arXiv Daily Digest - 2026-01-27

Attention matrices are fundamental to transformer research, supporting a broad range of applications including interpretability, visualization, manipulation, and distillation. Yet, most existing analyses focus on individual attention heads or layers, failing to account for the model's global behavior. While prior efforts have extended attention formulations across multiple heads via averaging and matrix multiplications or incorporated components such as normalization and FFNs, a unified and complete representation that encapsulates all transformer blocks is still lacking. We address this gap by introducing TensorLens, a novel formulation that captures the entire transformer as a single, input-dependent linear operator expressed through a high-order attention-interaction tensor. This tensor jointly encodes attention, FFNs, activations, normalizations, and residual connections, offering a theoretically coherent and expressive linear representation of the model's computation. TensorLens is theoretically grounded and our empirical validation shows that it yields richer representations than previous attention-aggregation methods. Our experiments demonstrate that the attention tensor can serve as a powerful foundation for developing tools aimed at interpretability and model understanding. Our code is attached as a supplementary.

Grassroots Logic Programs (GLP) is a concurrent logic programming language in which logic variables are partitioned into paired readers and writers. An assignment is produced at most once via a writer and consumed at most once via its paired reader, and may contain additional readers and/or writers. This enables the concise expression of rich multidirectional communication modalities. ``Logic Programs as Types for Logic Programs'' (LICS'91) defined types as regular sets of paths over derivable ground atoms. Here, we define types to be regular sets of moded paths, where a mode captures directionality of communication -- whether a subterm is consumed from or produced to the environment -- enabling the typing of interactive partial computations including those that eventually deadlock or fail, or never terminate. We provide a syntactic definition of well-typing and prove that a program is well-typed iff the path abstraction of its moded-atom semantics satisfies covariance and contravariance conditions with respect to its type. The GLP type system was implemented in Dart by AI, starting from a mathematical specification of Typed GLP (this paper), deriving from it an English spec (written by AI), and from the spec deriving Dart code (by AI). While GLP is naturally untyped, the motivation for Typed GLP comes from programming with AI: Asking AI to program complex communication modalities in GLP (and in general) and hoping for the best is a tenuous strategy. The emerging discipline we advocate and employ is for the human designer and AI to jointly develop and agree upon (1)~GLP types; (2)~GLP procedure type declarations; (3)~informal (English) descriptions of the procedures; and only then let AI attempt to write (4)~GLP code based on those.

We investigate the neural Actor Critic algorithm using shallow neural networks for both the Actor and Critic models. The focus of this work is twofold: first, to compare the convergence properties of the network outputs under various scaling schemes as the network width and the number of training steps tend to infinity; and second, to provide precise control of the approximation error associated with each scaling regime. Previous work has shown convergence to ordinary differential equations with random initial conditions under inverse square root scaling in the network width. In this work, we shift the focus from convergence speed alone to a more comprehensive statistical characterization of the algorithm's output, with the goal of quantifying uncertainty in neural Actor Critic methods. Specifically, we study a general inverse polynomial scaling in the network width, with an exponent treated as a tunable hyperparameter taking values strictly between one half and one. We derive an asymptotic expansion of the network outputs, interpreted as statistical estimators, in order to clarify their structure. To leading order, we show that the variance decays as a power of the network width, with an exponent equal to one half minus the scaling parameter, implying improved statistical robustness as the scaling parameter approaches one. Numerical experiments support this behavior and further suggest faster convergence for this choice of scaling. Finally, our analysis yields concrete guidelines for selecting algorithmic hyperparameters, including learning rates and exploration rates, as functions of the network width and the scaling parameter, ensuring provably favorable statistical behavior.

Interpretability remains a key challenge for deploying large language models (LLMs) in clinical settings such as Alzheimer's disease progression diagnosis, where early and trustworthy predictions are essential. Existing attribution methods exhibit high inter-method variability and unstable explanations due to the polysemantic nature of LLM representations, while mechanistic interpretability approaches lack direct alignment with model inputs and outputs and do not provide explicit importance scores. We introduce a unified interpretability framework that integrates attributional and mechanistic perspectives through monosemantic feature extraction. By constructing a monosemantic embedding space at the level of an LLM layer and optimizing the framework to explicitly reduce inter-method variability, our approach produces stable input-level importance scores and highlights salient features via a decompressed representation of the layer of interest, advancing the safe and trustworthy application of LLMs in cognitive health and neurodegenerative disease.

We consider a setting in which a group of agents share resources that must be allocated among them in each discrete time period. Agents have time-varying demands and derive constant marginal utility from each unit of resource received up to their demand, with zero utility for any additional resources. In this setting, it is known that independently maximizing the minimum utility in each round satisfies sharing incentives (agents weakly prefer participating in the mechanism to not participating), strategyproofness (agents have no incentive to misreport their demands), and Pareto efficiency (Freeman et al. 2018). However, recent work (Vuppalapati et al. 2023) has shown that this max-min mechanism can lead to large disparities in the total resources received by agents, even when they have the same average demand. In this paper, we introduce credit fairness, a strengthening of sharing incentives that ensures agents who lend resources in early rounds are able to recoup them in later rounds. Credit fairness can be achieved in conjunction with either Pareto efficiency or strategyproofness, but not both. We propose a mechanism that is credit fair and Pareto efficient, and we evaluate its performance in a computational resource-sharing setting.

Text-to-SQL has emerged as a prominent research area, particularly with the rapid advancement of large language models (LLMs). By enabling users to query databases through natural language rather than SQL, this technology significantly lowers the barrier to data analysis. However, generating accurate SQL from natural language remains challenging due to ambiguity in user queries, the complexity of schema linking, limited generalization across SQL dialects, and the need for domain-specific understanding. In this study, we propose a Single-Agent Self-Refinement with Ensemble Voting (SSEV) pipeline built on PET-SQL that operates without ground-truth data, integrating self-refinement with Weighted Majority Voting (WMV) and its randomized variant (RWMA). Experimental results show that the SSEV achieves competitive performance across multiple benchmarks, attaining execution accuracies of 85.5% on Spider 1.0-Dev, 86.4% on Spider 1.0-Test, and 66.3% on BIRD-Dev. Building on insights from the SSEV pipeline, we further propose ReCAPAgent-SQL (Refinement-Critique-Act-Plan agent-based SQL framework) to address the growing complexity of enterprise databases and real-world Text-to-SQL tasks. The framework integrates multiple specialized agents for planning, external knowledge retrieval, critique, action generation, self-refinement, schema linking, and result validation, enabling iterative refinement of SQL predictions through agent collaboration. ReCAPAgent-SQL's WMA results achieve 31% execution accuracy on the first 100 queries of Spider 2.0-Lite, demonstrating significant improvements in handling real-world enterprise scenarios. Overall, our work facilitates the deployment of scalable Text-to-SQL systems in practical settings, supporting better data-driven decision-making at lower cost and with greater efficiency.

Virtual Asset Service Providers (VASPs) face a fundamental tension between regulatory compliance and user privacy when detecting cross-institutional money laundering. Current approaches require either sharing sensitive transaction data or operating in isolation, leaving critical cross-chain laundering patterns undetected. We present FedGraph-VASP, a privacy-preserving federated graph learning framework that enables collaborative anti-money laundering (AML) without exposing raw user data. Our key contribution is a Boundary Embedding Exchange protocol that shares only compressed, non-invertible graph neural network representations of boundary accounts. These exchanges are secured using post-quantum cryptography, specifically the NIST-standardized Kyber-512 key encapsulation mechanism combined with AES-256-GCM authenticated encryption. Experiments on the Elliptic Bitcoin dataset with realistic Louvain partitioning show that FedGraph-VASP achieves an F1-score of 0.508, outperforming the state-of-the-art generative baseline FedSage+ (F1 = 0.453) by 12.1 percent on binary fraud detection. We further show robustness under low-connectivity settings where generative imputation degrades performance, while approaching centralized performance (F1 = 0.620) in high-connectivity regimes. We additionally evaluate generalization on an Ethereum fraud detection dataset, where FedGraph-VASP (F1 = 0.635) is less effective under sparse cross-silo connectivity, while FedSage+ excels (F1 = 0.855), outperforming even local training (F1 = 0.785). These results highlight a topology-dependent trade-off: embedding exchange benefits connected transaction graphs, whereas generative imputation can dominate in highly modular sparse graphs. A privacy audit shows embeddings are only partially invertible (R^2 = 0.32), limiting exact feature recovery.

Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and the inability of Parameter-Efficient Fine-Tuning (PEFT) to exploit unlabeled data. While conventional models like U-Net excel in semi-supervised medical learning, their potential to assist a PEFT SAM has been largely overlooked. We introduce SC-SAM, a specialist-generalist framework where U-Net provides point-based prompts and pseudo-labels to guide SAM's adaptation, while SAM serves as a powerful generalist supervisor to regularize U-Net. This reciprocal guidance forms a bidirectional co-training loop that allows both models to effectively exploit the unlabeled data. Across prostate MRI and polyp segmentation benchmarks, our method achieves state-of-the-art results, outperforming other existing semi-supervised SAM variants and even medical foundation models like MedSAM, highlighting the value of specialist-generalist cooperation for label-efficient medical image segmentation. Our code is available at https://github.com/vnlvi2k3/SC-SAM.

We propose a perspective in which learning is an intrinsically dissipative process. Forgetting and regularization are not heuristic add-ons but structural requirements for adaptive systems. Drawing on information theory, thermodynamics, and information geometry, we introduce the BEDS (Bayesian Emergent Dissipative Structures) framework, modeling learning as the evolution of compressed belief states under dissipation constraints. A central contribution is the Conditional Optimality Theorem, showing that Fisher-Rao regularization measuring change via information divergence rather than Euclidean distance is the unique thermodynamically optimal regularization strategy, achieving minimal dissipation. Euclidean regularization is shown to be structurally suboptimal. The framework unifies existing methods (Ridge, SIGReg, EMA, SAC) as special cases of a single governing equation. Within this view, overfitting corresponds to over-crystallization, while catastrophic forgetting reflects insufficient dissipation control. The framework distinguishes BEDS-crystallizable problems, where beliefs converge to stable equilibria, from BEDS-maintainable problems, which require continual adaptation. It extends naturally to continual and multi-agent systems, where viability, stability under adaptation and finite resources replaces asymptotic optimality as the primary criterion. Overall, this work reframes learning as maintaining viable belief states under dissipation constraints, providing a principled lens on forgetting, regularization, and stability.

Lifelong agents should expand their competence over time without retraining from scratch or overwriting previously learned behaviors. We investigate this in a challenging real-time control setting (Dark Souls III) by representing combat as a directed skill graph and training its components in a hierarchical curriculum. The resulting agent decomposes control into five reusable skills: camera control, target lock-on, movement, dodging, and a heal-attack decision policy, each optimized for a narrow responsibility. This factorization improves sample efficiency by reducing the burden on any single policy and supports selective post-training: when the environment shifts from Phase 1 to Phase 2, only a subset of skills must be adapted, while upstream skills remain transferable. Empirically, we find that targeted fine-tuning of just two skills rapidly recovers performance under a limited interaction budget, suggesting that skill-graph curricula together with selective fine-tuning offer a practical pathway toward evolving, continually learning agents in complex real-time environments.

Low-rank adaption (LoRA) is a representative method in the field of parameter-efficient fine-tuning (PEFT), and is key to Democratizating the modern large language models (LLMs). The vanilla LoRA is implemented with uniform ranks, and the recent literature have found that properly allocating ranks on the LLM backbones results in performance boosts. However, the previous rank allocation methods have limitations since they rely on inexplanable and unreliable importance measures for the LoRA ranks. To address the above issues, we propose the ShapLoRA framework. Inspired by the explanable attribution measure Shapley Value, we combine the sensitivity-based measures with the idea of coalitions in the collaborative games among LoRA ranks, and propose a more explainable importance measure called Shapley sensitivity. In addition, we optimize the workflow of the existing works by: (a) calculating Shapley sensitivity on a separate validation set; (b) Setting up the allocating-retraining procedures for fair comparisons. We have conducted experiments on various challenging tasks, and the experimental results demonstrate that our ShapLoRA method can outperform the recent baselines with comparable tunable parameters.\footnote{Codes and fine-tuned models will be open-sourced to facilitate future research.

Self-driving laboratories (SDLs) close the loop between experiment design, automated execution, and data-driven decision making, and they provide a demanding testbed for agentic AI under expensive actions, noisy and delayed feedback, strict feasibility and safety constraints, and non-stationarity. This survey uses soft matter as a representative setting but focuses on the AI questions that arise in real laboratories. We frame SDL autonomy as an agent environment interaction problem with explicit observations, actions, costs, and constraints, and we use this formulation to connect common SDL pipelines to established AI principles. We review the main method families that enable closed loop experimentation, including Bayesian optimization and active learning for sample efficient experiment selection, planning and reinforcement learning for long horizon protocol optimization, and tool using agents that orchestrate heterogeneous instruments and software. We emphasize verifiable and provenance aware policies that support debugging, reproducibility, and safe operation. We then propose a capability driven taxonomy that organizes systems by decision horizon, uncertainty modeling, action parameterization, constraint handling, failure recovery, and human involvement. To enable meaningful comparison, we synthesize benchmark task templates and evaluation metrics that prioritize cost aware performance, robustness to drift, constraint violation behavior, and reproducibility. Finally, we distill lessons from deployed SDLs and outline open challenges in multi-modal representation, calibrated uncertainty, safe exploration, and shared benchmark infrastructure.

Large Vision-Language Models (LVLMs) hold significant promise for medical applications, yet their deployment is often constrained by insufficient alignment and reliability. While Direct Preference Optimization (DPO) has emerged as a potent framework for refining model responses, its efficacy in high-stakes medical contexts remains underexplored, lacking the rigorous empirical groundwork necessary to guide future methodological advances. To bridge this gap, we present the first comprehensive examination of diverse DPO variants within the medical domain, evaluating nine distinct formulations across two medical LVLMs: LLaVA-Med and HuatuoGPT-Vision. Our results reveal several critical limitations: current DPO approaches often yield inconsistent gains over supervised fine-tuning, with their efficacy varying significantly across different tasks and backbones. Furthermore, they frequently fail to resolve fundamental visual misinterpretation errors. Building on these insights, we present a targeted preference construction strategy as a proof-of-concept that explicitly addresses visual misinterpretation errors frequently observed in existing DPO models. This design yields a 3.6% improvement over the strongest existing DPO baseline on visual question-answering tasks. To support future research, we release our complete framework, including all training data, model checkpoints, and our codebase at https://github.com/dmis-lab/med-vlm-dpo.

Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirectional attention to achieve superior global coherence compared to autoregressive models. While recent works have accelerated inference via KV cache reuse or heuristic decoding, they overlook the intrinsic inefficiencies within the block-wise diffusion process. Specifically, they suffer from spatial redundancy by modeling informative-sparse suffix regions uniformly and temporal inefficiency by applying fixed denoising schedules across all the decoding process. To address this, we propose Streaming-dLLM, a training-free framework that streamlines inference across both spatial and temporal dimensions. Spatially, we introduce attenuation guided suffix modeling to approximate the full context by pruning redundant mask tokens. Temporally, we employ a dynamic confidence aware strategy with an early exit mechanism, allowing the model to skip unnecessary iterations for converged tokens. Extensive experiments show that Streaming-dLLM achieves up to 68.2X speedup while maintaining generation quality, highlighting its effectiveness in diffusion decoding. The code is available at https://github.com/xiaoshideta/Streaming-dLLM.

Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocardiogram (ECG). Large Language Models (LLMs) offer a powerful reasoning engine for this task but struggle to natively process these heterogeneous, non-textual data types. To address this, we propose UniPACT (Unified Prognostic Question Answering for Clinical Time-series), a unified framework for prognostic question answering that bridges this modality gap. UniPACT's core contribution is a structured prompting mechanism that converts numerical EHR data into semantically rich text. This textualized patient context is then fused with representations learned directly from raw ECG waveforms, enabling an LLM to reason over both modalities holistically. We evaluate UniPACT on the comprehensive MDS-ED benchmark, it achieves a state-of-the-art mean AUROC of 89.37% across a diverse set of prognostic tasks including diagnosis, deterioration, ICU admission, and mortality, outperforming specialized baselines. Further analysis demonstrates that our multimodal, multi-task approach is critical for performance and provides robustness in missing data scenarios.

LLM agents excel when environments are mostly static and the needed information fits in a model's context window, but they often fail in open-ended investigations where explanations must be constructed by iteratively mining evidence from massive, heterogeneous operational data. These investigations exhibit hidden dependency structure: entities interact, signals co-vary, and the importance of a fact may only become clear after other evidence is discovered. Because the context window is bounded, agents must summarize intermediate findings before their significance is known, increasing the risk of discarding key evidence. ReAct-style agents are especially brittle in this regime. Their retrieve-summarize-reason loop makes conclusions sensitive to exploration order and introduces run-to-run non-determinism, producing a reliability gap where Pass-at-k may be high but Majority-at-k remains low. Simply sampling more rollouts or generating longer reasoning traces does not reliably stabilize results, since hypotheses cannot be autonomously checked as new evidence arrives and there is no explicit mechanism for belief bookkeeping and revision. In addition, ReAct entangles semantic reasoning with controller duties such as tool orchestration and state tracking, so execution errors and plan drift degrade reasoning while consuming scarce context. We address these issues by formulating investigation as abductive reasoning over a dependency graph and proposing EoG (Explanations over Graphs), a disaggregated framework in which an LLM performs bounded local evidence mining and labeling (cause vs symptom) while a deterministic controller manages traversal, state, and belief propagation to compute a minimal explanatory frontier. On a representative ITBench diagnostics task, EoG improves both accuracy and run-to-run consistency over ReAct baselines, including a 7x average gain in Majority-at-k entity F1.

Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal models (SCM). They leverage in-context learning to offer high predictive accuracy in real-world tasks. However, the fairness properties of these foundational models, which incorporate ideas from causal reasoning during pre-training, have not yet been explored in sufficient depth. In this work, we conduct a comprehensive empirical evaluation of TabPFN and its fine-tuned variants, assessing predictive performance, fairness, and robustness across varying dataset sizes and distributional shifts. Our results reveal that while TabPFN achieves stronger predictive accuracy compared to baselines and exhibits robustness to spurious correlations, improvements in fairness are moderate and inconsistent, particularly under missing-not-at-random (MNAR) covariate shifts. These findings suggest that the causal pre-training in TabPFN is helpful but insufficient for algorithmic fairness, highlighting implications for deploying such models in practice and the need for further fairness interventions.

Knowledge distillation with multiple teachers is increasingly used to improve robustness, efficiency, and safety, yet existing approaches rely largely on heuristic or implementation-specific weighting schemes. This paper develops an operator-agnostic axiomatic framework for adaptive weighting in multi-teacher knowledge distillation across three complementary scales: token, task, and context. We formalize structural conditions under which adaptive weighting operators are well-defined, admit multiple non-equivalent implementations, and can be hierarchically composed via product-structure normalization. Within this framework, we establish existence and non-uniqueness of conforming operators, characterize convergence of gradient-based optimization under standard assumptions, analyze stability and perturbation robustness, and provide an abstract formulation of safety-constrained distillation. The results decouple theoretical guarantees from specific weighting formulas, enabling principled analysis of adaptive distillation methods under heterogeneity, distribution shift, and safety constraints.

Malware classification models often face performance degradation due to concept drift, arising from evolving threat landscapes and the emergence of novel malware families. This paper presents FARM (Few-shot Adaptive Recognition of Malware), a framework designed to detect and adapt to both covariate and label drift in Windows Portable Executable (PE) malware classification. FARM leverages a triplet autoencoder to project samples into a discriminative latent space, enabling unsupervised drift detection via DBSCAN clustering and dynamic thresholding. For rapid adaptation, it employs few-shot learning using prototype-based classification, requiring only a handful of labeled samples. FARM also supports full retraining when enough drifted samples accumulate, updating the latent space for long-term integration. Experiments on the BenchMFC dataset demonstrate that FARM improves classification performance under covariate drift by 5.6\%, and achieves an average F1 score of 0.85 on unseen malware families using only few-shot adaptation, which further increases to 0.94 after retraining. These results highlight FARM's robustness and adaptability in dynamic malware detection environments under limited supervision.

Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing novel classes with limited data. We focus on the challenging FSCIL setup where a model receives only a single sample (1-shot) for each novel class and no further training or model alterations are allowed after the base training phase. This makes generalization to novel classes particularly difficult. We propose a novel approach predicated on the hypothesis that base and novel class embeddings have structural similarity. We map the original embedding space into a residual space by subtracting the class prototype (i.e., the average class embedding) of input samples. Then, we leverage generative modeling with VAE or diffusion models to learn the multi-modal distribution of residuals over the base classes, and we use this as a valuable structural prior to improve recognition of novel classes. Our approach, Gen1S, consistently improves novel class recognition over the state of the art across multiple benchmarks and backbone architectures.

Large Language Models (LLMs) are increasingly used to support software testing tasks, yet there is little evidence of their effectiveness for REST API testing in industrial settings. To address this gap, we replicate our earlier work on LLM-based REST API test amplification within an industrial context at one of the largest logistics companies in Belgium. We apply LLM-based test amplification to six representative endpoints of a production microservice embedded in a large-scale, security-sensitive system, where there is in-depth complexity in authentication, stateful behavior, and organizational constraints. Our experience shows that LLM-based test amplification remains practically useful in industry by increasing coverage and revealing various observations and anomalies.

Neighborhood search operators are critical to the performance of Multi-Objective Evolutionary Algorithms (MOEAs) and rely heavily on expert design. Although recent LLM-based Automated Heuristic Design (AHD) methods have made notable progress, they primarily optimize individual heuristics or components independently, lacking explicit exploration and exploitation of dynamic coupling relationships between multiple operators. In this paper, multi-operator optimization in MOEAs is formulated as a Markov decision process, enabling the improvement of interdependent operators through sequential decision-making. To address this, we propose the Evolution of Operator Combination (E2OC) framework for MOEAs, which achieves the co-evolution of design strategies and executable codes. E2OC employs Monte Carlo Tree Search to progressively search combinations of operator design strategies and adopts an operator rotation mechanism to identify effective operator configurations while supporting the integration of mainstream AHD methods as the underlying designer. Experimental results across AHD tasks with varying objectives and problem scales show that E2OC consistently outperforms state-of-the-art AHD and other multi-heuristic co-design frameworks, demonstrating strong generalization and sustained optimization capability.

Named entity recognition (NER) is evolving from a sequence labeling task into a generative paradigm with the rise of large language models (LLMs). We conduct a systematic evaluation of open-source LLMs on both flat and nested NER tasks. We investigate several research questions including the performance gap between generative NER and traditional NER models, the impact of output formats, whether LLMs rely on memorization, and the preservation of general capabilities after fine-tuning. Through experiments across eight LLMs of varying scales and four standard NER datasets, we find that: (1) With parameter-efficient fine-tuning and structured formats like inline bracketed or XML, open-source LLMs achieve performance competitive with traditional encoder-based models and surpass closed-source LLMs like GPT-3; (2) The NER capability of LLMs stems from instruction-following and generative power, not mere memorization of entity-label pairs; and (3) Applying NER instruction tuning has minimal impact on general capabilities of LLMs, even improving performance on datasets like DROP due to enhanced entity understanding. These findings demonstrate that generative NER with LLMs is a promising, user-friendly alternative to traditional methods. We release the data and code at https://github.com/szu-tera/LLMs4NER.

A growing body of research suggests that the cognitive processes of large language models (LLMs) differ fundamentally from those of humans. However, existing interpretability methods remain limited in explaining how cognitive abilities are engaged during LLM reasoning. In this paper, we propose UniCog, a unified framework that analyzes LLM cognition via a latent mind space. Formulated as a latent variable model, UniCog encodes diverse abilities from dense model activations into sparse, disentangled latent dimensions. Through extensive analysis on six advanced LLMs, including DeepSeek-V3.2 and GPT-4o, we reveal a Pareto principle of LLM cognition, where a shared reasoning core is complemented by ability-specific signatures. Furthermore, we discover that reasoning failures often manifest as anomalous intensity in latent activations. These findings opens a new paradigm in LLM analysis, providing a cognition grounded view of reasoning dynamics. Finally, leveraging these insights, we introduce a latent-informed candidate prioritization strategy, which improves reasoning performance by up to 7.5% across challenging benchmarks. Our code is available at https://github.com/milksalute/unicog.

Artificial intelligence's rapid integration with intellectual property rights necessitates assessment of its impact on trade secrets, copyrights and patents. This study addresses lacunae in existing laws where India lacks AI-specific provisions, creating doctrinal inconsistencies and enforcement inefficacies. Global discourse on AI-IPR protections remains nascent. The research identifies gaps in Indian IP laws' adaptability to AI-generated outputs: trade secret protection is inadequate against AI threats; standardized inventorship criteria are absent. Employing doctrinal and comparative methodology, it scrutinizes legislative texts, judicial precedents and policy instruments across India, US, UK and EU. Preliminary findings reveal shortcomings: India's contract law creates fragmented trade secret regime; Section 3(k) of Indian Patents Act blocks AI invention patenting; copyright varies in authorship attribution. The study proposes harmonized legal taxonomy accommodating AI's role while preserving innovation incentives. India's National AI Strategy (2024) shows progress but legislative clarity is imperative. This contributes to global discourse with AI-specific IP protections ensuring resilience and equitable innovation. Promising results underscore recalibrating India's IP jurisprudence for global alignment.

Indirect call resolution remains a key challenge in reverse engineering and control-flow graph recovery, especially for stripped or optimized binaries. Static analysis is sound but often over-approximates, producing many false positives, whereas machine-learning approaches can improve precision but may sacrifice completeness and generalization. We present iResolveX, a hybrid multi-layered framework that combines conservative static analysis with learning-based refinement. The first layer applies a conservative value-set analysis (BPA) to ensure high recall. The second layer adds a learning-based soft-signature scorer (iScoreGen) and selective inter-procedural backward analysis with memory inspection (iScoreRefine) to reduce false positives. The final output, p-IndirectCFG, annotates indirect edges with confidence scores, enabling downstream analyses to choose appropriate precision--recall trade-offs. Across SPEC CPU2006 and real-world binaries, iScoreGen reduces predicted targets by 19.2% on average while maintaining BPA-level recall (98.2%). Combined with iScoreRefine, the total reduction reaches 44.3% over BPA with 97.8% recall (a 0.4% drop). iResolveX supports both conservative, recall-preserving and F1-optimized configurations and outperforms state-of-the-art systems.

Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized agents prioritizes utility and user experience, treating memory as a neutral component and largely overlooking its safety implications. In this paper, we reveal intent legitimation, a previously underexplored safety failure in personalized agents, where benign personal memories bias intent inference and cause models to legitimize inherently harmful queries. To study this phenomenon, we introduce PS-Bench, a benchmark designed to identify and quantify intent legitimation in personalized interactions. Across multiple memory-augmented agent frameworks and base LLMs, personalization increases attack success rates by 15.8%-243.7% relative to stateless baselines. We further provide mechanistic evidence for intent legitimation from internal representations space, and propose a lightweight detection-reflection method that effectively reduces safety degradation. Overall, our work provides the first systematic exploration and evaluation of intent legitimation as a safety failure mode that naturally arises from benign, real-world personalization, highlighting the importance of assessing safety under long-term personal context. WARNING: This paper may contain harmful content.

Bimanual manipulation in cluttered scenes requires policies that remain stable under occlusions, viewpoint and scene variations. Existing vision-language-action models often fail to generalize because (i) multi-view features are fused via view-agnostic token concatenation, yielding weak 3D-consistent spatial understanding, and (ii) language is injected as global conditioning, resulting in coarse instruction grounding. In this paper, we introduce PEAfowl, a perception-enhanced multi-view VLA policy for bimanual manipulation. For spatial reasoning, PEAfowl predicts per-token depth distributions, performs differentiable 3D lifting, and aggregates local cross-view neighbors to form geometrically grounded, cross-view consistent representations. For instruction grounding, we propose to replace global conditioning with a Perceiver-style text-aware readout over frozen CLIP visual features, enabling iterative evidence accumulation. To overcome noisy and incomplete commodity depth without adding inference overhead, we apply training-only depth distillation from a pretrained depth teacher to supervise the depth-distribution head, providing perception front-end with geometry-aware priors. On RoboTwin 2.0 under domain-randomized setting, PEAfowl improves the strongest baseline by 23.0 pp in success rate, and real-robot experiments further demonstrate reliable sim-to-real transfer and consistent improvements from depth distillation. Project website: https://peafowlvla.github.io/.

Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for brain-computer interfaces (BCIs), aiming to learn transferable neural representations from large-scale heterogeneous recordings. Despite rapid progresses, there lacks fair and comprehensive comparisons of existing EEG foundation models, due to inconsistent pre-training objectives, preprocessing choices, and downstream evaluation protocols. This paper fills this gap. We first review 50 representative models and organize their design choices into a unified taxonomic framework including data standardization, model architectures, and self-supervised pre-training strategies. We then evaluate 12 open-source foundation models and competitive specialist baselines across 13 EEG datasets spanning nine BCI paradigms. Emphasizing real-world deployments, we consider both cross-subject generalization under a leave-one-subject-out protocol and rapid calibration under a within-subject few-shot setting. We further compare full-parameter fine-tuning with linear probing to assess the transferability of pre-trained representations, and examine the relationship between model scale and downstream performance. Our results indicate that: 1) linear probing is frequently insufficient; 2) specialist models trained from scratch remain competitive across many tasks; and, 3) larger foundation models do not necessarily yield better generalization performance under current data regimes and training practices.

Long-form deep research requires multi-faceted investigations over extended horizons to get a comprehensive report. When handling such complex tasks, existing agents manage context at the subtask level to overcome linear context accumulation and information loss. However, they still adhere to a single context window and sequential execution paradigm, which results in mutual interference and blocking behavior, restricting scalability and adaptability. To address this issue, this paper introduces Self-Manager, a parallel agent loop that enables asynchronous and concurrent execution. The main thread can create multiple subthreads, each with its own isolated context, and manage them iteratively through Thread Control Blocks, allowing for more focused and flexible parallel agent execution. To assess its effectiveness, we benchmark Self-Manager on DeepResearch Bench, where it consistently outperforms existing single-agent loop baselines across all metrics. Furthermore, we conduct extensive analytical experiments to demonstrate the necessity of Self-Manager's design choices, as well as its advantages in contextual capacity, efficiency, and generalization.

The study investigates the juridico-technological architecture of international public health instruments, focusing on their implementation across India, the European Union, the United States and low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa. It addresses a research lacuna: the insufficient harmonisation between normative health law and algorithmic public health infrastructures in resource-constrained jurisdictions. The principal objective is to assess how artificial intelligence augments implementation of instruments grounded in IHR 2005 and the WHO FCTC while identifying doctrinal and infrastructural bottlenecks. Using comparative doctrinal analysis and legal-normative mapping, the study triangulates legislative instruments, WHO monitoring frameworks, AI systems including BlueDot, Aarogya Setu and EIOS, and compliance metrics. Preliminary results show that AI has improved early detection, surveillance precision and responsiveness in high-capacity jurisdictions, whereas LMICs face infrastructural deficits, data privacy gaps and fragmented legal scaffolding. The findings highlight the relevance of the EU Artificial Intelligence Act and GDPR as regulatory prototypes for health-oriented algorithmic governance and contrast them with embryonic AI integration and limited internet penetration in many LMICs. The study argues for embedding AI within a rights-compliant, supranationally coordinated regulatory framework to secure equitable health outcomes and stronger compliance. It proposes a model for algorithmic treaty-making inspired by FCTC architecture and calls for WHO-led compliance mechanisms modelled on the WTO Dispute Settlement Body to enhance pandemic preparedness, surveillance equity and transnational governance resilience.

Learning structural information from observational data is central to producing new knowledge outside the training corpus. This holds for mechanistic understanding in scientific discovery as well as flexible test-time compositional generation. We thus study how language models learn abstract structures and utilize the learnt structural information at test-time. To ensure a controlled setup, we design a natural language dataset based on linguistic structural transformations. We empirically show that the emergence of learning structural information correlates with complex reasoning tasks, and that the ability to perform test-time compositional generation remains limited.

Standard Autoregressive Video LLMs inevitably suffer from causal masking biases that hinder global spatiotemporal modeling, leading to suboptimal understanding efficiency. We propose VidLaDA, a Video LLM based on Diffusion Language Model utilizing bidirectional attention to capture bidirectional dependencies. To further tackle the inference bottleneck of diffusion decoding on massive video tokens, we introduce MARS-Cache. This framework accelerates inference by combining asynchronous visual cache refreshing with frame-wise chunk attention, effectively pruning redundancy while preserving global connectivity via anchor tokens. Extensive experiments show VidLaDA outperforms diffusion baselines and rivals state-of-the-art autoregressive models (e.g., Qwen2.5-VL and LLaVA-Video), with MARS-Cache delivering over 12x speedup without compromising reasoning accuracy. Code and checkpoints are open-sourced at https://github.com/ziHoHe/VidLaDA.

The predictive probability of the next token (P_token) in large language models (LLMs) is inextricably linked to the probability of relevance for the next piece of information, the purchase probability of the next product, and the execution probability of the next action-all of which fall under the scope of the task-level target distribution (P_task). While LLMs are known to generate samples that approximate real-world distributions, whether their fine-grained sampling probabilities faithfully align with task requirements remains an open question. Through controlled distribution-sampling simulations, we uncover a striking dichotomy in LLM behavior, distinguishing two model types: D-models (e.g. Qwen-2.5), whose P_token exhibits large step-to-step variability and poor alignment with P_task; and E-models (e.g. Mistral-Small), whose P_token is more stable and better aligned with P_task. We further evaluate these two model types in downstream tasks such as code generation and recommendation, revealing systematic trade-offs between diversity and stability that shape task outcomes. Finally, we analyze the internal properties of both model families to probe their underlying mechanisms. These findings offer foundational insights into the probabilistic sampling behavior of LLMs and provide practical guidance on when to favor D- versus E-models. For web-scale applications, including recommendation, search, and conversational agents, our results inform model selection and configuration to balance diversity with reliability under real-world uncertainty, providing a better level of interpretation.

Optimizing data mixtures is essential for unlocking the full potential of large language models (LLMs), yet identifying the optimal composition remains computationally prohibitive due to reliance on heuristic trials or expensive proxy training. To address this, we introduce \textbf{MergeMix}, a novel approach that efficiently determines optimal data mixing ratios by repurposing model merging weights as a high-fidelity, low-cost performance proxy. By training domain-specific experts on minimal tokens and optimizing their merging weights against downstream benchmarks, MergeMix effectively optimizes the performance of data mixtures without incurring the cost of full-scale training. Extensive experiments on models with 8B and 16B parameters validate that MergeMix achieves performance comparable to or surpassing exhaustive manual tuning while drastically reducing search costs. Furthermore, MergeMix exhibits high rank consistency (Spearman $ρ> 0.9$) and strong cross-scale transferability, offering a scalable, automated solution for data mixture optimization.

Load balancing-the allocation of work across parallel resources to reduce delay, energy and cost-is a pervasive challenge in science and engineering, from large-scale simulation and data processing to cloud and manufacturing operations. Motivated by the emerging bottleneck in large language model (LLM) serving, we study a particularly stringent regime of load balancing that arises in barrier-synchronized, stateful systems: work cannot be freely migrated and progress is gated by the slowest participant at each step, so heterogeneity and temporal drift in workloads create persistent stragglers and substantial idle time. LLM serving under data-parallel decoding provides a prominent modern instance: in production traces, barrier-induced idle can exceed 40% of compute time per decode step. Here we develop a universal load-balancing principle, which admits a step-wise finite-horizon integer-optimization formulation and yields worst-case guarantees: across LLM decode models and a broader class of non-decreasing workload drift processes, it reduces long-run imbalance by a factor that grows with batch size and system scale. Extensive experiments corroborate the theory, showing substantial improvements in throughput and latency together with reductions in energy consumption. These results provide a general, theoretically grounded framework for load balancing, with immediate implications for sustainable LLM serving and broad relevance to other synchronization-gated resource-allocation problems.

