling relations undergo fundamental transitions, using the Horizon Run 5 simulation. Focusing on massive ($M_{\rm tot} \geq 10^{12}{\rm M_\odot}$) central galaxies, we examine the mass-dependent turnover of the stellar-to-total mass ratio (STR) and the physical processes driving it. We decompose STR into the stellar-to-baryon mass ratio ($M_*/M_{\rm bar}$) and baryon retention fraction ($M_{\rm bar}/M_{\rm tot}$) to examine galaxies' ability to retain baryons and convert them into stars. We find that STR evolution is dominated by variation in $M_*/M_{\rm bar}$, which changes by over a factor of three, peaking within a narrow range of $M_{\rm tot} \sim 10^{12.4\text{--}12.7}{\rm M_\odot}$ independent of redshift, while $M_{\rm bar}/M_{\rm tot}$ varies by at most 30%. A redshift-independent critical mass at $M_{\rm tot} \sim 10^{12.5}{\rm M_\odot}$ ($M_* \sim 10^{10.7}{\rm M_\odot}$) arises from the changing nature of gas accretion. At this scale, a dynamically stable hot gas halo develops that suppresses cool gas inflow, reducing in-situ star formation efficiency such that $M_{\rm tot}$ growth exceeds in-situ $M_{*}$ growth. Consequently, the hot gas reservoir grows while $M_{*}$ growth slows, producing upturns in $M_{\rm gas}/M_{\rm tot}$ and $M_{\rm bar}/M_{\rm tot}$ and a downturn in $M_{*}/M_{\rm bar}$ that ultimately drives the STR turnover. We also identify a secondary critical mass at $M_{\rm tot} \approx 10^{11}{\rm M_\odot}$ (or $M_{*} \approx 10^{9\text{--}9.5}{\rm M_\odot}$) where gas retention fraction peaks, above which increasing hot gas fraction gradually suppresses in-situ star formation efficiency.

We present a comprehensive analysis of the 21 cm intensity mapping (IM) data from the Tianlai Cylinder Pathfinder Array (TCPA), focusing on multi-scale foreground mitigation and three-dimensional power spectrum estimation. Utilizing 20 days of drift-scan observations (714.4-781.7 MHz, corresponding to HI emission at redshift $z \approx 0.82-0.99$), we reconstruct high-fidelity sky maps by incorporating a high-precision, drone-measured primary beam model. This in-situ calibration significantly enhances reconstruction accuracy over previous analytical approximations. To address astrophysical foregrounds, which exceed the cosmological signal by approximately five orders of magnitude, we implement a robust multi-scale subtraction strategy--mPCA-UWTS--which combines an isotropic Undecimated Wavelet Transform on the Sphere (UWTS) with independent Principal Component Analysis (PCA) within each wavelet domain. We subsequently estimate the 3D power spectrum via Spherical Fourier-Bessel (SFB) decomposition, providing a mathematically rigorous treatment of wide-angle and line-of-sight curvature effects inherent in wide-field surveys. Our analysis demonstrates that the SFB framework effectively isolates systematic contaminants and recovers the clustering signal without the biases introduced by conventional flat-sky approximations. This work represents the first application of the SFB formalism to observational 21 cm IM data, establishing it as a computationally efficient and scalable diagnostic tool for the next generation of wide-field 21 cm surveys, including the Square Kilometre Array (SKA) and the full Tianlai array.