Electroencephalogram (EEG) decoding is a critical component of medical diagnostics, rehabilitation engineering, and brain-computer interfaces. However, contemporary decoding methodologies remain heavily dependent on task-specific datasets to train specialized neural network architectures. Consequently, limited data availability impedes the development of generalizable large brain decoding models. In this work, we propose a paradigm shift from conventional signal-based decoding by leveraging large-scale vision-language models (VLMs) to analyze EEG waveform plots. By converting multivariate EEG signals into stacked waveform images and integrating neuroscience domain expertise into textual prompts, we demonstrate that foundational VLMs can effectively differentiate between different patterns in the human brain. To address the inherent non-stationarity of EEG signals, we introduce a Retrieval-Augmented In-Context Learning (RAICL) approach, which dynamically selects the most representative and relevant few-shot examples to condition the autoregressive outputs of the VLM. Experiments on EEG-based seizure detection indicate that state-of-the-art VLMs under RAICL achieved better or comparable performance with traditional time series based approaches. These findings suggest a new direction in physiological signal processing that effectively bridges the modalities of vision, language, and neural activities. Furthermore, the utilization of off-the-shelf VLMs, without the need for retraining or downstream architecture construction, offers a readily deployable solution for clinical applications.

Leveraging Large Language Models (LLMs) for Mental Health Question Answering (MHQA) is promising for mitigating resource shortages. However, existing Cognitive Behavioral Therapy (CBT)-based approaches predominantly favor a "top-down" rational restructuring, often neglecting clients' embodied experiences and primary emotion processing. To address this, we propose an Emotion-Focused Therapy (EFT)-based Multi-Agent Chain-of-Thought framework (EFT-CoT). Adopting a "bottom-up" trajectory, it deconstructs the intervention into a three-stage reasoning flow: "Embodied Perception - Cognitive Exploration - Narrative Intervention." Utilizing eight specialized agents, the system explicitly executes critical components such as somatic awareness mapping, adaptive assessment, core belief extraction, and narrative restructuring. We further constructed "EFT-Instruct," a high-quality dataset via Chain-of-Thought distillation of approximately 67,000 authentic texts, and fine-tuned a specialized model, EFT-LLM. Experimental evaluations demonstrate that EFT-LLM outperforms strong baselines and human responses across metrics like empathy depth and structural professionalism. Ablation studies confirm the necessity of the multi-agent mechanism. The model exhibits superior psychological reasoning, offering an effective pathway for interpretable, high-empathy counseling systems.

We consider polynomial codes for private distributed matrix multiplication (PDMM/SDMM). Existing codes for PDMM are either specialized for the outer product partitioning (OPP), or inner product partitioning (IPP), or are valid for the more general grid partitioning (GP). We design extension operations that can be applied to a large class of OPP code designs to extend them to the GP case. Applying them to existing codes improves upon the state-of-the-art for certain parameters. Additionally, we show that the GP schemes resulting from extension fulfill additional combinatorial constraints, potentially limiting their performance. We illustrate this point by presenting a new GP scheme that does not adhere to these constraints and outperforms the state-of-the-art for a range of parameters.

The construction of function calling agents has emerged as a promising avenue for extending model capabilities. A major challenge for this task is obtaining high quality diverse data for training. Prior work emphasizes diversity in functions, invocation patterns, and interaction turns, yet linguistic diversity of requests and coverage of arguments (e.g., \texttt{city\_name}, \texttt{stock\_ticker}) remain underexplored. We propose a method that generates synthetic datasets via optimizing general-purpose diversity metrics across both queries and arguments, without relying on hand-crafted rules or taxonomies, making it robust to different usecases. We demonstrate the effectiveness of our technique via both intrinsic and extrinsic testing, comparing it to SoTA data generation methods. We show a superiority over baselines in terms of diversity, while keeping comparable correctness. Additionally, when used as a training set, the model resulting from our dataset exhibits superior performance compared to analogous models based on the baseline data generation methods in out-of-distribution performance. In particular, we achieve an $7.4\%$ increase in accuracy on the BFCL benchmark compared to similar counterparts.

We present Information Gain Fine-Tuning (IGFT), a novel approach for training medical conversational AI to conduct effective patient interviews and generate comprehensive History of Present Illness (HPI) without requiring pre-collected human conversations. IGFT combines online Group Relative Policy Optimization (GRPO) with information-theoretic rewards, enabling models to learn from self-generated conversations with simulated patients. Unlike existing approaches that rely on expensive expert-annotated conversations or static datasets, our online RL framework allows models to discover effective questioning strategies through exploration. Our key innovation is an information gain reward function that tracks which clinical entities such as symptoms, temporal patterns, and medical history, are revealed during conversation. Each question's reward is computed based on its expected information gain combined with GPT-4o-mini quality assessments across dimensions including clinical relevance, patient engagement, and specificity. This hybrid approach ensures models learn to ask targeted, clinically appropriate questions that efficiently gather diagnostic information. We fine-tune two models using LoRA: Llama-3.1-8B-Instruct and DeepSeek-R1-Distill-Qwen-7B (a reasoning-optimized model). Training exclusively on Avey data containing concise HPIs, we evaluate generalization to MIMIC data with longer, more elaborate HPIs. DeepSeek-R1-Distill-Qwen-7B (IGFT) achieves F1 scores of 0.408 on Avey (10.9% improvement over base) and 0.289 on MIMIC (12.9% improvement), while Llama-3.1-8B-Instruct (IGFT) reaches 0.384 and 0.336 respectively. Both models outperform OpenAI's model on MIMIC and surpass medical domain-specific baselines like HuatuoGPT and UltraMedical, which were optimized for single-turn medical QA rather than multi-turn conversations.

The increasing frequency and complexity of regulatory updates present a significant burden for multinational pharmaceutical companies. Compliance teams must interpret evolving rules across jurisdictions, formats, and agencies, often manually, at high cost and risk of error. We introduce RegGuard, an industrial-scale AI assistant designed to automate the interpretation of heterogeneous regulatory texts and align them with internal corporate policies. The system ingests heterogeneous document sources through a secure pipeline and enhances retrieval and generation quality with two novel components: HiSACC (Hierarchical Semantic Aggregation for Contextual Chunking) semantically segments long documents into coherent units while maintaining consistency across non-contiguous sections. ReLACE (Regulatory Listwise Adaptive Cross-Encoder for Reranking), a domain-adapted cross-encoder built on an open-source model, jointly models user queries and retrieved candidates to improve ranking relevance. Evaluations in enterprise settings demonstrate that RegGuard improves answer quality specifically in terms of relevance, groundedness, and contextual focus, while significantly mitigating hallucination risk. The system architecture is built for auditability and traceability, featuring provenance tracking, access control, and incremental indexing, making it highly responsive to evolving document sources and relevant for any domain with stringent compliance demands.

In this paper, we present DIETA, a small, decoder-only Transformer model with 0.5 billion parameters, specifically designed and trained for Italian-English machine translation. We collect and curate a large parallel corpus consisting of approximately 207 million Italian-English sentence pairs across diverse domains, including parliamentary proceedings, legal texts, web-crawled content, subtitles, news, literature and 352 million back-translated data using pretrained models. Additionally, we create and release a new small-scale evaluation set, consisting of 450 sentences, based on 2025 WikiNews articles, enabling assessment of translation quality on contemporary text. Comprehensive evaluations show that DIETA achieves competitive performance on multiple Italian-English benchmarks, consistently ranking in the second quartile of a 32-system leaderboard and outperforming most other sub-3B models on four out of five test suites. The training script, trained models, curated corpus, and newly introduced evaluation set are made publicly available, facilitating further research and development in specialized Italian-English machine translation. https://github.com/pkasela/DIETA-Machine-Translation

Multimodal large language models (MLLMs) have advanced rapidly, yet heterogeneity in architecture, alignment strategies, and efficiency means that no single model is uniformly superior across tasks. In practical deployments, workloads span lightweight OCR to complex multimodal reasoning; using one MLLM for all queries either over-provisions compute on easy instances or sacrifices accuracy on hard ones. Query-level model selection (routing) addresses this tension, but extending routing from text-only LLMs to MLLMs is nontrivial due to modality fusion, wide variation in computational cost across models, and the absence of a standardized, budget-aware evaluation. We present MMR-Bench, a unified benchmark that isolates the multimodal routing problem and enables comparison under fixed candidate sets and cost models. MMR-Bench provides (i) a controlled environment with modality-aware inputs and variable compute budgets, (ii) a broad suite of vision-language tasks covering OCR, general VQA, and multimodal math reasoning, and (iii) strong single-model reference, oracle upper bounds, and representative routing policies. Using MMR-Bench, we show that incorporating multimodal signals improves routing quality. Empirically, these cues improve the cost-accuracy frontier and enable the routed system to exceed the strongest single model's accuracy at roughly 33% of its cost. Furthermore, policies trained on a subset of models and tasks generalize zero-shot to new datasets and text-only benchmarks without retuning, establishing MMR-Bench as a foundation for studying adaptive multimodal model selection and efficient MLLM deployment. The code will be available at: https://github.com/Hunter-Wrynn/MMR-Bench.

Motif discovery is a core problem in computational biology, traditionally formulated as a likelihood optimization task that returns a single dominant motif from a DNA sequence dataset. However, regulatory sequence data admit multiple plausible motif explanations, reflecting underlying biological heterogeneity. In this work, we frame motif discovery as a quality-diversity problem and apply the MAP-Elites algorithm to evolve position weight matrix motifs under a likelihood-based fitness objective while explicitly preserving diversity across biologically meaningful dimensions. We evaluate MAP-Elites using three complementary behavioral characterizations that capture trade-offs between motif specificity, compositional structure, coverage, and robustness. Experiments on human CTCF liver ChIP-seq data aligned to the human reference genome compare MAP-Elites against a standard motif discovery tool, MEME, under matched evaluation criteria across stratified dataset subsets. Results show that MAP-Elites recovers multiple high-quality motif variants with fitness comparable to MEME's strongest solutions while revealing structured diversity obscured by single-solution approaches.

Scientific discovery is severely bottlenecked by the inability of manual curation to keep pace with exponential publication rates. This creates a widening knowledge gap. This is especially stark in photovoltaics, where the leading database for perovskite solar cells has been stagnant since 2021 despite massive ongoing research output. Here, we resolve this challenge by establishing an autonomous, self-updating living database (PERLA). Our pipeline integrates large language models with physics-aware validation to extract complex device data from the continuous literature stream, achieving human-level precision (>90%) and eliminating annotator variance. By employing this system on the previously inaccessible post-2021 literature, we uncover critical evolutionary trends hidden by data lag: the field has decisively shifted toward inverted architectures employing self-assembled monolayers and formamidinium-rich compositions, driving a clear trajectory of sustained voltage loss reduction. PERLA transforms static publications into dynamic knowledge resources that enable data-driven discovery to operate at the speed of publication.

Accurate identification of non-enhancing hypercellular (NEH) tumor regions is an unmet need in neuro-oncological imaging, with significant implications for patient management and treatment planning. We present a robust computational framework that generates probability maps of NEH regions from routine MRI data, leveraging multiple network architectures to address the inherent variability and lack of clear imaging boundaries. Our approach was validated against independent clinical markers -- relative cerebral blood volume (rCBV) and enhancing tumor recurrence location (ETRL) -- demonstrating both methodological robustness and biological relevance. This framework enables reliable, non-invasive mapping of NEH tumor compartments, supporting their integration as imaging biomarkers in clinical workflows and advancing precision oncology for brain tumor patients.

We show how the differentiability method employed in the paper ``Differentiable Integer Linear Programming'', Geng, et al., 2025 as shown in its theorem 5 is incorrect. Moreover, there already exists some downstream work that inherits the same error. The underlying reason comes from that, though being continuous in expectation, the surrogate loss is discontinuous in almost every realization of the randomness, for the stochastic gradient descent.

A fundamental problem of applying Large Language Models (LLMs) to important applications is that LLMs do not always follow instructions, and violations are often hard to observe or check. In LLM-based agentic workflows, such violations can propagate and amplify along reasoning chains, causing task failures and system incidents. This paper presents NSVIF, a neuro-symbolic framework for verifying whether an LLM's output follows the instructions used to prompt the LLM. NSVIF is a universal, general-purpose verifier; it makes no assumption about the instruction or the LLM. NSVIF formulates instruction-following verification as a constraint-satisfaction problem by modeling user instructions as constraints. NSVIF models both logical and semantic constraints; constraint solving is done by a unified solver that orchestrates logical reasoning and semantic analysis. To evaluate NSVIF, we develop VIFBENCH, a new benchmark for instruction-following verifiers with fine-grained data labels. Experiments show that NSVIF significantly outperforms LLM-based approaches and provides interpretable feedback. We also show that feedback from NSVIF helps improve LLMs' instruction-following capability without post-training.

Text anomaly detection (TAD) plays a critical role in various language-driven real-world applications, including harmful content moderation, phishing detection, and spam review filtering. While two-step "embedding-detector" TAD methods have shown state-of-the-art performance, their effectiveness is often limited by the use of a single embedding model and the lack of adaptability across diverse datasets and anomaly types. To address these limitations, we propose to exploit the embeddings from multiple pretrained language models and integrate them into $MCA^2$, a multi-view TAD framework. $MCA^2$ adopts a multi-view reconstruction model to effectively extract normal textual patterns from multiple embedding perspectives. To exploit inter-view complementarity, a contrastive collaboration module is designed to leverage and strengthen the interactions across different views. Moreover, an adaptive allocation module is developed to automatically assign the contribution weight of each view, thereby improving the adaptability to diverse datasets. Extensive experiments on 10 benchmark datasets verify the effectiveness of $MCA^2$ against strong baselines. The source code of $MCA^2$ is available at https://github.com/yankehan/MCA2.

The widespread adoption of deep-learning models in data-driven applications has drawn attention to the potential risks associated with biased datasets and models. Neglected or hidden biases within datasets and models can lead to unexpected results. This study addresses the challenges of dataset bias and explores ``shortcut learning'' or ``Clever Hans effect'' in binary classifiers. We propose a novel framework for analyzing the black-box classifiers and for examining the impact of both training and test data on classifier scores. Our framework incorporates intervention and observational perspectives, employing a linear mixed-effects model for post-hoc analysis. By evaluating classifier performance beyond error rates, we aim to provide insights into biased datasets and offer a comprehensive understanding of their influence on classifier behavior. The effectiveness of our approach is demonstrated through experiments on audio anti-spoofing and speaker verification tasks using both statistical models and deep neural networks. The insights gained from this study have broader implications for tackling biases in other domains and advancing the field of explainable artificial intelligence.

The way our eyes move while reading can tell us about the cognitive effort required to process the text. In the present study, we use this fact to generate texts with controllable reading ease. Our method employs a model that predicts human gaze patterns to steer language model outputs towards eliciting certain reading behaviors. We evaluate the approach in an eye-tracking experiment with native and non-native speakers of English. The results demonstrate that the method is effective at making the generated texts easier or harder to read, measured both in terms of reading times and perceived difficulty of the texts. A statistical analysis reveals that the changes in reading behavior are mostly due to features that affect lexical processing. Possible applications of our approach include text simplification for information accessibility and generation of personalized educational material for language learning.

Supervised fine-tuning (SFT) is a crucial step for adapting large language models (LLMs) to downstream tasks. However, conflicting objectives across heterogeneous SFT tasks often induce the "seesaw effect": optimizing for one task may degrade performance on others, particularly when model parameters are updated indiscriminately. In this paper, we propose a principled approach to disentangle and isolate task-specific parameter regions, motivated by the hypothesis that parameter heterogeneity underlies cross-task interference. Specifically, we first independently fine-tune LLMs on diverse SFT tasks and identify each task's core parameter region as the subset of parameters exhibiting the largest updates. Tasks with highly overlapping core parameter regions are merged for joint training, while disjoint tasks are organized into different stages. During multi-stage SFT, core parameters acquired in prior tasks are frozen, thereby preventing overwriting by subsequent tasks. To verify the effectiveness of our method, we conducted intensive experiments on multiple public datasets. The results showed that our dynamic parameter isolation strategy consistently reduced data conflicts and achieved consistent performance improvements compared to multi-stage and multi-task tuning baselines.

Distributed Graph Neural Network (GNN) training suffers from substantial communication overhead due to the inherent neighborhood dependency in graph-structured data. This neighbor explosion problem requires workers to frequently exchange boundary node features across partitions, creating a communication bottleneck that severely limits training scalability. Existing approaches rely on static graph partitioning strategies that cannot adapt to dynamic network conditions. In this paper, we propose CondenseGraph, a novel communication-efficient framework for distributed GNN training. Our key innovation is an on-the-fly graph condensation mechanism that dynamically compresses boundary node features into compact super nodes before transmission. To compensate for the information loss introduced by compression, we develop a gradient-based error feedback mechanism that maintains convergence guarantees while reducing communication volume by 40-60%. Extensive experiments on four benchmark datasets demonstrate that CondenseGraph achieves comparable accuracy to full-precision baselines while significantly reducing communication costs and training time.

This paper introduces MarketGAN, a factor-based generative framework for high-dimensional asset return generation under severe data scarcity. We embed an explicit asset-pricing factor structure as an economic inductive bias and generate returns as a single joint vector, thereby preserving cross-sectional dependence and tail co-movement alongside inter-temporal dynamics. MarketGAN employs generative adversarial learning with a temporal convolutional network (TCN) backbone, which models stochastic, time-varying factor loadings and volatilities and captures long-range temporal dependence. Using daily returns of large U.S. equities, we find that MarketGAN more closely matches empirical stylized facts of asset returns, including heavy-tailed marginal distributions, volatility clustering, leverage effects, and, most notably, high-dimensional cross-sectional correlation structures and tail co-movement across assets, than conventional factor-model-based bootstrap approaches. In portfolio applications, covariance estimates derived from MarketGAN-generated samples outperform those derived from other methods when factor information is at least weakly informative, demonstrating tangible economic value.

The success of large-scale language models has established tokens as compact and meaningful units for natural-language representation, which motivates token communication over wireless channels, where tokens are considered fundamental units for wireless transmission. We propose a context-aware token communication framework that uses a pretrained masked language model (MLM) as a shared contextual probability model between the transmitter (Tx) and receiver (Rx). At Rx, we develop an iterative token detection method that jointly exploits MLM-guided contextual priors and channel observations based on a Bayesian perspective. At Tx, we additionally introduce a context-aware masking strategy which skips highly predictable token transmission to reduce transmission rate. Simulation results demonstrate that the proposed framework substantially improves reconstructed sentence quality and supports effective rate adaptation under various channel conditions.

In LLM inference, the same prompt may yield different outputs across different runs. At the system level, this non-determinism arises from floating-point non-associativity combined with dynamic batching and GPU kernels whose reduction orders vary with batch size. A straightforward way to eliminate non-determinism is to disable dynamic batching during inference, but doing so severely degrades throughput. Another approach is to make kernels batch-invariant; however, this tightly couples determinism to kernel design, requiring new implementations. This coupling also imposes fixed runtime overheads, regardless of how much of the workload actually requires determinism. Inspired by ideas from speculative decoding, we present LLM-42, a scheduling-based approach to enable determinism in LLM inference. Our key observation is that if a sequence is in a consistent state, the next emitted token is likely to be consistent even with dynamic batching. Moreover, most GPU kernels use shape-consistent reductions. Leveraging these insights, LLM-42 decodes tokens using a non-deterministic fast path and enforces determinism via a lightweight verify-rollback loop. The verifier replays candidate tokens under a fixed-shape reduction schedule, commits those that are guaranteed to be consistent across runs, and rolls back those violating determinism. LLM-42 mostly re-uses existing kernels unchanged and incurs overhead only in proportion to the traffic that requires determinism.

Cardiovascular disease (CVD) remains the foremost cause of mortality worldwide, underscoring the urgent need for intelligent and data-driven diagnostic tools. Traditional predictive models often struggle to generalize across heterogeneous datasets and complex physiological patterns. To address this, we propose a hybrid ensemble framework that integrates deep learning architectures, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), with classical machine learning algorithms, including K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGB), using an ensemble voting mechanism. This approach combines the representational power of deep networks with the interpretability and efficiency of traditional models. Experiments on two publicly available Kaggle datasets demonstrate that the proposed model achieves superior performance, reaching 82.30 percent accuracy on Dataset I and 97.10 percent on Dataset II, with consistent gains in precision, recall, and F1-score. These findings underscore the robustness and clinical potential of hybrid AI frameworks for predicting cardiovascular disease and facilitating early intervention. Furthermore, this study directly supports the United Nations Sustainable Development Goal 3 (Good Health and Well-being) by promoting early diagnosis, prevention, and management of non-communicable diseases through innovative, data-driven healthcare solutions.

Large Language Models (LLMs) have achieved significant success in recent years; yet, issues of intrinsic gender bias persist, especially in non-English languages. Although current research mostly emphasizes English, the linguistic and cultural biases inherent in Global South languages, like Bengali, are little examined. This research seeks to examine the characteristics and magnitude of gender bias in Bengali, evaluating the efficacy of current approaches in identifying and alleviating bias. We use several methods to extract gender-biased utterances, including lexicon-based mining, computational classification models, translation-based comparison analysis, and GPT-based bias creation. Our research indicates that the straight application of English-centric bias detection frameworks to Bengali is severely constrained by language disparities and socio-cultural factors that impact implicit biases. To tackle these difficulties, we executed two field investigations inside rural and low-income areas, gathering authentic insights on gender bias. The findings demonstrate that gender bias in Bengali presents distinct characteristics relative to English, requiring a more localized and context-sensitive methodology. Additionally, our research emphasizes the need of integrating community-driven research approaches to identify culturally relevant biases often neglected by automated systems. Our research enhances the ongoing discussion around gender bias in AI by illustrating the need to create linguistic tools specifically designed for underrepresented languages. This study establishes a foundation for further investigations into bias reduction in Bengali and other Indic languages, promoting the development of more inclusive and fair NLP systems.

Software vulnerability management has become increasingly critical as modern systems scale in size and complexity. However, existing automated approaches remain insufficient. Traditional static analysis methods struggle to precisely capture contextual dependencies, especially when vulnerabilities span multiple functions or modules. Large language models (LLMs) often lack the ability to retrieve and exploit sufficient contextual information, resulting in incomplete reasoning and unreliable outcomes. Meanwhile, recurring vulnerabilities emerge repeatedly due to code reuse and shared logic, making historical vulnerability knowledge an indispensable foundation for effective vulnerability detection and repair. Nevertheless, prior approaches such as clone-based detection and patch porting, have not fully leveraged this knowledge. To address these challenges, we present MAVM, a multi-agent framework for end-to-end recurring vulnerability management. MAVM integrates five components, including a vulnerability knowledge base, detection, confirmation, repair, and validation, into a unified multi-agent pipeline. We construct a knowledge base from publicly disclosed vulnerabilities, thereby addressing the underuse of historical knowledge in prior work and mitigating the lack of domain-specific expertise in LLMs. Furthermore, we design context-retrieval tools that allow agents to extract and reason over repository-level information, overcoming the contextual limitations of previous methods. Based on agents, MAVM effectively simulates real-world security workflows. To evaluate the performance of MAVM, we construct a dataset containing 78 real-world patch-porting cases (covering 114 function-level migrations). On this dataset, MAVM successfully detects and repairs 51 real vulnerabilities, outperforming baselines by 31.9%-45.2% in repair accuracy, which demonstrates its effectiveness.

Real-world perception and interaction are inherently multimodal, encompassing not only language but also vision and speech, which motivates the development of "Omni" MLLMs that support both multimodal inputs and multimodal outputs. While a sequence of omni MLLMs has emerged, most existing systems still rely on additional expert components to achieve multimodal generation, limiting the simplicity of unified training and inference. Autoregressive (AR) modeling, with a single token stream, a single next-token objective, and a single decoder, is an elegant and scalable foundation in the text domain. Motivated by this, we present AR-Omni, a unified any-to-any model in the autoregressive paradigm without any expert decoders. AR-Omni supports autoregressive text and image generation, as well as streaming speech generation, all under a single Transformer decoder. We further address three practical issues in unified AR modeling: modality imbalance via task-aware loss reweighting, visual fidelity via a lightweight token-level perceptual alignment loss for image tokens, and stability-creativity trade-offs via a finite-state decoding mechanism. Empirically, AR-Omni achieves strong quality across three modalities while remaining real-time, achieving a 0.88 real-time factor for speech generation.

Existing Subject-to-Video Generation (S2V) methods have achieved high-fidelity and subject-consistent video generation, yet remain constrained to single-view subject references. This limitation renders the S2V task reducible to an S2I + I2V pipeline, failing to exploit the full potential of video subject control. In this work, we propose and address the challenging Multi-View S2V (MV-S2V) task, which synthesizes videos from multiple reference views to enforce 3D-level subject consistency. Regarding the scarcity of training data, we first develop a synthetic data curation pipeline to generate highly customized synthetic data, complemented by a small-scale real-world captured dataset to boost the training of MV-S2V. Another key issue lies in the potential confusion between cross-subject and cross-view references in conditional generation. To overcome this, we further introduce Temporally Shifted RoPE (TS-RoPE) to distinguish between different subjects and distinct views of the same subject in reference conditioning. Our framework achieves superior 3D subject consistency w.r.t. multi-view reference images and high-quality visual outputs, establishing a new meaningful direction for subject-driven video generation. Our project page is available at <a href="https://szy-young.github.io/mv-s2v">this URL</a>

Graph Retrieval-Augmented Generation (GraphRAG) has been successfully applied in various knowledge-intensive question answering tasks by organizing external knowledge into structured graphs of entities and relations. It enables large language models (LLMs) to perform complex reasoning beyond text-chunk retrieval. Recent works have employed reinforcement learning (RL) to train agentic GraphRAG frameworks that perform iterative interactions between LLMs and knowledge graphs. However, existing RL-based frameworks such as Graph-R1 suffer from two key limitations: (1) they primarily depend on semantic similarity for retrieval, often overlooking the underlying graph structure, and (2) they rely on sparse, outcome-level rewards, failing to capture the quality of intermediate retrieval steps and their dependencies. To address these limitations, we propose ProGraph-R1, a progress-aware agentic framework for graph-based retrieval and multi-step reasoning. ProGraph-R1 introduces a structure-aware hypergraph retrieval mechanism that jointly considers semantic relevance and graph connectivity, encouraging coherent traversal along multi-hop reasoning paths. We also design a progress-based step-wise policy optimization, which provides dense learning signals by modulating advantages according to intermediate reasoning progress within a graph, rather than relying solely on final outcomes. Experiments on multi-hop question answering benchmarks demonstrate that ProGraph-R1 consistently improves reasoning accuracy and generation quality over existing GraphRAG methods.

The Cerebras Wafer-Scale Engine (WSE) delivers performance at an unprecedented scale of over 900,000 compute units, all connected via a single-wafer on-chip interconnect. Initially designed for AI, the WSE architecture is also well-suited for High Performance Computing (HPC). However, its distributed asynchronous programming model diverges significantly from the simple sequential or bulk-synchronous programs that one would typically derive for a given mathematical program description. Targeting the WSE requires a bespoke re-implementation when porting existing code. The absence of WSE support in compilers such as MLIR, meant that there was little hope for automating this process. Stencils are ubiquitous in HPC, and in this paper we explore the hypothesis that domain specific information about stencils can be leveraged by the compiler to automatically target the WSE without requiring application-level code changes. We present a compiler pipeline that transforms stencil-based kernels into highly optimized CSL code for the WSE, bridging the semantic gap between the mathematical representation of the problem and the WSE's asynchronous execution model. Based upon five benchmarks across three HPC programming technologies, running on both the Cerebras WSE2 and WSE3, our approach delivers comparable, if not slightly better, performance than manually optimized code. Furthermore, without requiring any application level code changes, performance on the WSE3 is around 14 times faster than 128 Nvidia A100 GPUs and 20 times faster than 128 nodes of a CPU-based Cray-EX supercomputer when using our approach.

Research keyloggers are essential for cognitive studies of text production, yet most fail to capture the on-screen transformations performed by Input Method Editors (IMEs) for non-alphabetic scripts. To address this methodological gap, we present Hylog, a novel hybrid logging system that combines analytical keylogging with ecological text logging for a more complete and finer-grained analysis. Our modular, open-source system uses plug-ins for standard applications (Microsoft Word, Google Chrome) to capture both keyboard output and rendered text, which a hybridizer module then synchronizes into a dual trace. To validate the system's technical feasibility and demonstrate its analytical capabilities, we conducted a proof-of-concept study where two volunteers translated a text into simplified Chinese. Hylog successfully captured keypresses and temporal intervals between Latin letters, Chinese characters, and IME confirmations -- some measurements invisible to traditional keyloggers. The resulting data enable the formulation of new, testable hypotheses about the cognitive restrictions and affordances at different linguistic layers in IME-mediated typing. Our plug-in architecture enables extension to other IME systems and fosters more inclusive multilingual text-production research.

The recently envisioned goal-oriented communications paradigm calls for the application of inference on wirelessly transferred data via Machine Learning (ML) tools. An emerging research direction deals with the realization of inference ML models directly in the physical layer of Multiple-Input Multiple-Output (MIMO) systems, which, however, entails certain significant challenges. In this paper, leveraging the technology of programmable MetaSurfaces (MSs), we present an eXtremely Large (XL) MIMO system that acts as an Extreme Learning Machine (ELM) performing binary classification tasks completely Over-The-Air (OTA), which can be trained in closed form. The proposed system comprises a receiver architecture consisting of densely parallel placed diffractive layers of XL MSs followed by a single reception radio-frequency chain. The front layer facing the MIMO channel consists of identical unit cells of a fixed NonLinear (NL) response, while the remaining layers of elements of tunable linear responses are utilized to approximate OTA the trained ELM weights. Our numerical investigations showcase that, in the XL regime of MS elements, the proposed XL-MIMO-ELM system achieves performance comparable to that of digital and idealized ML models across diverse datasets and wireless scenarios, thereby demonstrating the feasibility of embedding OTA learning capabilities into future communication systems.

Autonomous agent systems increasingly trigger real-world side effects: deploying infrastructure, modifying databases, moving money, and executing workflows. Yet most agent stacks provide no mandatory execution checkpoint where organizations can deterministically permit, deny, or defer an action before it changes reality. This paper introduces Faramesh, a protocol-agnostic execution control plane that enforces execution-time authorization for agent-driven actions via a non-bypassable Action Authorization Boundary (AAB). Faramesh canonicalizes agent intent into a Canonical Action Representation (CAR), evaluates actions deterministically against policy and state, and issues a decision artifact (PERMIT/DEFER/DENY) that executors must validate prior to execution. The system is designed to be framework- and model-agnostic, supports multi-agent and multi-tenant deployments, and remains independent of transport protocols (e.g., MCP). Faramesh further provides decision-centric, append-only provenance logging keyed by canonical action hashes, enabling auditability, verification, and deterministic replay without re-running agent reasoning. We show how these primitives yield enforceable, predictable governance for autonomous execution while avoiding hidden coupling to orchestration layers or observability-only approaches.

Recent advances in video generation have produced models capable of synthesizing stunning visual content from simple text prompts. However, these models struggle to generate long-form, coherent narratives from high-level concepts like dialogue, revealing a ``semantic gap'' between a creative idea and its cinematic execution. To bridge this gap, we introduce a novel, end-to-end agentic framework for dialogue-to-cinematic-video generation. Central to our framework is ScripterAgent, a model trained to translate coarse dialogue into a fine-grained, executable cinematic script. To enable this, we construct ScriptBench, a new large-scale benchmark with rich multimodal context, annotated via an expert-guided pipeline. The generated script then guides DirectorAgent, which orchestrates state-of-the-art video models using a cross-scene continuous generation strategy to ensure long-horizon coherence. Our comprehensive evaluation, featuring an AI-powered CriticAgent and a new Visual-Script Alignment (VSA) metric, shows our framework significantly improves script faithfulness and temporal fidelity across all tested video models. Furthermore, our analysis uncovers a crucial trade-off in current SOTA models between visual spectacle and strict script adherence, providing valuable insights for the future of automated filmmaking.

Interactivity is crucial for effective data visualizations. However, it is often challenging to implement interactions for existing static visualizations, since the underlying code and data for existing static visualizations are often not available, and it also takes significant time and effort to enable interactions for them even if the original code and data are available. To fill this gap, we propose Athanor, a novel approach to transform existing static visualizations into interactive ones using multimodal large language models (MLLMs) and natural language instructions. Our approach introduces three key innovations: (1) an action-modification interaction design space that maps visualization interactions into user actions and corresponding adjustments, (2) a multi-agent requirement analyzer that translates natural language instructions into an actionable operational space, and (3) a visualization abstraction transformer that converts static visualizations into flexible and interactive representations regardless of their underlying implementation. Athanor allows users to effortlessly author interactions through natural language instructions, eliminating the need for programming. We conducted two case studies and in-depth interviews with target users to evaluate our approach. The results demonstrate the effectiveness and usability of our approach in allowing users to conveniently enable flexible interactions for static visualizations.

Relational databases (RDBs) play a crucial role in many real-world web applications, supporting data management across multiple interconnected tables. Beyond typical retrieval-oriented tasks, prediction tasks on RDBs have recently gained attention. In this work, we address this problem by generating informative relational features that enhance predictive performance. However, generating such features is challenging: it requires reasoning over complex schemas and exploring a combinatorially large feature space, all without explicit supervision. To address these challenges, we propose ReFuGe, an agentic framework that leverages specialized large language model agents: (1) a schema selection agent identifies the tables and columns relevant to the task, (2) a feature generation agent produces diverse candidate features from the selected schema, and (3) a feature filtering agent evaluates and retains promising features through reasoning-based and validation-based filtering. It operates within an iterative feedback loop until performance converges. Experiments on RDB benchmarks demonstrate that ReFuGe substantially improves performance on various RDB prediction tasks. Our code and datasets are available at https://github.com/K-Kyungho/REFUGE.

As AI systems become pervasive, grounding their behavior in human values is critical. Prior work suggests that language models (LMs) exhibit limited inherent moral reasoning, leading to calls for explicit moral teaching. However, constructing ground truth data for moral evaluation is difficult given plural frameworks and pervasive biases. We investigate unsupervised elicitation as an alternative, asking whether pretrained (base) LMs possess intrinsic moral reasoning capability that can be surfaced without human supervision. Using the Internal Coherence Maximization (ICM) algorithm across three benchmark datasets and four LMs, we test whether ICM can reliably label moral judgments, generalize across moral frameworks, and mitigate social bias. Results show that ICM outperforms all pre-trained and chatbot baselines on the Norm Bank and ETHICS benchmarks, while fine-tuning on ICM labels performs on par with or surpasses those of human labels. Across theoretically motivated moral frameworks, ICM yields its largest relative gains on Justice and Commonsense morality. Furthermore, although chatbot LMs exhibit social bias failure rates comparable to their pretrained ones, ICM reduces such errors by more than half, with the largest improvements in race, socioeconomic status, and politics. These findings suggest that pretrained LMs possess latent moral reasoning capacities that can be elicited through unsupervised methods like ICM, providing a scalable path for AI alignment.