We report the indication of a long-term quasi-periodic oscillation (QPO) in the $γ$-ray emission of the BL Lac object 4FGL J1037.7+5711 (GB6 J1037+5711) using more than 17 years of monthly binned Fermi-LAT observations. Since blazar $γ$-ray variability is typically dominated by stochastic red-noise processes from turbulent jet activity and accretion fluctuations, we applied multiple independent timing techniques to test periodic modulation. These include the Lomb--Scargle periodogram (LSP), weighted wavelet $Z$-transform (WWZ), first-order autoregressive red-noise modeling (REDFIT), and epoch-folding analysis. The LSP reveals a significant periodic signal at $478.74 \pm 17.55$ days, exceeding the $99.99\%$ confidence level from Monte Carlo simulations. The WWZ analysis independently recovers a comparable period of $474.72 \pm 27.24$ days at $99.7\%$ significance, while the REDFIT analysis identifies a similar periodic feature at $481.67 \pm 35.41$ days above the $99\%$ confidence level. The epoch-folding analysis further confirms the same modulation timescale. The small differences in period estimates across methods are expected given the distinct mathematical frameworks and sensitivity functions. The long-term flux distribution of the source is better described by a lognormal profile than a Gaussian, suggesting that the underlying variability arises from multiplicative processes. The indication of a $\sim$478 day QPO, independently confirmed across all four timing techniques, may be associated with the orbital dynamics of a supermassive binary black hole system driving Newtonian jet precession or periodic Doppler-factor modulation, Lense--Thirring precession of the inner accretion disk around a rapidly spinning SMBH, or accretion-driven instabilities at the disk--jet interface amplified through relativistic beaming.

Massive stars (> 8 $M_\odot$) are known to have high degrees of multiplicity, e.g., with about 60% in triples or higher-order multiples. Such high levels of multiplicity may arise during formation (primary multiplicity) or through dynamical processing of already formed stars in dense clusters (secondary multiplicity). The level of primary multiplicity is an important metric to help distinguish between different formation scenarios, such as core accretion and competitive accretion. The level of secondary multiplicity is expected to evolve with time and be sensitive to local cluster environment. Here we analyze a suite of $N$-body simulations to study bound multiplicity and local projected stellar density, $N_*$, around massive stars within gradually forming star clusters with 50% primordial binaries in the Turbulent Clump Core Accretion (TCCA) paradigm. We find that massive stars rapidly gather triple or higher-order bound companions and enhancements in local $N_*$ via dynamical processes. We study these metrics as a function of environment in a given cluster, quantifying the increasing multiplicity that arises towards cluster centers. We find that secondary multiplicity tends to decrease in more massive clusters due to their higher velocity dispersions, but rises as the mean density of the bound cluster increases. We find our $N_*$ radial profiles are shallower compared to those in the STARFORGE simulations, which form massive stars via competitive accretion. A comparison to the AFGL 5180 system suggests it is better described by TCCA models. However, a larger number of observed systems is needed to better discriminate between these formation models.

This is the fourth paper of our series investigating the effects of active galactic nucleus (AGN) feedback in the evolution of an elliptical galaxy using the {\it MACER} framework. While previous works considered only AGN radiation and wind, we now add jet feedback. The values of the jet parameters are taken from small-scale general relativity MHD simulations of black hole accretion. We run three models: {\tt FullFeedback}, {\tt JetOnly}, and {\tt WindOnly}. Time-averaged star formation rates are $10^{-1}$, $10^{-2}$, and $10^{-3} \mathrm{M}_\odot\,\mathrm{yr}^{-1}$ in {\tt JetOnly}, {\tt WindOnly}, and {\tt FullFeedback}, respectively. Despite the higher jet power, jet feedback is less efficient than wind due to a small opening angle and low momentum flux. The much lower star formation rate in {\tt FullFeedback} indicates nonlinear coupling between jet and wind, with stronger suppression than the linear sum. The AGN energy dissipation efficiency values (fraction of injected kinetic energy dissipated via turbulence and shock) are 0.64 ({\tt FullFeedback}), 0.48 ({\tt WindOnly}), and 0.26 ({\tt JetOnly}). In the {\tt FullFeedback} model the wind-jet shear results in Kelvin-Helmholtz instability, driving stronger turbulence that effectively converts AGN kinetic energy into heating.