Recent advances in Multimodal Large Language Models (MLLMs) have enabled agents to operate in open-ended web and operating system environments. However, existing benchmarks predominantly target consumer-oriented scenarios (e.g., e-commerce and travel booking), failing to capture the complexity and rigor of professional enterprise workflows. Enterprise systems pose distinct challenges, including high-density user interfaces, strict business logic constraints, and a strong reliance on precise, state-consistent information retrieval-settings in which current generalist agents often struggle. To address this gap, we introduce EntWorld, a large-scale benchmark consisting of 1,756 tasks across six representative enterprise domains, including customer relationship management (CRM), information technology infrastructure library (ITIL), and enterprise resource planning (ERP) systems. Unlike previous datasets that depend on fragile execution traces or extensive manual annotation, EntWorld adopts a schema-grounded task generation framework that directly reverse-engineers business logic from underlying database schemas, enabling the synthesis of realistic, long-horizon workflows. Moreover, we propose a SQL-based deterministic verification mechanism in building datasets that replaces ambiguous visual matching with rigorous state-transition validation. Experimental results demonstrate that state-of-the-art models (e.g., GPT-4.1) achieve 47.61% success rate on EntWorld, substantially lower than the human performance, highlighting a pronounced enterprise gap in current agentic capabilities and the necessity of developing domain-specific agents. We release EntWorld as a rigorous testbed to facilitate the development and evaluation of the next generation of enterprise-ready digital agents.

Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resource into a controllable asset, LLMs mitigate the bottlenecks imposed by the acquisition costs of real-world data for model training, evaluation, and system iteration. However, ensuring the high quality of LLM-generated synthetic data remains a critical challenge. Existing research primarily focuses on generation methodologies, with limited direct attention to the quality of the resulting data. Furthermore, most studies are restricted to single modalities, lacking a unified perspective across different data types. To bridge this gap, we propose the \textbf{LLM Data Auditor framework}. In this framework, we first describe how LLMs are utilized to generate data across six distinct modalities. More importantly, we systematically categorize intrinsic metrics for evaluating synthetic data from two dimensions: quality and trustworthiness. This approach shifts the focus from extrinsic evaluation, which relies on downstream task performance, to the inherent properties of the data itself. Using this evaluation system, we analyze the experimental evaluations of representative generation methods for each modality and identify substantial deficiencies in current evaluation practices. Based on these findings, we offer concrete recommendations for the community to improve the evaluation of data generation. Finally, the framework outlines methodologies for the practical application of synthetic data across different modalities.

Large Language Models (LLMs) excel at many tasks but still struggle with a critical ability for LLM-based agents: asking good questions for resolving ambiguity in user requests. While prior work has explored information-seeking behavior through word games, existing benchmarks lack comprehensive evaluation frameworks that provide both final and intermediate signals based on Information Gain (IG). Moreover, they rarely provide systematic comparisons between models that use chain-of-thought reasoning and those that do not. We propose a multi-turn dialogue framework that quantitatively measures how effectively LLMs gather information through yes/no questions in a hierarchical knowledge graph environment. Our framework employs a triad of interacting LLM agents that ask questions, answer them, and update the hypothesis space. We adopt IG as the main metric, grounded in Shannon entropy, to assess query effectiveness at each turn and cumulatively. We instantiate our framework in a geographical Guess My City game setting organized in a five-level taxonomy and evaluate multiple LLM variants under fully and partially observable conditions, with and without Chain-of-Thought reasoning. Our experiments demonstrate that, among the evaluated models, the ones with explicit reasoning capabilities achieve higher IG per turn and reach solutions in fewer steps, particularly in partially observable settings. Analysis of reasoning traces reveals that smaller models compensate for limited capacity through more aggressive exploration of candidate questions, while larger models exhibit higher assertiveness in selecting optimal queries, generating candidates with greater potential IG.

With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local training challenging. To this end, we propose Client-Centric Adaptation federated learning (FedCCA), an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client through selective adaptation, aiming to alleviate the influence of data heterogeneity. Specifically, FedCCA employs dynamic client selection and adaptive aggregation based on the additional client-specific encoder. To enhance multi-source knowledge transfer, we adopt an attention-based global aggregation strategy. We conducted extensive experiments on diverse datasets to assess the efficacy of FedCCA. The experimental results demonstrate that our approach exhibits a substantial performance advantage over competing baselines in addressing this specific problem.

Distributed microphone array (DMA) is a promising next-generation platform for speech interaction, where speech enhancement (SE) is still required to improve the speech quality in noisy cases. Existing SE methods usually first gather raw waveforms at a fusion center (FC) from all devices and then design a multi-microphone model, causing high bandwidth and energy costs. In this work, we propose a \emph{Compress-and-Send Network (CaSNet)} for resource-constrained DMAs, where one microphone serves as the FC and reference. Each of other devices encodes the measured raw data into a feature matrix, which is then compressed by singular value decomposition (SVD) to produce a more compact representation. The received features at the FC are aligned via cross window query with respect to the reference, followed by neural decoding to yield spatially coherent enhanced speech. Experiments on multiple datasets show that the proposed CaSNet can save the data amount with a negligible impact on the performance compared to the uncompressed case. The reproducible code is available at https://github.com/Jokejiangv/CaSNet.

Balanced butterfly counting, corresponding to counting balanced (2, 2)-bicliques, is a fundamental primitive in the analysis of signed bipartite graphs and provides a basis for studying higher-order structural properties such as clustering coefficients and community structure. Although prior work has proposed an efficient CPU-based serial method for counting balanced (2, k)-bicliques. The computational cost of balanced butterfly counting remains a major bottleneck on large-scale graphs. In this work, we present the highly parallel implementations for balanced butterfly counting for both multicore CPUs and GPUs. The proposed multi-core algorithm (M-BBC) employs fine-grained vertex-level parallelism to accelerate wedge-based counting while eliminating the generation of unbalanced substructures. To improve scalability, we develop a GPU-based method (G-BBC) that uses a tile-based parallel approach to effectively leverage shared memory while handling large vertex sets. We then present an improved variation, G-BBC++, which integrates dynamic scheduling to mitigate workload imbalance and maximize throughput. We conduct an experimental assessment of the proposed methods across 15 real-world datasets. Experimental results exhibit that M-BBC achieves speedups of up to 71.13x (average 38.13x) over the sequential baseline BB2K. The GPU-based algorithms deliver even greater improvements, achieving up to 13,320x speedup (average 2,600x) over BB2K and outperforming M-BBC by up to 186x (average 50x). These results indicate the substantial scalability and efficiency of our parallel algorithms and establish a robust foundation for high-performance signed motif analysis on massive bipartite graphs.

Images often communicate more than they literally depict: a set of tools can suggest an occupation and a cultural artifact can suggest a tradition. This kind of indirect visual reference, known as visual metonymy, invites viewers to recover a target concept via associated cues rather than explicit depiction. In this work, we present the first computational investigation of visual metonymy. We introduce a novel pipeline grounded in semiotic theory that leverages large language models and text-to-image models to generate metonymic visual representations. Using this framework, we construct ViMET, the first visual metonymy dataset comprising 2,000 multiple-choice questions to evaluate the cognitive reasoning abilities in multimodal language models. Experimental results on our dataset reveal a significant gap between human performance (86.9%) and state-of-the-art vision-language models (65.9%), highlighting limitations in machines' ability to interpret indirect visual references. Our dataset is publicly available at: https://github.com/cincynlp/ViMET.

A measure of similarity between text embeddings can be considered adequate only if it adheres to the human perception of similarity between texts. In this paper, we introduce the distance-to-distance ratio (DDR), a novel measure of similarity between LLM sentence embeddings. Inspired by Lipschitz continuity, DDR measures the rate of change in similarity between the pre-context word embeddings and the similarity between post-context LLM embeddings, thus measuring the semantic influence of context. We evaluate the performance of DDR in experiments designed as a series of perturbations applied to sentences drawn from a sentence dataset. For each sentence, we generate variants by replacing one, two, or three words with either synonyms, which constitute semantically similar text, or randomly chosen words, which constitute semantically dissimilar text. We compare the performance of DDR with other prevailing similarity metrics and demonstrate that DDR consistently provides finer discrimination between semantically similar and dissimilar texts, even under minimal, controlled edits.

Large language models are increasingly applied to multi-document and long-form inputs, yet long-context inference remains memory- and noise-inefficient. Key-value (KV) caching scales linearly with context length, while external retrieval methods often return lexically similar but causally irrelevant passages. We present S3-Attention, a memory-first inference-time framework that treats long-context processing as attention-aligned endogenous retrieval. S3-Attention decodes transient key and query projections into top-k sparse feature identifiers using lightweight sparse autoencoders, and constructs a CPU-based inverted index mapping features to token positions or spans during a single streaming scan. This design allows the KV cache to be discarded entirely and bounds GPU memory usage by the scan chunk size. At generation time, feature co-activation is used to retrieve compact evidence spans, optionally fused with BM25 for exact lexical matching. Under a unified LongBench evaluation protocol with fixed prompting, decoding, and matched token budgets, S3-Hybrid closely matches full-context inference across multiple model families and improves robustness in several information-dense settings. We also report an engineering limitation of the current prototype, which incurs higher wall-clock latency than optimized full-KV baselines, motivating future kernel-level optimization.

While large language models (LLMs) have substantially improved Text-to-SQL generation, a pronounced gap remains between AI systems and human experts on challenging benchmarks such as BIRD-SQL. We argue this gap stems largely from the prevailing single-pass paradigm, which lacks the iterative reasoning, schema exploration, and error-correction behaviors that humans naturally employ. To address this limitation, we introduce SQL-Trail, a multi-turn reinforcement learning (RL) agentic framework for Text-to-SQL. Rather than producing a query in one shot, SQL-Trail interacts with the database environment and uses execution feedback to iteratively refine its predictions. Our approach centers on two key ideas: (i) an adaptive turn-budget allocation mechanism that scales the agent's interaction depth to match question difficulty, and (ii) a composite reward panel that jointly incentivizes SQL correctness and efficient exploration. Across benchmarks, SQL-Trail sets a new state of the art and delivers strong data efficiency--up to 18x higher than prior single-pass RL state-of-the-art methods. Notably, our 7B and 14B models outperform substantially larger proprietary systems by 5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation.

Statute retrieval is essential for legal assistance and judicial decision support, yet real-world legal queries are often implicit, multi-issue, and expressed in colloquial or underspecified forms. These characteristics make it difficult for conventional retrieval-augmented generation pipelines to recover the statutory elements required for accurate retrieval. Dense retrievers focus primarily on the literal surface form of the query, whereas lightweight rerankers lack the legal-reasoning capacity needed to assess statutory applicability. We present LegalMALR, a retrieval framework that integrates a Multi-Agent Query Understanding System (MAS) with a zero-shot large-language-model-based reranking module (LLM Reranker). MAS generates diverse, legally grounded reformulations and conducts iterative dense retrieval to broaden candidate coverage. To stabilise the stochastic behaviour of LLM-generated rewrites, we optimise a unified MAS policy using Generalized Reinforcement Policy Optimization(GRPO). The accumulated candidate set is subsequently evaluated by the LLM Reranker, which performs natural-language legal reasoning to produce the final ranking. We further construct CSAID, a dataset of 118 difficult Chinese legal queries annotated with multiple statutory labels, and evaluate LegalMALR on both CSAID and the public STARD benchmark. Experiments show that LegalMALR substantially outperforms strong Retrieval-augmented generation(RAG) baselines in both in-distribution and out-of-distribution settings, demonstrating the effectiveness of combining multi-perspective query interpretation, reinforcement-based policy optimisation, and large-model reranking for statute retrieval.

Audio fingerprinting provides an identifiable representation of acoustic signals, which can be later used for identification and retrieval systems. To obtain a discriminative representation, the input audio is usually segmented into shorter time intervals, allowing local acoustic features to be extracted and analyzed. Modern neural approaches typically operate on short, fixed-duration audio segments, yet the choice of segment duration is often made heuristically and rarely examined in depth. In this paper, we study how segment length affects audio fingerprinting performance. We extend an existing neural fingerprinting architecture to adopt various segment lengths and evaluate retrieval accuracy across different segment lengths and query durations. Our results show that short segment lengths (0.5-second) generally achieve better performance. Moreover, we evaluate LLM capacity in recommending the best segment length, which shows that GPT-5-mini consistently gives the best suggestions across five considerations among three studied LLMs. Our findings provide practical guidance for selecting segment duration in large-scale neural audio retrieval systems.

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model's prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Language models are revolutionizing the biochemistry domain, assisting scientists in drug design and chemical synthesis with high efficiency. Yet current approaches struggle between small language models prone to hallucination and limited knowledge retention, and large cloud-based language models plagued by privacy risks and high inference costs. To bridge this gap, we introduce ChemCRAFT, a novel framework leveraging agentic reinforcement learning to decouple chemical reasoning from knowledge storage. Instead of forcing the model to memorize vast chemical data, our approach empowers the language model to interact with a sandbox for precise information retrieval. This externalization of knowledge allows a locally deployable small model to achieve superior performance with minimal inference costs. To enable small language models for agent-calling ability, we build an agentic trajectory construction pipeline and a comprehensive chemical-agent sandbox. Based on sandbox interactions, we constructed ChemToolDataset, the first large-scale chemical tool trajectory dataset. Simultaneously, we propose SMILES-GRPO to build a dense chemical reward function, promoting the model's ability to call chemical agents. Evaluations across diverse aspects of drug design show that ChemCRAFT outperforms current cloud-based LLMs in molecular structure analysis, molecular optimization, and synthesis pathway prediction, demonstrating that scientific reasoning is not solely an emergent ability of model scale, but a learnable policy of tool orchestration. This work establishes a cost-effective and privacy-preserving paradigm for AI-aided chemistry, opening new avenues for accelerating molecular discovery with locally deployable agents.

Due to the fundamental connection between next-symbol prediction and compression, modern predictive models, such as large language models (LLMs), can be combined with entropy coding to achieve compression rates that surpass those of standard compression algorithms. However, this approach relies on the assumption that the predictive model produces identical output distributions at both the encoder and decoder, since even small mismatches can cause the decoding to fail. This assumption often fails with complex predictive models, particularly those based on neural networks, a phenomenon referred to as non-determinism. In this work, we propose a new compression algorithm based on next-token prediction that is robust to arbitrarily large, but structured, prediction mismatches. We prove the correctness of the proposed scheme under a formal mismatch certification, characterize its theoretical performance, and validate it experimentally on real datasets. Our results demonstrate reliable operation within the certified mismatch regime while achieving compression ratios that exceed those of commonly used compression methods.

The Mixture of Experts (MoE) selects a few feed-forward networks (FFNs) per token, achieving an effective trade-off between computational cost and performance. In conventional MoE, each expert is treated as entirely independent, and experts are combined in a discrete space. As a result, when the number of experts increases, it becomes difficult to train each expert effectively. To stabilize training while increasing the number of experts, we propose $\infty$-MoE that selects a portion of the parameters of large FFNs based on continuous values sampled for each token. By considering experts in a continuous space, this approach allows for an infinite number of experts while maintaining computational efficiency. Experiments show that a GPT-2 Small-based $\infty$-MoE model, with 129M active and 186M total parameters, achieves comparable performance to a dense GPT-2 Medium with 350M parameters. Adjusting the number of sampled experts at inference time allows for a flexible trade-off between accuracy and speed, with an improvement of up to 2.5\% in accuracy over conventional MoE.

Bangla, one of the most widely spoken languages, remains underrepresented in state-of-the-art automatic speech recognition (ASR) research, particularly under noisy and speaker-diverse conditions. This paper presents BanglaRobustNet, a hybrid denoising-attention framework built on Wav2Vec-BERT, designed to address these challenges. The architecture integrates a diffusion-based denoising module to suppress environmental noise while preserving Bangla-specific phonetic cues, and a contextual cross-attention module that conditions recognition on speaker embeddings for robustness across gender, age, and dialects. Trained end-to-end with a composite objective combining CTC loss, phonetic consistency, and speaker alignment, BanglaRobustNet achieves substantial reductions in word error rate (WER) and character error rate (CER) compared to Wav2Vec-BERT and Whisper baselines. Evaluations on Mozilla Common Voice Bangla and augmented noisy speech confirm the effectiveness of our approach, establishing BanglaRobustNet as a robust ASR system tailored to low-resource, noise-prone linguistic settings.

We study inverse mechanism learning: recovering an unknown incentive-generating mechanism from observed strategic interaction traces of self-interested learning agents. Unlike inverse game theory and multi-agent inverse reinforcement learning, which typically infer utility/reward parameters inside a structured mechanism, our target includes unstructured mechanism -- a (possibly neural) mapping from joint actions to per-agent payoffs. Unlike differentiable mechanism design, which optimizes mechanisms forward, we infer mechanisms from behavior in an observational setting. We propose DIML, a likelihood-based framework that differentiates through a model of multi-agent learning dynamics and uses the candidate mechanism to generate counterfactual payoffs needed to predict observed actions. We establish identifiability of payoff differences under a conditional logit response model and prove statistical consistency of maximum likelihood estimation under standard regularity conditions. We evaluate DIML with simulated interactions of learning agents across unstructured neural mechanisms, congestion tolling, public goods subsidies, and large-scale anonymous games. DIML reliably recovers identifiable incentive differences and supports counterfactual prediction, where its performance rivals tabular enumeration oracle in small environments and its convergence scales to large, hundred-participant environments. Code to reproduce our experiments is open-sourced.

Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object locations in images, they often fail to faithfully execute the same spatial relations during text-to-image generation, where such relations constitute core semantic information in remote sensing. Motivated by this observation, we propose Uni-RS, the first unified multimodal model tailored for remote sensing, to explicitly address the spatial asymmetry between understanding and generation. Specifically, we first introduce explicit Spatial-Layout Planning to transform textual instructions into spatial layout plans, decoupling geometric planning from visual synthesis. We then impose Spatial-Aware Query Supervision to bias learnable queries toward spatial relations explicitly specified in the instruction. Finally, we develop Image-Caption Spatial Layout Variation to expose the model to systematic geometry-consistent spatial transformations. Extensive experiments across multiple benchmarks show that our approach substantially improves spatial faithfulness in text-to-image generation, while maintaining strong performance on multimodal understanding tasks like image captioning, visual grounding, and VQA tasks.