In this work, we aim to advance the Universal Rotation Curve (URC) paradigm by leveraging new data and extending its observational domain. Building on previous studies that established the URC using optical rotation curves reaching the galaxy optical radius $R_{opt}$, we exploit the SPARC sample's extended HI rotation curves to construct the URC out to $2R_{opt}$. This crucial extension enables us to investigate the mass distribution of spiral galaxies in a region dominated by dark matter an important step to better constrain galaxy mass models and to explore the nature of the dark matter particle. We find that the URC constructed from the SPARC sample's extended HI rotation curves maintains its universal character out to $2R_{opt}$, with the double-normalized rotation curves collapsing onto a single profile. This extended URC provides new insights into the interplay between baryonic matter and dark matter in shaping galaxy rotation curves, particularly in the outer regions where dark matter dominates. Our results not only reinforce the URC paradigm but also refine our understanding of the mass distribution in spiral galaxies, offering new constraints on galaxy mass models and implications for the nature of dark matter.

A merger of clusters naturally drives shocks with Mach number $\mathscr{M}\lesssim 3$ in the intra-cluster medium (ICM). This process creates several distinct signatures, including sharp surface brightness "edges", temperature, and gas velocity jumps. The low density of the ICM implies that the ionization balance and electron-ion equilibration times can be long enough to produce a set of additional observable signatures. Here, we focus on two "transient" spectral signatures accessible with the high-energy-resolution telescopes such as XRISM, even for unfavorable geometry, e.g., when we are looking inside the Mach cone of the shock, precluding the appearance of sharp edges in X-ray images. In this work, we focus on (i) the $\mathtt{Z/W}$ line ratio of the Fe~XXV triplet and (ii) the contribution of ions with $T_i>T_{\rm e}$ to the line width, which might be mistakenly interpreted as the gas turbulence. We demonstrate that the $\mathtt{Z/W}$ ratio can serve as a proxy for the non-equilibrium state of the shocked ICM and facilitate interpretation of the line broadening. We conclude that these spectral signatures are within reach with missions like XRISM and can be used to constrain the heating of electrons at the collisionless cluster shocks, as well as the rate of subsequent temperature equilibration between different particle species.

XRISM observations to date have shown that gas kinetic pressures in the intracluster medium (ICM) tend towards the low end of predictions from cosmological simulations. Here, we present a XRISM observation of the merging cluster Abell 2034, which exhibits the broadest emission lines yet observed in a galaxy cluster. We measure a velocity dispersion of ~470 km/s, corresponding to a kinetic pressure fraction of ~15%. This places A2034 at or above the high end of the theoretical predictions for similar-mass clusters. This large velocity dispersion may reflect Mach ~0.5 turbulence in the ICM and/or result from a core disruption driven by the ongoing head-on merger. We also detect a ~380 km/s gas bulk velocity gradient along the merger axis with an opposite sign to the galaxy velocity gradient, indicating a decoupling of the cluster galaxies (and dark matter) from the ICM. Finally, we report tentative evidence of several spectral anomalies, including a suppressed Fe He$α$-z line, an enhanced Fe Ly$α$-2 line, and a potential absorption feature at ~8.7 keV. The first two features may be explained by the combination of a multi-phase ICM and a non-equilibrium ionization state in the wake of a merger shock. Deeper XRISM observations of this cluster are required to confirm these features. This work highlights the importance of kinematic measurements across a large sample of merging clusters as well as the need for deep XRISM observations to unveil more exotic physics in the ICM.

We present Northern Extended Millimeter Array (NOEMA) observations of the CO (2-1) molecular gas kinematics in the nearby Compton-thick Seyfert 2 galaxy NGC 3079, with an angular resolution of 0.5" ($\sim$40 pc). To interpret the observed CO (2-1) kinematics, we model the rotating disk using two software tools, 3D-Barolo and DysmalPy, to generate mock 3D data cubes. Both models indicate, in addition to the rotating disk, the presence of a spatially unresolved nuclear component characterized by high velocity dispersion. Analysis of the visibility data reveals that the blue-shifted, high-velocity component is spatially offset from the continuum peak by 0.17" ($\sim$ 14 pc) and exhibits line-of-sight velocities of $v$ - $v_{sys}$ = -350 to -450 km s$^{-1}$, which we interpret as a nuclear molecular outflow. We calculate a molecular gas mass outflow rate of 8.82 $M_\odot$ yr$^{-1}$, with a kinetic power ($\dot{E}_{\text{out}}$) of 3.8 $\times$ 10$^{41}$ erg s$^{-1}$ and a momentum rate ($\dot{p}_{\text{out}}$) of 2.05 $\times$ 10$^{34}$ Dyne. The momentum rate exceeds the AGN radiation momentum rate by a factor of $\sim$15, suggesting an energy-driven outflow. Furthermore, we argue that the derived kinetic power of the nuclear molecular outflow favors a jet-powered scenario that explains the slowdown and brightening of the parsec-scale radio source observed with the Very Long Baseline Array.

Recent observations of protostars with the James Webb Space Telescope have revealed unprecedented chemical complexity from their ice absorption features. However, these spectra are likely influenced by radiative transfer effects, and there is little understanding of how this impacts our ability to identify, quantify, and interpret the observed ice features. We have developed a new modeling framework to investigate the radiative transfer through icy protostellar envelopes, and apply this to the IRAS 15398-3359 protostar observed by the JWST CORINOS program. The modeled H$_2$O and CO column densities are similar to previous empirical studies, but we require a high CO$_2$/H$_2$O ratio of 76% to match the optical depth of the 15 $μ$m band. We use our modeled continuum to calculate a 6-10 $μ$m optical depth spectrum, and see considerable differences compared to a simple polynomial continuum model, underscoring the challenges with quantifying trace ice species in this range. For this source, we find that the observed absorption predominantly originates along the viewing line of sight between 1000 - 2000 au, peaking at the transition from the outflow cavity to the envelope; the spectra are largely insensitive to absorption from ices in the outer envelope, which extends out to 20,000 au. Lastly, we show that depending on how the line of sight intersects the cavity, the apparent CO$_2$/H$_2$O and CO/H$_2$O column density ratios can be underestimated compared to the underlying ice abundance ratios. Together this provides important context for interpreting the ice constraints derived from JWST observations of protostars.

Giant radio halos are the Mpc-scale extended sources associated with the merging clusters, while the mini-halos are preferentially associated with cool-core clusters. Both trace the ICM plasma physical process, and recent low-frequency observations increasingly blur the distinction between the two classes. We present the first multi-frequency spectral analysis of the galaxy cluster RXCJ0232--4420, hosting a cool core, using uGMRT (400 and 650 MHz) and MeerKAT (1283 MHz) observations. The central radio emission extends beyond $\sim 1$ Mpc at all frequencies, confirming it as a giant radio halo. One candidate relic (in the east) has also been detected, with an extent of $\sim 300$ kpc. The integrated spectral indices of halo and candidate east relic are $α= -1.17 \pm 0.17$, and $α= -0.85 \pm 0.17$, respectively. The resolved spectral map of the halo is mostly uniform ($-1.0$ to $-1.3$) and does not show any radial steepening. The radio surface brightness profile is well modelled by a single exponential law, with the e-folding radius constant across frequencies. The radio halo emission is morphologically well correlated with the thermal emission. Point-to-point radio-X-ray correlation analysis gives a sublinear relationship (slope $\sim 0.80$), with no frequency evolution. The presence of Mpc-scale emission in the cool-core cluster shows that such emission can arise in dynamically intermediate systems. Our results demonstrate that merger-driven turbulence, even from minor disturbances, can sustain cluster-wide particle re-acceleration without destroying the cool core.