Despite the impressive reasoning abilities demonstrated by large language models (LLMs), empirical evidence indicates that they are not language agnostic as expected, leading to performance declines in multilingual settings, especially for low-resource languages. We attribute the decline to the model's inconsistent multilingual understanding and reasoning alignment. To address this, we present Pivot-Aligned Self-Feedback Multilingual Reasoning (PASMR), aiming to improve the alignment of multilingual math reasoning abilities in LLMs. This approach designates the model's primary language as the pivot language. During training, the model first translates questions into the pivot language to facilitate better alignment of reasoning patterns. The reasoning process in the target language is then supervised by the pivot language's reasoning answers, thereby establishing a cross-lingual self-feedback mechanism without relying on external correct answers or reward models. Extensive experimental results demonstrate that our method enhances both the model's understanding of questions and its reasoning capabilities, leading to notable task improvements.

This work investigates generative mathematical programming through the lens of Algebraic Modelling Languages (AMLs) and compiler-guided model synthesis. By leveraging PyOPL, an OPL-like AML compiler that provides detailed syntax diagnostics, we introduce SyntAGM, an end-to-end system that translates natural language problem descriptions into PyOPL models via a generate--compile--assess--revise loop. SyntAGM is grammar-aware thanks to in-context exposure to the PyOPL BNF grammar, and benefits from few-shot retrieval of literate PyOPL model exemplars. To obtain a valid PyOPL model that matches the problem description, SyntAGM mobilises compiler feedback and an LLM-based alignment judge. In a comparative study against established prompting baselines SyntAGM achieves competitive accuracy with superior token, cost, and latency profiles.

Efficient key-value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade-off between performance degradation and computational overhead. We propose a novel gating-based KV cache eviction method for frozen-weight LLMs that achieves high compression ratios with negligible computational cost. Our approach introduces lightweight sink-attention gating modules to identify and retain critical KV pairs, and integrates seamlessly into both the prefill and decoding stages. The proposed gate training algorithm relies on forward passes of an LLM, avoiding expensive backpropagation, while achieving strong task generalization through a task-agnostic reconstruction objective. Extensive experiments across the Qwen2.5-1M, Qwen3, and Gemma3 families show that our method maintains near-lossless performance while evicting up to 70% of the KV cache. The results are consistent across a wide range of tasks, including long-context understanding, code comprehension, and mathematical reasoning, demonstrating the generality of our approach.

We propose a provably correct Monte Carlo tree search (MCTS) algorithm for solving \textit{risk-aware} Markov decision processes (MDPs) with \textit{entropic risk measure} (ERM) objectives. We provide a \textit{non-asymptotic} analysis of our proposed algorithm, showing that the algorithm: (i) is \textit{correct} in the sense that the empirical ERM obtained at the root node converges to the optimal ERM; and (ii) enjoys \textit{polynomial regret concentration}. Our algorithm successfully exploits the dynamic programming formulations for solving risk-aware MDPs with ERM objectives introduced by previous works in the context of an upper confidence bound-based tree search algorithm. Finally, we provide a set of illustrative experiments comparing our risk-aware MCTS method against relevant baselines.

Urdu, spoken by 230 million people worldwide, lacks dedicated transformer-based language models and curated corpora. While multilingual models provide limited Urdu support, they suffer from poor performance, high computational costs, and cultural inaccuracies due to insufficient training data. To address these challenges, we present UrduLM, a pretrained Urdu monolingual language model trained in low-resource settings. We curate a 33GB Urdu corpus from diverse sources, develop a custom BPE tokenizer that reduces tokenization overhead by atleast 20-30% compared to multilingual alternatives, and pretrain a 100M-parameter decoder-only model. In few-shot evaluations, UrduLM achieves competitive performance with multilingual models up to 30x its size, reaching 66.6% accuracy on sentiment classification and BLEU scores exceeding 30 on grammar correction tasks. The complete methodology -- including corpus, tokenizer, model weights, and evaluation benchmarks -- is released openly to establish a baseline for Urdu NLP research and provide a scalable framework for other underrepresented languages.

The rise of conspiracy theories has created far-reaching societal harm in the public discourse by eroding trust and fueling polarization. Beyond this public impact lies a deeply personal toll on the friends and families of conspiracy believers, a dimension often overlooked in large-scale computational research. This study fills this gap by systematically mapping radicalization journeys and quantifying the associated emotional toll inflicted on loved ones. We use the prominent case of QAnon as a case study, analyzing 12747 narratives from the r/QAnonCasualties support community through a novel mixed-methods approach. First, we use topic modeling (BERTopic) to map the radicalization trajectories, identifying key pre-existing conditions, triggers, and post-radicalization characteristics. From this, we apply an LDA-based graphical model to uncover six recurring archetypes of QAnon adherents, which we term "radicalization personas." Finally, using LLM-assisted emotion detection and regression modeling, we link these personas to the specific emotional toll reported by narrators. Our findings reveal that these personas are not just descriptive; they are powerful predictors of the specific emotional harms experienced by narrators. Radicalization perceived as a deliberate ideological choice is associated with narrator anger and disgust, while those marked by personal and cognitive collapse are linked to fear and sadness. This work provides the first empirical framework for understanding radicalization as a relational phenomenon, offering a vital roadmap for researchers and practitioners to navigate its interpersonal fallout.

The computing demand of AI is growing at an unprecedented rate, but energy supply is not keeping pace. As a result, energy has become an expensive, contended resource that requires explicit management and optimization. Although recent works have made significant progress in large model training optimization, they focus only on a single aspect of energy consumption: dynamic or static energy. We find that fine-grained kernel scheduling and frequency scaling jointly and interdependently impact both dynamic and static energy consumption. Based on this finding, we design Kareus, a training system that pushes the time--energy tradeoff frontier by optimizing both aspects. Kareus decomposes the intractable joint optimization problem into local, partition-based subproblems. It then uses a multi-pass multi-objective optimization algorithm to find execution schedules that push the time--energy tradeoff frontier. Compared to the state of the art, Kareus reduces training energy by up to 28.3% at the same training time, or reduces training time by up to 27.5% at the same energy consumption.

Quantifying the causal relationship between ice melt and freshwater distribution is critical, as these complex interactions manifest as regional fluctuations in sea surface height (SSH). Leveraging SSH as a proxy for sea ice dynamics enables improved understanding of the feedback mechanisms driving polar climate change and global sea-level rise. However, conventional deep learning models often struggle with reliable treatment effect estimation in spatiotemporal settings due to unobserved confounders and the absence of physical constraints. To address these challenges, we propose the Knowledge-Guided Causal Model Variational Autoencoder (KGCM-VAE) to quantify causal mechanisms between sea ice thickness and SSH. The proposed framework integrates a velocity modulation scheme in which smoothed velocity signals are dynamically amplified via a sigmoid function governed by SSH transitions to generate physically grounded causal treatments. In addition, the model incorporates Maximum Mean Discrepancy (MMD) to balance treated and control covariate distributions in the latent space, along with a causal adjacency-constrained decoder to ensure alignment with established physical structures. Experimental results on both synthetic and real-world Arctic datasets demonstrate that KGCM-VAE achieves superior PEHE compared to state-of-the-art benchmarks. Ablation studies further confirm the effectiveness of the approach, showing that the joint application of MMD and causal adjacency constraints yields a 1.88\% reduction in estimation error.

Empirical risk minimization (ERM) stability is usually studied via single-valued outputs, while convex non-strict losses yield set-valued minimizers. We identify Painlevé-Kuratowski upper semicontinuity (PK-u.s.c.) as the intrinsic stability notion for the ERM solution correspondence (set-level Hadamard well-posedness) and a prerequisite to interpret stability of selections. We then characterize a minimal non-degenerate qualitative regime: Mosco-consistent perturbations and locally bounded minimizers imply PK-u.s.c., minimal-value continuity, and consistency of vanishing-gap near-minimizers. Quadratic growth yields explicit quantitative deviation bounds.

Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand such signals in human cultural contexts, we introduce AVMeme Exam, a human-curated benchmark of over one thousand iconic Internet sounds and videos spanning speech, songs, music, and sound effects. Each meme is paired with a unique Q&A assessing levels of understanding from surface content to context and emotion to usage and world knowledge, along with metadata such as original year, transcript, summary, and sensitivity. We systematically evaluate state-of-the-art multimodal large language models (MLLMs) alongside human participants using this benchmark. Our results reveal a consistent limitation: current models perform poorly on textless music and sound effects, and struggle to think in context and in culture compared to surface content. These findings highlight a key gap in human-aligned multimodal intelligence and call for models that can perceive contextually and culturally beyond the surface of what they hear and see. Project page: avmemeexam.github.io/public

The growing adoption of multimodal Retrieval-Augmented Generation (mRAG) pipelines for vision-centric tasks (e.g. visual QA) introduces important privacy challenges. In particular, while mRAG provides a practical capability to connect private datasets to improve model performance, it risks the leakage of private information from these datasets during inference. In this paper, we perform an empirical study to analyze the privacy risks inherent in the mRAG pipeline observed through standard model prompting. Specifically, we implement a case study that attempts to infer the inclusion of a visual asset, e.g. image, in the mRAG, and if present leak the metadata, e.g. caption, related to it. Our findings highlight the need for privacy-preserving mechanisms and motivate future research on mRAG privacy.

Safety alignment in Large Language Models is critical for healthcare; however, reliance on binary refusal boundaries often results in \emph{over-refusal} of benign queries or \emph{unsafe compliance} with harmful ones. While existing benchmarks measure these extremes, they fail to evaluate Safe Completion: the model's ability to maximise helpfulness on dual-use or borderline queries by providing safe, high-level guidance without crossing into actionable harm. We introduce \textbf{Health-ORSC-Bench}, the first large-scale benchmark designed to systematically measure \textbf{Over-Refusal} and \textbf{Safe Completion} quality in healthcare. Comprising 31,920 benign boundary prompts across seven health categories (e.g., self-harm, medical misinformation), our framework uses an automated pipeline with human validation to test models at varying levels of intent ambiguity. We evaluate 30 state-of-the-art LLMs, including GPT-5 and Claude-4, revealing a significant tension: safety-optimised models frequently refuse up to 80\% of "Hard" benign prompts, while domain-specific models often sacrifice safety for utility. Our findings demonstrate that model family and size significantly influence calibration: larger frontier models (e.g., GPT-5, Llama-4) exhibit "safety-pessimism" and higher over-refusal than smaller or MoE-based counterparts (e.g., Qwen-3-Next), highlighting that current LLMs struggle to balance refusal and compliance. Health-ORSC-Bench provides a rigorous standard for calibrating the next generation of medical AI assistants toward nuanced, safe, and helpful completions. The code and data will be released upon acceptance. \textcolor{red}{Warning: Some contents may include toxic or undesired contents.}

Brain decoding aims to interpret and translate neural activity into behaviors. As such, it is imperative that decoding models are able to generalize across variations, such as recordings from different brain sites, distinct sessions, different types of behavior, and a variety of subjects. Current models can only partially address these challenges and warrant the development of pretrained neural transformer models capable to adapt and generalize. In this work, we propose RPNT - Robust Pretrained Neural Transformer, designed to achieve robust generalization through pretraining, which in turn enables effective finetuning given a downstream task. In particular, RPNT unique components include 1) Multidimensional rotary positional embedding (MRoPE) to aggregate experimental metadata such as site coordinates, session name and behavior types; 2) Context-based attention mechanism via convolution kernels operating on global attention to learn local temporal structures for handling non-stationarity of neural population activity; 3) Robust self-supervised learning (SSL) objective with uniform causal masking strategies and contrastive representations. We pretrained two separate versions of RPNT on distinct datasets a) Multi-session, multi-task, and multi-subject microelectrode benchmark; b) Multi-site recordings using high-density Neuropixel 1.0 probes. The datasets include recordings from the dorsal premotor cortex (PMd) and from the primary motor cortex (M1) regions of nonhuman primates (NHPs) as they performed reaching tasks. After pretraining, we evaluated the generalization of RPNT in cross-session, cross-type, cross-subject, and cross-site downstream behavior decoding tasks. Our results show that RPNT consistently achieves and surpasses the decoding performance of existing decoding models in all tasks.

Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support three NDP paradigms: in-storage processing (ISP), processing using DRAM in the SSD (PuD-SSD), and in-flash processing (IFP). A large body of prior SSD-based NDP techniques operate in isolation, mapping computations to only one or two NDP paradigms (i.e., ISP, PuD-SSD, or IFP) within the SSD. These techniques (1) are tailored to specific workloads or kernels, (2) do not exploit the full computational potential of an SSD, and (3) lack programmer-transparency. While several prior works propose techniques to partition computation between the host and near-memory accelerators, adapting these techniques to SSDs has limited benefits because they (1) ignore the heterogeneity of the SSD resources, and (2) make offloading decisions based on limited factors such as bandwidth utilization, or data movement cost. We propose Conduit, a general-purpose, programmer-transparent NDP framework for SSDs that leverages multiple SSD computation resources. At compile time, Conduit executes a custom compiler (e.g., LLVM) pass that (i) vectorizes suitable application code segments into SIMD operations that align with the SSD's page layout, and (ii) embeds metadata (e.g., operation type, operand sizes) into the vectorized instructions to guide runtime offloading decisions. At runtime, within the SSD, Conduit performs instruction-granularity offloading by evaluating six key features, and uses a cost function to select the most suitable SSD resource. We evaluate Conduit and two prior NDP offloading techniques using an in-house event-driven SSD simulator on six data-intensive workloads. Conduit outperforms the best-performing prior offloading policy by 1.8x and reduces energy consumption by 46%.

AI coding agents can autonomously generate pull requests (PRs), yet little is known about how their contributions compare to those of humans. We analyze 33,596 agent-generated PRs (APRs) and 6,618 human PRs (HPRs) to compare code-change characteristics and message quality. We observe that APR-introduced symbols (functions and classes) are removed much sooner than those in HPRs (median time to removal 3 vs. 34 days) and are also removed more often (symbol churn 7.33% vs. 4.10%), reflecting a focus on other tasks like documentation and test updates. Agents generate stronger commit-level messages (semantic similarity 0.72 vs. 0.68) but lag humans at PR-level summarization (PR-commit similarity 0.86 vs. 0.88). Commit message length is the best predictor of description quality, indicating reliance on individual commits over full-PR reasoning. These findings highlight a gap between agents' micro-level precision and macro-level communication, suggesting opportunities to improve agent-driven development workflows.

Transformer-based neural decoders with large parameter counts, pre-trained on large-scale datasets, have recently outperformed classical machine learning models and small neural networks on brain-computer interface (BCI) tasks. However, their large parameter counts and high computational demands hinder deployment in power-constrained implantable systems. To address this challenge, we introduce BrainDistill, a novel implantable motor decoding pipeline that integrates an implantable neural decoder (IND) with a task-specific knowledge distillation (TSKD) framework. Unlike standard feature distillation methods that attempt to preserve teacher representations in full, TSKD explicitly prioritizes features critical for decoding through supervised projection. Across multiple neural datasets, IND consistently outperforms prior neural decoders on motor decoding tasks, while its TSKD-distilled variant further surpasses alternative distillation methods in few-shot calibration settings. Finally, we present a quantization-aware training scheme that enables integer-only inference with activation clipping ranges learned during training. The quantized IND enables deployment under the strict power constraints of implantable BCIs with minimal performance loss.

We introduce Memento, a conversational AR assistant that permanently captures and memorizes user's verbal queries alongside their spatiotemporal and activity contexts. By storing these "memories," Memento discovers connections between users' recurring interests and the contexts that trigger them. Upon detection of similar or identical spatiotemporal activity, Memento proactively recalls user interests and delivers up-to-date responses through AR, seamlessly integrating AR experience into their daily routine. Unlike prior work, each interaction in Memento is not a transient event, but a connected series of interactions with coherent long--term perspective, tailored to the user's broader multimodal (visual, spatial, temporal, and embodied) context. We conduct preliminary evaluation through user feedbacks with participants of diverse expertise in immersive apps, and explore the value of proactive context-aware AR assistant in everyday settings. We share our findings and challenges in designing a proactive, context-aware AR system.

LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understanding of how agentic search sessions unfold and how retrieved evidence is used. This paper presents a large-scale log analysis of agentic search based on 14.44M search requests (3.97M sessions) collected from DeepResearchGym, i.e. an open-source search API accessed by external agentic clients. We sessionize the logs, assign session-level intents and step-wise query-reformulation labels using LLM-based annotation, and propose Context-driven Term Adoption Rate (CTAR) to quantify whether newly introduced query terms are traceable to previously retrieved evidence. Our analyses reveal distinctive behavioral patterns. First, over 90% of multi-turn sessions contain at most ten steps, and 89% of inter-step intervals fall under one minute. Second, behavior varies by intent. Fact-seeking sessions exhibit high repetition that increases over time, while sessions requiring reasoning sustain broader exploration. Third, agents reuse evidence across steps. On average, 54% of newly introduced query terms appear in the accumulated evidence context, with contributions from earlier steps beyond the most recent retrieval. The findings suggest that agentic search may benefit from repetition-aware early stopping, intent-adaptive retrieval budgets, and explicit cross-step context tracking. We plan to release the anonymized logs to support future research.

Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task-Agnostic Continual Learning, referred to as SETA, a framework that resolves the plasticity-stability conflict by decomposing the model into modular subspaces. Unlike standard updates, where tasks compete for the same parameters, SETA separates knowledge into unique experts, designed to isolate task-specific patterns, and shared experts, responsible for capturing common features. This structure is maintained through elastic weight anchoring, which protects critical shared knowledge and enables a unified gating network to automatically retrieve the correct expert combination for each task during inference. Extensive experiments across diverse domain-specific and general benchmarks demonstrate that SETA consistently outperforms state-of-the-art parameter-efficient fine-tuning-based continual learning methods.