Understanding the role of galactic scale bars in disk galaxy evolution requires detailed measurements of bar properties across galaxies hosting bars at many redshifts. We present measurements of bar lengths and widths in a sample of 8230 disk galaxies from Hubble Space Telescope (HST) Legacy surveys. The highest-redshift barred galaxies in the sample have $z \sim 3$; most have $z \leq 1$. Using a mass-complete sample from the COSMOS field, we examine bar properties and evolution within $0.25 < z < 1$ in galaxies with stellar mass $\log(M_{\ast}/M_{\odot}) \geq 9.5$. The lowest-mass galaxies in our sample have similar star formation rate (SFR) distributions whether or not they host bars. For galaxies with $\log(M_{\ast}/M_{\odot}) \geq 10$, barred galaxies are more likely to be quiescent or quenched, consistent with bars mainly participating in slow quenching processes. The median physical bar length increases with increasing stellar mass. Relative bar lengths and widths (as a fraction of disk radius) peak at stellar mass $\log(M_{\ast}/M_{\odot}) \sim 10.25$, and change together with mass such that the median ratio, a proxy for bar strength, does not significantly change with stellar mass. Bars in our sample tend to be slightly ($\approx 13$%) weaker at higher redshift. Quiescent and quenching galaxies have longer and wider bars than those in galaxies on or above the star-forming sequence, especially at lower redshift and higher masses; at the low-mass end of our sample, starburst galaxies host relatively longer and stronger bars. Our findings are consistent with other results from studies at both higher and lower redshift, cementing the fundamental importance of bars in disk galaxy evolution.

Fluid equations are nonlinear, dissipative, and non-Hamiltonian, which makes their relation to Schrödinger evolution and quantum algorithms nontrivial. We derive an exact Eulerian Cole-Hopf-type reformulation of isothermal compressible Navier-Stokes (NS) flow in Schrödinger-type amplitude variables. To our knowledge, this gives the first exact Cole-Hopf-type Schrödinger-variable reformulation of compressible NS flow. In two dimensions, a Helmholtz decomposition separates the velocity into compressive and vortical potentials, whose logarithmic transforms yield two scalar imaginary-time Schrödinger-type equations with nonlinear self-consistent potentials. We show that the mixed density-compressive amplitude $Ψ_α=ρ^αΘ^{1-2α}$, where $ρ$ is the density, $Θ$ is the compressive amplitude, and $α\neq 0,\,1/2$, satisfies a nonlinear Schrödinger-type equation with a vector-potential-coupled Laplacian. The transformed system is exactly equivalent to compressible NS and is nonlocal only through Helmholtz and Poisson projections. In three dimensions, the density-carrying equation retains the same vector-potential-coupled structure, while the solenoidal sector admits a compressible analogue of Ohkitani's incompressible NS Cole-Hopf formulation. Unlike unitary hydrodynamic Schrödinger-flow representations, the present equations are imaginary-time heat or drift-diffusion equations with self-consistent potentials, but they remain an exact change of variables for compressible NS. A two-dimensional Kelvin-Helmholtz unstable shear-layer calculation verifies the transformed equations against a direct compressible NS simulation. The formulation exposes operator structures that may be useful for reduced flow descriptions, quantum algorithms for operator evolution, and quantum partial differential equation solvers.

A high-resolution ($\sim$45000), high signal-to-noise ($>$100) K-band spectral atlas of massive stars is presented. It includes 81 stars consisting of known optical standards, spanning spectral and luminosity subclasses from O2 to O9, and supergiant luminosity and spectral subclasses from O2-B1. The telluric-corrected reduced spectra are publicly available, and are discussed here.

Measuring the properties of cold molecular gas available for intense star formation in galaxy protoclusters at $z>2$ is a crucial step in understanding large scale structure formation. We present ALMA observations of CO(3$-$2) in five dusty star-forming galaxies within $\sim0.5-4$ cMpc of the core of the Spiderweb protocluster at $z=2.16$ to measure the molecular gas mass and kinematics in the most starbursting members of the protocluster. All five galaxies exhibit evidence for disturbed kinematics including non-Gaussian CO line profiles, irregular spatial morphology, and strong residuals when fitting the galaxies with a classical disk model. This could be indicative of an elevated merger rate in the outskirts of the mature Spiderweb protocluster, as all of the galaxies in our sample have multiple companions detected in H$α$. Both the gas fractions and the gas depletion timescales of the galaxies are similar to field relations at cosmic noon, indicative of the fact that their prodigious star formation rates are compensated by similarly high gas masses. The most massive galaxies, as well as all of the galaxies identified as X-ray AGN in previous works, have gas fractions $<30$%, compared to the sample average of 49%, indicating declining availability of gas for star formation. Finally, we find that the gas fractions and specific star formation rates decline with distance from the Spiderweb Galaxy, supporting the reversal of the SFR density--radius relation in high-redshift protoclusters.

New observations from the Gaia spacecraft have traced an emerging demographic of low-mass associations disconnected from larger associations or GMCs. The first of these associations were recently characterized, but the star-forming environments they trace remain unknown. Using new velocities and ages alongside literature catalogs, we uncover the origins of 16 low-mass associations ($M\lesssim100$ M$_{\odot}$, $τ\lesssim50$ Myr) using dynamical traceback. We reveal that three groups of currently disparate populations share common formation sites, comprising the Leo, CaNMoS, and AquENS associations. Twelve of 16 associations have plausible connections to larger complexes, six of which form while moving outward from well-established multi-generational star-forming events that drive known or suspected bubbles. We find that feedback from the oldest co-spatial and co-moving relatives of these associations can explain the current morphologies of the Local and Orion-Eridanus Bubbles, along with the formation of related associations like Sco-Cen and Orion OB1. Most remaining populations show evidence for triggered star formation. In the Leo Association, high vertical velocities and a deceleration signature suggest that it formed out of an intermediate velocity cloud colliding with gas in Orion, which would make it the first known case of star formation in one of these clouds. The other newly defined associations show similar asymmetric velocity signatures, such as CaNMoS, which may trace bubble-driven acceleration or a cloud collision. We conclude that the lowest-mass young associations remain undiscovered, and that these populations may have a critical role revealing the small gas overdensities that trace the processes sculpting galactic star formation.

We present a detailed study of radio-detected dwarf galaxies (with stellar masses less than 3 billion solar masses) to characterize extreme star formation and search for (variable) radio AGNs. Our sample comes from Reines et al. (2020) (arXiv:1909.04670), who used the Karl G. Jansky Very Large Array (VLA) with 0.25 arcsecond resolution to observe 111 dwarf galaxies with lower-resolution (5 arcsecond) detections in the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) survey. While that work identified and focused on 13 compact radio AGN candidates in dwarf galaxies, here we focus on 16 compact radio sources consistent with star formation in dwarf galaxies. We find that these objects are dominated by thermal HII regions with ages less than 10 Myr, and the most extreme sources have ionizing luminosities requiring the equivalent of around 10,000 to 100,000 O-type stars. We also investigate the dwarf galaxies detected in FIRST but not detected in the high-resolution follow-up observations. Using the infrared-radio correlation parameter, we identify eight sources consistent with radio-excess AGNs. Five of these objects plus another 15 dwarf galaxies have no corresponding detections in the VLA Sky Survey (VLASS) indicating variability between the FIRST and VLASS observations. The FIRST radio luminosities of these sources are significantly higher than expected for supernova-related emission, suggesting the radio variability is likely associated with AGNs. Together, these results provide new context for the presence of compact star formation and massive black holes in dwarf galaxies, and highlight the utility of radio variability and multi-resolution data for identifying the dominant power sources in low-mass systems.