Prefetching and off-chip prediction are two techniques proposed to hide long memory access latencies in high-performance processors. In this work, we demonstrate that: (1) prefetching and off-chip prediction often provide complementary performance benefits, yet (2) naively combining them often fails to realize their full performance potential, and (3) existing prefetcher control policies leave significant room for performance improvement behind. Our goal is to design a holistic framework that can autonomously learn to coordinate an off-chip predictor with multiple prefetchers employed at various cache levels. To this end, we propose a new technique called Athena, which models the coordination between prefetchers and off-chip predictor (OCP) as a reinforcement learning (RL) problem. Athena acts as the RL agent that observes multiple system-level features (e.g., prefetcher/OCP accuracy, bandwidth usage) over an epoch of program execution, and uses them as state information to select a coordination action (i.e., enabling the prefetcher and/or OCP, and adjusting prefetcher aggressiveness). At the end of every epoch, Athena receives a numerical reward that measures the change in multiple system-level metrics (e.g., number of cycles taken to execute an epoch). Athena uses this reward to autonomously and continuously learn a policy to coordinate prefetchers with OCP. Our extensive evaluation using a diverse set of memory-intensive workloads shows that Athena consistently outperforms prior state-of-the-art coordination policies across a wide range of system configurations with various combinations of underlying prefetchers, OCPs, and main memory bandwidths, while incurring only modest storage overhead. Athena is freely available at https://github.com/CMU-SAFARI/Athena.

Sound event localization and detection with distance estimation (3D SELD) in video involves identifying active sound events at each time frame while estimating their spatial coordinates. This multimodal task requires joint reasoning across semantic, spatial, and temporal dimensions, a challenge that single models often struggle to address effectively. To tackle this, we introduce the Team of Specialists (ToS) ensemble framework, which integrates three complementary sub-networks: a spatio-linguistic model, a spatio-temporal model, and a tempo-linguistic model. Each sub-network specializes in a unique pair of dimensions, contributing distinct insights to the final prediction, akin to a collaborative team with diverse expertise. ToS has been benchmarked against state-of-the-art audio-visual models for 3D SELD on the DCASE2025 Task 3 Stereo SELD development set, consistently outperforming existing methods across key metrics. Future work will extend this proof of concept by strengthening the specialists with appropriate tasks, training, and pre-training curricula.

In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that struggle to generalize to real-world populations. Large language models contain extensive knowledge from years of research across these domains. We propose LoID (Logit-Informed Distributions), a deterministic method for extracting informative prior distributions for Bayesian logistic regression by directly accessing their token-level predictions. Rather than relying on generated text, we probe the model's confidence in opposing semantic directions (positive vs. negative impact) through carefully constructed sentences. By measuring how consistently the LLM favors one direction across diverse phrasings, we extract the strength and reliability of the model's belief about each feature's influence. We evaluate LoID on ten real-world tabular datasets under synthetic out-of-distribution (OOD) settings characterized by covariate shift, where the training data represents only a subset of the population. We compare our approach against (1) standard uninformative priors, (2) AutoElicit, a recent method that prompts LLMs to generate priors via text completions, (3) LLMProcesses, a method that uses LLMs to generate numerical predictions through in-context learning and (4) an oracle-style upper bound derived from fitting logistic regression on the full dataset. We assess performance using Area Under the Curve (AUC). Across datasets, LoID significantly improves performance over logistic regression trained on OOD data, recovering up to \textbf{59\%} of the performance gap relative to the oracle model. LoID outperforms AutoElicit and LLMProcessesc on 8 out of 10 datasets, while providing a reproducible and computationally efficient mechanism for integrating LLM knowledge into Bayesian inference.

Learning systems acquire structured internal representations from data, yet classical information-theoretic results state that deterministic transformations do not increase information. This raises a fundamental question: how can learning produce abstraction and insight without violating information-theoretic limits? We argue that learning is inherently an irreversible process when performed over finite time, and that the realization of epistemic structure necessarily incurs entropy production. To formalize this perspective, we model learning as a transport process in the space of probability distributions over model configurations and introduce an epistemic free-energy framework. Within this framework, we define the free-energy drop as a bookkeeping quantity that records the total reduction of epistemic free energy along a learning trajectory. This reduction decomposes into a reversible component associated with potential improvement and an irreversible component corresponding to entropy production. We then derive the Epistemic Speed Limit (ESL), a finite-time inequality that lower-bounds the minimal entropy production required by any learning process to realize a given distributional transformation. This bound depends only on the Wasserstein distance between initial and final ensemble distributions and is independent of the specific learning algorithm.

Performant all-to-all collective operations in MPI are critical to fast Fourier transforms, transposition, and machine learning applications. There are many existing implementations for all-to-all exchanges on emerging systems, with the achieved performance dependent on many factors, including message size, process count, architecture, and parallel system partition. This paper presents novel all-to-all algorithms for emerging many-core systems. Further, the paper presents a performance analysis against existing algorithms and system MPI, with novel algorithms achieving up to 3x speedup over system MPI at 32 nodes of state-of-the-art Sapphire Rapids systems.

Large Language Models (LLMs) are widely used to support software developers in tasks such as code generation, optimization, and documentation. However, their ability to improve existing programming answers in a human-like manner remains underexplored. On technical question-and-answer platforms such as Stack Overflow (SO), contributors often revise answers based on user comments that identify errors, inefficiencies, or missing explanations. Yet roughly one-third of this feedback is never addressed due to limited time, expertise, or visibility, leaving many answers incomplete or outdated. This study investigates whether LLMs can enhance programming answers by interpreting and incorporating comment-based feedback. We make four main contributions. First, we introduce ReSOlve, a benchmark consisting of 790 SO answers with associated comment threads, annotated for improvement-related and general feedback. Second, we evaluate four state-of-the-art LLMs on their ability to identify actionable concerns, finding that DeepSeek achieves the best balance between precision and recall. Third, we present AUTOCOMBAT, an LLM-powered tool that improves programming answers by jointly leveraging user comments and question context. Compared to human revised references, AUTOCOMBAT produces near-human quality improvements while preserving the original intent and significantly outperforming the baseline. Finally, a user study with 58 practitioners shows strong practical value, with 84.5 percent indicating they would adopt or recommend the tool. Overall, AUTOCOMBAT demonstrates the potential of scalable, feedback-driven answer refinement to improve the reliability and trustworthiness of technical knowledge platforms.

We study Transformer overparameterization through the lens of angular similarity in high-dimensional encoder-decoder embeddings. We apply Bernoulli dropout between the encoder and the decoder, varying the keep probability $p$ to identify a sparsity-dependent threshold above which the Top-1 prediction is preserved. Theoretically, we prove that, if the effective sparsity embeddings is sufficiently large, and thus decoder performance, remain stable under moderate coordinate dropout. Empirically, we implement the Bernoulli dropout by constructing a new Transformer model augmented with Binary Erasure Channel (BEC) and test its performance on an English-French translation task. Experimental results visualize the trends for validation accuracies and BLEU scores, both decline sharply at some threshold.

Deep reinforcement learning (DRL) has driven major advances in autonomous control. Still, standard Deep Q-Network (DQN) agents tend to rely on fixed learning rates and uniform update scaling, even as updates are modulated by temporal-difference (TD) error. This rigidity destabilizes convergence, especially in sparse-reward settings where feedback is infrequent. We introduce Deep Intrinsic Surprise-Regularized Control (DISRC), a biologically inspired augmentation to DQN that dynamically scales Q-updates based on latent-space surprise. DISRC encodes states via a LayerNorm-based encoder and computes a deviation-based surprise score relative to a moving latent setpoint. Each update is then scaled in proportion to both TD error and surprise intensity, promoting plasticity during early exploration and stability as familiarity increases. We evaluate DISRC on two sparse-reward MiniGrid environments, which included MiniGrid-DoorKey-8x8 and MiniGrid-LavaCrossingS9N1, under identical settings as a vanilla DQN baseline. In DoorKey, DISRC reached the first successful episode (reward > 0.8) 33% faster than the vanilla DQN baseline (79 vs. 118 episodes), with lower reward standard deviation (0.25 vs. 0.34) and higher reward area under the curve (AUC: 596.42 vs. 534.90). These metrics reflect faster, more consistent learning - critical for sparse, delayed reward settings. In LavaCrossing, DISRC achieved a higher final reward (0.95 vs. 0.93) and the highest AUC of all agents (957.04), though it converged more gradually. These preliminary results establish DISRC as a novel mechanism for regulating learning intensity in off-policy agents, improving both efficiency and stability in sparse-reward domains. By treating surprise as an intrinsic learning signal, DISRC enables agents to modulate updates based on expectation violations, enhancing decision quality when conventional value-based methods fall short.

Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. To address this, we introduce MLE-Ideator, a dual-agent framework that separates ideation from implementation. In our system, an implementation agent can request strategic help from a dedicated Ideator. We show this approach is effective in two ways. First, in a training-free setup, our framework significantly outperforms implementation-only agent baselines on MLE-Bench. Second, we demonstrate that the Ideator can be trained with reinforcement learning (RL) to generate more effective ideas. With only 1K training samples from 10 MLE tasks, our RL-trained Qwen3-8B Ideator achieves an 11.5% relative improvement compared to its untrained counterpart and surpasses Claude Sonnet 3.5. These results highlights a promising path toward training strategic AI systems for scientific discovery.

Recent progress in large language models has renewed interest in mechanistically characterizing how multi-step reasoning is represented and computed. While much prior work treats reasoning as a linear chain of steps, many reasoning problems are more naturally structured as directed acyclic graphs (DAGs), where intermediate conclusions may depend on multiple premises, branch into parallel sub-derivations, and later merge or be reused. Understanding whether such graph-structured reasoning is reflected in model internals remains an open question. In this work, we introduce Reasoning DAG Probing, a framework that directly asks whether LLM hidden states encode the geometry of a reasoning DAG in a linearly accessible form, and where this structure emerges across layers. Within this framework, we associate each reasoning node with a textual realization and train lightweight probes to predict two graph-theoretic properties from hidden states: node depth and pairwise node distance. We use these probes to analyze the layerwise emergence of DAG structure and evaluate controls that disrupt reasoning-relevant structure while preserving superficial textual properties. Our results provide evidence that reasoning DAG geometry is meaningfully encoded in intermediate layers, with recoverability varying systematically by node depth and model scale, suggesting that LLM reasoning is not only sequential but exhibits measurable internal graph structure.

While thousands of satellites photograph Earth every day, most of that data never makes it to the ground because downlink bandwidth simply cannot keep up. Processing data in the Low Earth Orbit (LEO) zone offers promising capabilities to overcome this limitation. We propose SpaceCoMP, a MapReduce-inspired processing model for LEO satellite mesh networks. Ground stations submit queries over an area of interest; satellites collect sensor data, process it cooperatively at light-speed using inter-satellite laser links, and return only the results. Our compute model leverages space physics to accelerate computations on LEO megaconstellations. Our distance-aware routing protocol exploits orbital geometry. In addition, our bipartite match scheduling strategy places map and reduce tasks within orbital regions while minimizing aggregation costs. We have simulated constellations of 1,000-10,000 satellites showcasing 61-79% improvement in map placement efficiency over baselines, 18-28% over greedy allocation, and 67-72% reduction in aggregation cost. SpaceCoMP demonstrates that the orbital mesh is not merely useful as a communication relay, as seen today, but can provide the foundations for faster data processing above the skies.

Recent advances in LLMs have reignited scientific debate over whether embodiment is necessary for intelligence. We present the argument that intelligence requires grounding, a phenomenon entailed by embodiment, but not embodiment itself. We define intelligence as the possession of four properties -- motivation, predictive ability, understanding of causality, and learning from experience -- and argue that each can be achieved by a non-embodied, grounded agent. We use this to conclude that grounding, not embodiment, is necessary for intelligence. We then present a thought experiment of an intelligent LLM agent in a digital environment and address potential counterarguments.

Configuring the parameters of additive manufacturing processes for metal alloys is a challenging problem due to complex relationships between input parameters (e.g., laser power, scan speed) and quality of printed outputs. The standard trial-and-error approach to find feasible parameter configurations is highly inefficient because validating each configuration is expensive in terms of resources (physical and human labor) and the configuration space is very large. This paper combines the general principles of AI-driven adaptive experimental design with domain knowledge to address the challenging problem of discovering feasible configurations. The key idea is to build a surrogate model from past experiments to intelligently select a small batch of input configurations for validation in each iteration. To demonstrate the effectiveness of this methodology, we deploy it for Directed Energy Deposition process to print GRCop--42, a high-performance copper--chromium--niobium alloy developed by NASA for aerospace applications. Within three months, our approach yielded multiple defect-free outputs across a range of laser powers dramatically reducing time to result and resource expenditure compared to several months of manual experimentation by domain scientists with no success. By enabling high-quality GRCop--42 fabrication on readily available infrared laser platforms for the first time, we democratize access to this critical alloy, paving the way for cost-effective, decentralized production for aerospace applications.

Deep learning models in medical image analysis often struggle with generalizability across domains and demographic groups due to data heterogeneity and scarcity. Traditional augmentation improves robustness, but fails under substantial domain shifts. Recent advances in stylistic augmentation enhance domain generalization by varying image styles but fall short in terms of style diversity or by introducing artifacts into the generated images. To address these limitations, we propose Stylizing ViT, a novel Vision Transformer encoder that utilizes weight-shared attention blocks for both self- and cross-attention. This design allows the same attention block to maintain anatomical consistency through self-attention while performing style transfer via cross-attention. We assess the effectiveness of our method for domain generalization by employing it for data augmentation on three distinct image classification tasks in the context of histopathology and dermatology. Results demonstrate an improved robustness (up to +13% accuracy) over the state of the art while generating perceptually convincing images without artifacts. Additionally, we show that Stylizing ViT is effective beyond training, achieving a 17% performance improvement during inference when used for test-time augmentation. The source code is available at https://github.com/sdoerrich97/stylizing-vit .

Modern language models (LMs) are trained in an autoregressive manner, conditioned only on the prefix. In contrast, sequence labeling (SL) tasks assign labels to each individual input token, naturally benefiting from bidirectional context. This discrepancy has historically led SL to rely on inherently bidirectional encoder-only models. However, the rapid development of decoder-only models has raised the question of whether they can be adapted to SL. While causal mask removal has emerged as a viable technique for adapting decoder-only models to leverage the full context for SL, it requires considerable changes to the base model functionality. In this work, we explore sequence repetition (SR) as a less invasive alternative for enabling bidirectionality in decoder-only models. Through fine-tuning experiments, we show that SR inherently makes decoders bidirectional, improving the quality of token-level embeddings and surpassing encoders and unmasked decoders. Contrary to earlier claims, we find that increasing the number of repetitions does not degrade SL performance. Finally, we demonstrate that embeddings from intermediate layers are highly effective for SR, comparable to those from final layers, while being significantly more efficient to compute. Our findings underscore that SR alleviates the structural limitations of decoders, enabling more efficient and adaptable LMs and broadening their applicability to other token-level tasks.

Large language models are increasingly used in software development, yet their ability to generate and maintain large, multi module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420 line Terminal User Interface framework for the Ring programming language, completed in roughly ten hours of active work spread across three days using a purely prompt driven workflow with Claude Code, Opus 4.5. The system was produced through 107 prompts: 21 feature requests, 72 bug fix prompts, 9 prompts sharing information from Ring documentation, 4 prompts providing architectural guidance, and 1 prompt dedicated to generating documentation. Development progressed across five phases, with the Window Manager phase requiring the most interaction, followed by complex UI systems and controls expansion. Bug related prompts covered redraw issues, event handling faults, runtime errors, and layout inconsistencies, while feature requests focused primarily on new widgets, window manager capabilities, and advanced UI components. Most prompts were short, reflecting a highly iterative workflow in which the human role was limited to specifying requirements, validating behaviour, and issuing corrective prompts without writing any code manually. The resulting framework includes a complete windowing subsystem, event driven architecture, interactive widgets, hierarchical menus, grid and tree components, tab controls, and a multi window desktop environment. By combining quantitative prompt analysis with qualitative assessment of model behaviour, this study provides empirical evidence that modern LLMs can sustain architectural coherence and support the construction of production grade tooling for emerging programming languages, highlighting prompt driven development as a viable methodology within software engineering practice.

Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics-and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.

AI coding agents are increasingly acting as autonomous contributors by generating and submitting pull requests (PRs). However, we lack empirical evidence on how these agent-generated PRs differ from human contributions, particularly in how they modify code and describe their changes. Understanding these differences is essential for assessing their reliability and impact on development workflows. Using the MSR 2026 Mining Challenge version of the AIDev dataset, we analyze 24,014 merged Agentic PRs (440,295 commits) and 5,081 merged Human PRs (23,242 commits). We examine additions, deletions, commits, and files touched, and evaluate the consistency between PR descriptions and their diffs using lexical and semantic similarity. Agentic PRs differ substantially from Human PRs in commit count (Cliff's $δ= 0.5429$) and show moderate differences in files touched and deleted lines. They also exhibit slightly higher description-to-diff similarity across all measures. These findings provide a large-scale empirical characterization of how AI coding agents contribute to open source development.

The R ecosystem offers a rich variety of map-reduce application programming interfaces (APIs) for iterative computations, yet parallelizing code across these diverse frameworks requires learning multiple, often incompatible, parallel APIs. The futurize package addresses this challenge by providing a single function, futurize(), which transpiles sequential map-reduce expressions into their parallel equivalents in the future ecosystem, which performs all the heavy lifting. By leveraging R's native pipe operator, users can parallelize existing code with minimal refactoring -- often by simply appending `|> futurize()' to an expression. The package supports classical map-reduce functions from base R, purrr, crossmap, foreach, plyr, BiocParallel, e.g., lapply(xs, fcn) |> futurize() and map(xs, fcn) |> futurize(), as well as a growing set of domain-specific packages, e.g., boot, caret, glmnet, lme4, mgcv, and tm. By abstracting away the underlying parallel machinery, and unifying handling of future options, the package enables developers to declare what to parallelize via futurize(), and end-users to choose how via plan(). This article describes the philosophy, design, and implementation of futurize, demonstrates its usage across various map-reduce paradigms, and discusses its role in simplifying parallel computing in R.