IRAS 13224-3809 is one of the most intensively studied narrow-line Seyfert 1 galaxies, with a rich literature reporting diverse and sometimes contrasting interpretations of its complex X-ray spectra and variability. Notably, a fast and variable ultra-fast outflow (UFO) was discovered in this source, sparking debate over its nature and driving mechanisms. Motivated by these open questions, we present a systematic, time- and flux-resolved reanalysis of the full 2016 XMM-Newton (1.5 Ms) and NuSTAR (500 ks) datasets, employing careful background treatment and equal-count spectral selections. We uniformly apply three spectral models, including photo-ionized absorption, broad emission, and relativistic reflection, to all intervals. We unambiguously confirm the presence of a strong, variable outflow with velocities exceeding 0.2$c$, and find that models including absorption consistently reveal robust physical trends: a velocity-luminosity correlation of the UFO, persistently large line widths, and no compelling equivalent-width-flux anti-correlation. When emission or reflection components are included, the significance of the absorption features decreases, but significant UFO detections remain in most intervals. We also report clear evidence for rapid acceleration of the wind in response to X-ray flares, with the outflow carrying momentum and kinetic power sufficient to drive an efficient AGN feedback. The observed rapid response favors magnetic driving, analogous to coronal mass ejections, over radiative acceleration. Our results reconcile contrasting previous claims and underline the need for high-resolution spectroscopy to resolve the wind substructure. The observed UFO variability and structure are consistent with a multiphase, clumpy wind produced by thermal and hydrodynamic instabilities, with magnetic reconnection providing the rapid acceleration mechanism.

Lopsidedness is common in disk galaxies, yet its origin and evolution remain unclear. Previous studies typically examined stellar and gas asymmetries separately, but a combined analysis offers a stronger probe of the mechanisms driving lopsidedness, recent galaxy evolution, and environment. We analyze the density and kinematics of stellar and atomic hydrogen (HI) components in nine Milky Way type galaxies from the Auriga Superstars cosmological zoom-in simulations. The high stellar mass resolution improves the visibility of disk features while reducing noise, enabling a detailed study of dynamical processes in a cosmological context. Morphological and kinematical lopsidedness are quantified using the first Fourier mode (m=1) of the face-on mass distribution and radial velocity maps, measured consistently for stars and gas between 0.5 and 1 stellar optical radius. At z=0, morphological lopsidedness in old stars (>0.5Gyr) strongly correlates with HI, tracing distortions in the global gravitational potential. In contrast, young stars (<0.5Gyr) trace asymmetric star formation along spiral arms. Stellar morphological and kinematical lopsidedness are strongly correlated, whereas HI shows a weaker correlation, with kinematic asymmetries dominating. We also find an anti-correlation between stellar lopsidedness and bar strength. Strongly barred galaxies tend to host more symmetric disks and higher central stellar mass densities. Tracing lopsidedness evolution over time, tidal interactions with massive satellites (mass ratio >1:50) induce coherent lopsidedness in both stars and HI. In contrast, smooth gas accretion mainly affects HI and young stars, leaving the total stellar component largely symmetric. Overall, these results demonstrate that lopsidedness is a powerful diagnostic of internal disk evolution, gas accretion, and environmental interactions across cosmic time.

Large surveys of galaxies in the local and high-redshift Universe have, traditionally, relied on the intensity of rest-optical emission lines from metal ions in the Interstellar Medium (ISM) to indirectly estimate the O/H abundance in the gas. However, these optical strong line diagnostics are also sensitive to the electron gas temperature ($T_e$), resulting in large systematic uncertainties that inherently limit their utility as metallicity tracers, especially in dust-obscured and metal-rich environments. To this end, we provide the first empirical calibration of Ne$_{23}$, a novel abundance diagnostic using the mid-infrared (MIR) $T_e$-insensitive [Ne II]$λ$12.81$μ$m and [Ne III]$λ$15.56$μ$m fine-structure lines. We present new JWST/MIRI MRS observations of ten H II regions with optical measurements of $T_e$ and O/H from the CHAOS project, and we analyze MIRI observations of eight low-metallicity galaxies with similarly high-fidelity direct O/H. We measure Ne$_{23}$ from 1D MIR spectra extracted from apertures matched to the ground-based spectroscopy used to obtain O/H, a method that is unfeasible from MIR spectra acquired on prior space-based observatories. From these nebulae, Ne$_{23}$ is strongly correlated with O/H over 1.5 dex in 12+log(O/H). We calibrate the O/H-Ne$_{23}$ relation from the empirical data, finding a scatter of just 0.06 dex in O/H at fixed Ne$_{23}$. The O/H-Ne$_{23}$ relation presented here provides a means to reliably estimate 12+log(O/H) from JWST/MIRI MRS observations of ionized nebulae out to z$\approx$0.8, enabling new chemical abundance surveys of highly-attenuated regions and in the metal-rich ISM.

HI-rich starless halos, should they exist, hold great promise for elucidating properties of dark matter halos. This Letter examines the properties of HI-rich failed halos at redshift zero across state-of-the-art cosmological simulations (FIREbox, NIVARIA-LG and Recal-EAGLE). First we compare two numerical analogs with Cloud-9, purported to be the first discovery of a starless HI-rich halo. We argue that differences may be driven by environmental factors, and/or the treatment of gas self-shielding -- which might further limit existing analytic schemes aimed at inferring dark matter halo information from 21 cm HI observations. We also find that the failed halo samples in the three simulations span different regions of the HI-gas-halo mass ($M_{\rm HI}-M_{\rm gas}-M_{\rm 200}$) plane. FIREbox objects occupy a very narrow regime, while NIVARIA-LG extends to a wider range of $M_{\rm 200}$ values - and achieves higher $M_{\rm HI}$ and $M_{\rm gas}$ values. Recal-EAGLE $M_{\rm HI}$ values are similar to FIREbox, albeit with lower gas and halo masses. Lastly, we predict that more HI-rich starless halos can be discovered by exploring the HI-poor regime in the local universe, rather than HI-rich populations at high redshift. Overall, we advocate for the allocation of resources to detect and characterize other HI-rich (and HI-poor) failed halos in the local universe, plus dedicated follow-up spectroscopic observations that scrutinize claims to the absence of a faint stellar component, and that assess their isolation status in detail.

Context. Growing evidence is revealing the crucial role of binarity in massive star evolution. This affects evolution models and demands a refinement of the available observational constraints. Aims. To investigate the possible evolutionary origins of a sample of 117 Galactic O-type stars with luminosity classes V to III and projected rotational velocities below 150 ${\rm km}\,{\rm s}^{-1}$. Methods: We extend previous quantitative spectroscopic analyses performed within the framework of the IACOB project and obtain N abundance estimates. We investigate correlations between these abundances and other stellar parameters. As a reference, we use predictions from single-star evolution models computed using different physical prescriptions. Results. We identify differences in the N abundance distributions corresponding to three He abundance regimes (He-low, He-normal, and He-rich). For the He-normal group, the N abundance distribution peaks slightly above the expected birth value and extends up to $ε_{\rm N}$=8.4 dex. For these stars, we find overall agreement with single-star evolutionary models that include efficient internal mixing and assume moderate-to-low initial rotation. In contrast, the He-rich group exhibits a bimodal N abundance distribution, with one peak at $\sim$8.1 dex and a second more enriched peak around $\sim$8.5 dex; none of these stars are consistent with predictions from single-star evolutionary models. We argue that He-rich stars are most plausibly explained as binary products. Furthermore, despite N abundance in He-normal stars with LC IV and V are reproduced by single stellar evolutionary models with efficient mixing, the same models predict a higher N abundance than observed for stars with LC III. This indicates that rotational mixing alone is unable to explain the observed distribution of N abundances among stars with normal He abundances.