From school playgrounds to corporate boardrooms, status hierarchies -- rank orderings based on respect and perceived competence -- are universal features of human social organization. Language models trained on human-generated text inevitably encounter these hierarchical patterns embedded in language, raising the question of whether they might reproduce such dynamics in multi-agent settings. This thesis investigates when and how language models form status hierarchies by adapting Berger et al.'s (1972) expectation states framework. I create multi-agent scenarios where separate language model instances complete sentiment classification tasks, are introduced with varying status characteristics (e.g., credentials, expertise), then have opportunities to revise their initial judgments after observing their partner's responses. The dependent variable is deference, the rate at which models shift their ratings toward their partner's position based on status cues rather than task information. Results show that language models form significant status hierarchies when capability is equal (35 percentage point asymmetry, p < .001), but capability differences dominate status cues, with the most striking effect being that high-status assignments reduce higher-capability models' deference rather than increasing lower-capability models' deference. The implications for AI safety are significant: status-seeking behavior could introduce deceptive strategies, amplify discriminatory biases, and scale across distributed deployments far faster than human hierarchies form organically. This work identifies emergent social behaviors in AI systems and highlights a previously underexplored dimension of the alignment challenge.

This study presents ME-WARD (Multimodal Ergonomic Workplace Assessment and Risk from Data), a novel system for ergonomic assessment and musculoskeletal risk evaluation that implements the Rapid Upper Limb Assessment (RULA) method. ME-WARD is designed to process joint angle data from motion capture systems, including inertial measurement unit (IMU)-based setups, and deep learning human body pose tracking models. The tool's flexibility enables ergonomic risk assessment using any system capable of reliably measuring joint angles, extending the applicability of RULA beyond proprietary setups. To validate its performance, the tool was tested in an industrial setting during the assembly of conveyor belts, which involved high-risk tasks such as inserting rods and pushing conveyor belt components. The experiments leveraged gold standard IMU systems alongside a state-of-the-art monocular 3D pose estimation system. The results confirmed that ME-WARD produces reliable RULA scores that closely align with IMU-derived metrics for flexion-dominated movements and comparable performance with the monocular system, despite limitations in tracking lateral and rotational motions. This work highlights the potential of integrating multiple motion capture technologies into a unified and accessible ergonomic assessment pipeline. By supporting diverse input sources, including low-cost video-based systems, the proposed multimodal approach offers a scalable, cost-effective solution for ergonomic assessments, paving the way for broader adoption in resource-constrained industrial environments.

Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and correlated trajectories. We address these limitations by reformulating episode selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solving it with quantum-inspired samplers. Our method, MC+QUBO, integrates a combinatorial filtering step into standard MC policy evaluation: from each batch of trajectories, we select a subset that maximizes cumulative reward while promoting state-space coverage. This selection is encoded as a QUBO, where linear terms favor high-reward episodes and quadratic terms penalize redundancy. We explore both Simulated Quantum Annealing (SQA) and Simulated Bifurcation (SB) as black-box solvers within this framework. Experiments in a finite-horizon GridWorld demonstrate that MC+QUBO outperforms vanilla MC in convergence speed and final policy quality, highlighting the potential of quantum-inspired optimization as a decision-making subroutine in reinforcement learning.

Personalization is crucial for aligning Large Language Model (LLM) outputs with individual user preferences and background knowledge. State-of-the-art solutions are based on retrieval augmentation, where relevant context from a user profile is retrieved for LLM consumption. These methods deal with a trade-off between exposing retrieved private data to cloud providers and relying on less capable local models. We introduce $P^3$, an interactive framework for high-quality personalization without revealing private profiles to server-side LLMs. In $P^3$, a large server-side model generates a sequence of $k$ draft tokens based solely on the user query, while a small client-side model, with retrieval access to the user's private profile, evaluates and modifies these drafts to better reflect user preferences. This process repeats until an end token is generated. Experiments on LaMP-QA, a recent benchmark consisting of three personalized question answering datasets, show that $P^3$ consistently outperforms both non-personalized server-side and personalized client-side baselines, achieving statistically significant improvements of $7.4%$ to $9%$ on average. Importantly, $P^3$ recovers $90.3%$ to $95.7%$ of the utility of a ``leaky'' upper-bound scenario in which the full profile is exposed to the large server-side model. Privacy analyses, including linkability and attribute inference attacks, indicate that $P^3$ preserves the privacy of a non-personalized server-side model, introducing only marginal additional leakage ($1.5%$--$3.5%$) compared to submitting a query without any personal context. Additionally, the framework is efficient for edge deployment, with the client-side model generating only $9.2%$ of the total tokens. These results demonstrate that $P^3$ provides a practical, effective solution for personalized generation with improved privacy.

Trending news detection in low-traffic search environments faces a fundamental cold-start problem, where a lack of query volume prevents systems from identifying emerging or long-tail trends. Existing methods relying on keyword frequency or query spikes are inherently slow and ineffective in these sparse settings, lagging behind real-world shifts in attention. We introduce RTTP, a novel Real-Time Trending Prediction framework that generates search queries directly from news content instead of waiting for users to issue them. RTTP leverages a continual learning LLM (CL-LLM) that converts posts into search-style queries and scores them using engagement strength + creator authority, enabling early trend surfacing before search volume forms. To ensure adaptation without degrading reasoning, we propose Mix-Policy DPO, a new preference-based continual learning approach that combines on-policy stability with off-policy novelty to mitigate catastrophic forgetting during model upgrades. Deployed at production scale on Facebook and Meta AI products, RTTP delivers +91.4% improvement in tail-trend detection precision@500 and +19% query generation accuracy over industry baselines, while sustaining stable performance after multi-week online training. This work demonstrates that LLM-generated synthetic search signals, when aligned and continually updated, unlock timely trend understanding in low-traffic search environments.

The Abstraction and Reasoning Corpus (ARC) tests AI systems' ability to perform human-like inductive reasoning from a few demonstration pairs. Existing Gymnasium-based RL environments severely limit experimental scale due to computational bottlenecks. We present JaxARC, an open-source, high-performance RL environment for ARC implemented in JAX. Its functional, stateless architecture enables massive parallelism, achieving 38-5,439x speedup over Gymnasium at matched batch sizes, with peak throughput of 790M steps/second. JaxARC supports multiple ARC datasets, flexible action spaces, composable wrappers, and configuration-driven reproducibility, enabling large-scale RL research previously computationally infeasible. JaxARC is available at https://github.com/aadimator/JaxARC.

State-of-the-art imitation learning from observation methods (ILfO) have recently made significant progress, but they still have some limitations: they need action-based supervised optimisation, assume that states have a single optimal action, and tend to apply teacher actions without full consideration of the actual environment state. While the truth may be out there in observed trajectories, existing methods struggle to extract it without supervision. In this work, we propose Unsupervised Imitation Learning from Observation (UfO) that addresses all of these limitations. UfO learns a policy through a two-stage process, in which the agent first obtains an approximation of the teacher's true actions in the observed state transitions, and then refines the learned policy further by adjusting agent trajectories to closely align them with the teacher's. Experiments we conducted in five widely used environments show that UfO not only outperforms the teacher and all other ILfO methods but also displays the smallest standard deviation. This reduction in standard deviation indicates better generalisation in unseen scenarios.

This work presents a systematic analysis and extension of the sparse radial basis function network (SparseRBFnet) previously introduced for solving nonlinear partial differential equations (PDEs). Based on its adaptive-width shallow kernel network formulation, we further investigate its function-space characterization, operator evaluation, and computational algorithm. We provide a unified description of the solution space for a broad class of radial basis functions (RBFs). Under mild assumptions, this space admits a characterization as a Besov space, independent of the specific kernel choice. We further demonstrate how the explicit kernel-based structure enables quasi-analytical evaluation of both differential and nonlocal operators, including fractional Laplacians. On the computational end, we study the adaptive-width network and related three-phase training strategy through a comparison with variants concerning the modeling and algorithmic details. In particular, we assess the roles of second-order optimization, inner-weight training, network adaptivity, and anisotropic kernel parameterizations. Numerical experiments on high-order, fractional, and anisotropic PDE benchmarks illustrate the empirical insensitivity to kernel choice, as well as the resulting trade-offs between accuracy, sparsity, and computational cost. Collectively, these results consolidate and generalize the theoretical and computational framework of SparseRBFnet, supporting accurate sparse representations with efficient operator evaluation and offering theory-grounded guidance for algorithmic and modeling choices.

This paper presents an open-source software application for analyzing traffic camera footage, focusing on vehicle behavior and braking events at signalized urban highways. The core innovation is a robust ground-plane homography estimation that links fixed traffic camera views to satellite orthoimagery. This process rectifies the camera's oblique perspective, ensuring that pixel distances accurately represent real-world distances. This enables the acquisition of features such as vehicle trajectory, speed, deceleration, and braking severity without the need for camera calibration. The pipeline employs the MAGSAC++ estimator to build the homography, converting YOLO11 object detections into a rectified top-down coordinate system. All detection and trajectory data are stored in a ClickHouse database for subsequent analysis. A real-world case study at two signalized intersections in Key West, Florida, showcased the system's capabilities. Across two days of daytime footage, braking activity at the higher-volume intersection peaked around 4 PM at approximately 57.5 events per hour, while the second intersection peaked around 10 AM at roughly 15.5 events per hour. The spatial analysis revealed that most braking events initiated upstream, with mild and moderate braking mostly occurring 30 to 45+ meters away from the stop bar and severe braking distributed throughout, but particularly concentrated in lanes with higher interaction and merging activity. The findings highlight the significant potential of this centralized safety information system to support connected vehicles, facilitating proactive traffic management, crash mitigation, and data-driven roadway design and safety analysis.

Large language models (LLMs) demonstrate remarkable capabilities, but their broad deployment is limited by significant computational resource demands, particularly energy consumption during inference. Static, one-model-fits-all inference strategies are often inefficient, as they do not exploit the diverse range of available models or adapt to varying query requirements. This paper presents GreenServ, a dynamic, context-aware routing framework that optimizes the trade-off between inference accuracy and energy efficiency. GreenServ extracts lightweight contextual features from each query, including task type, semantic cluster, and text complexity, and routes queries to the most suitable model from a heterogeneous pool, based on observed accuracy and energy usage. We employ a multi-armed bandit approach to learn adaptive routing policies online. This approach operates under partial feedback, eliminates the need for extensive offline calibration, and streamlines the integration of new models into the inference pipeline. We evaluated GreenServ across five benchmark tasks and a pool of 16 contemporary open-access LLMs. Experimental results show that GreenServ consistently outperforms static (single-model) and random baselines. In particular, compared to random routing, GreenServ achieved a 22% increase in accuracy while reducing cumulative energy consumption by 31%. Finally, we evaluated GreenServ with RouterBench, achieving an average accuracy of 71.7% with a peak accuracy of 75.7%. All artifacts are open-source and available as an anonymous repository for review purposes here: https://anonymous.4open.science/r/llm-inference-router-EBEA/README.md

The Model Context Protocol (MCP) has emerged as a de facto standard for integrating Large Language Models with external tools, yet no formal security analysis of the protocol specification exists. We present the first rigorous security analysis of MCP's architectural design, identifying three fundamental protocol-level vulnerabilities: (1) absence of capability attestation allowing servers to claim arbitrary permissions, (2) bidirectional sampling without origin authentication enabling server-side prompt injection, and (3) implicit trust propagation in multi-server configurations. We implement \textsc{MCPBench}, a novel framework bridging existing agent security benchmarks to MCP-compliant infrastructure, enabling direct measurement of protocol-specific attack surfaces. Through controlled experiments on 847 attack scenarios across five MCP server implementations, we demonstrate that MCP's architectural choices amplify attack success rates by 23--41\% compared to equivalent non-MCP integrations. We propose \textsc{MCPSec}, a backward-compatible protocol extension adding capability attestation and message authentication, reducing attack success rates from 52.8\% to 12.4\% with median latency overhead of 8.3ms per message. Our findings establish that MCP's security weaknesses are architectural rather than implementation-specific, requiring protocol-level remediation.

Enterprises increasingly adopt multi cloud architectures to take advantage of diverse database engines, regional availability, and cost models. In these environments, ETL pipelines must process large, distributed datasets while minimizing latency and transfer cost. Push down optimization, which executes transformation logic within database engines rather than within the ETL tool, has proven highly effective in single cloud systems. However, when applied across multiple clouds, it faces challenges related to data movement, heterogeneous SQL engines, orchestration complexity, and fragmented security controls. This paper examines the feasibility of push down optimization in multi cloud ETL pipelines and analyzes its benefits and limitations. It evaluates localized push down, hybrid models, and data federation techniques that reduce cross cloud traffic while improving performance. A case study across Redshift and BigQuery demonstrates measurable gains, including lower end to end runtime, reduced transfer volume, and improved cost efficiency. The study highlights practical strategies that organizations can adopt to improve ETL scalability and reliability in distributed cloud environments.

Modern DevOps practices have accelerated software delivery through automation, CI/CD pipelines, and observability tooling,but these approaches struggle to keep pace with the scale and dynamism of cloud-native systems. As telemetry volume grows and configuration drift increases, traditional, rule-driven automation often results in reactive operations, delayed remediation, and dependency on manual expertise. This paper introduces Cognitive Platform Engineering, a next-generation paradigm that integrates sensing, reasoning, and autonomous action directly into the platform lifecycle. This paper propose a four-plane reference architecture that unifies data collection, intelligent inference, policy-driven orchestration, and human experience layers within a continuous feedback loop. A prototype implementation built with Kubernetes, Terraform, Open Policy Agent, and ML-based anomaly detection demonstrates improvements in mean time to resolution, resource efficiency, and compliance. The results show that embedding intelligence into platform operations enables resilient, self-adjusting, and intent-aligned cloud environments. The paper concludes with research opportunities in reinforcement learning, explainable governance, and sustainable self-managing cloud ecosystems.

Despite the tremendous success of neural networks, benign images can be corrupted by adversarial perturbations to deceive these models. Intriguingly, images differ in their attackability. Specifically, given an attack configuration, some images are easily corrupted, whereas others are more resistant. Evaluating image attackability has important applications in active learning, adversarial training, and attack enhancement. This prompts a growing interest in developing attackability measures. However, existing methods are scarce and suffer from two major limitations: (1) They rely on a model proxy to provide prior knowledge (e.g., gradients or minimal perturbation) to extract model-dependent image features. Unfortunately, in practice, many task-specific models are not readily accessible. (2) Extracted features characterizing image attackability lack visual interpretability, obscuring their direct relationship with the images. To address these, we propose a novel Object Texture Intensity (OTI), a model-free and visually interpretable measure of image attackability, which measures image attackability as the texture intensity of the image's semantic object. Theoretically, we describe the principles of OTI from the perspectives of decision boundaries as well as the mid- and high-frequency characteristics of adversarial perturbations. Comprehensive experiments demonstrate that OTI is effective and computationally efficient. In addition, our OTI provides the adversarial machine learning community with a visual understanding of attackability.

The AI-native vision of 6G requires Radio Access Networks to train, deploy, and continuously refine thousands of machine learning (ML) models that drive real-time radio network optimization. Although the Open RAN (O-RAN) architecture provides open interfaces and an intelligent control plane, it leaves the life-cycle management of these models unspecified. Consequently, operators still rely on ad-hoc, manual update practices that can neither scale across the heterogeneous, multi-layer stack of Cell-Site, Edge-, Regional-, and Central-Cloud domains, nor across the three O-RAN control loops (real-, near-real-, and non-real-time). We present a self-learning framework that provides an efficient closed-loop version management for an AI-native O-RAN edge. In this framework, training pipelines in the Central/Regional Cloud continuously generate new models, which are cataloged along with their resource footprints, security scores, and accuracy metrics in a shared version repository. An Update Manager consults this repository and applies a self-learning policy to decide when and where each new model version should be promoted into operation. A container orchestrator then realizes these decisions across heterogeneous worker nodes, enabling multiple services (rApps, xApps, and dApps) to obtain improved inference with minimal disruption. Simulation results show that an efficient RL-driven decision-making can guarantee quality of service, bounded latencies while balancing model accuracy, system stability, and resilience.

Adapter-based Federated Large Language Models (FedLLMs) are widely adopted to reduce the computational, storage, and communication overhead of full-parameter fine-tuning for web-scale applications while preserving user privacy. By freezing the backbone and training only compact low-rank adapters, these methods appear to limit gradient leakage and thwart existing Gradient Inversion Attacks (GIAs). Contrary to this assumption, we show that low-rank adapters create new, exploitable leakage channels. We propose the Unordered-word-bag-based Text Reconstruction (UTR) attack, a novel GIA tailored to the unique structure of adapter-based FedLLMs. UTR overcomes three core challenges: low-dimensional gradients, frozen backbones, and combinatorially large reconstruction spaces by: (i) inferring token presence from attention patterns in frozen layers, (ii) performing sentence-level inversion within the low-rank subspace of adapter gradients, and (iii) enforcing semantic coherence through constrained greedy decoding guided by language priors. Extensive experiments across diverse models (GPT2-Large, BERT, Qwen2.5-7B) and datasets (CoLA, SST-2, Rotten Tomatoes) demonstrate that UTR achieves near-perfect reconstruction accuracy (ROUGE-1/2 > 99), even with large batch size settings where prior GIAs fail completely. Our results reveal a fundamental tension between parameter efficiency and privacy in FedLLMs, challenging the prevailing belief that lightweight adaptation inherently enhances security. Our code and data are available at https://github.com/shwksnshwowk-wq/GIA.

Retrieval-augmented generation (RAG) grounds large language models with external evidence, but under a limited context budget, the key challenge is deciding which retrieved passages should be injected. We show that retrieval relevance metrics (e.g., NDCG) correlate weakly with end-to-end QA quality and can even become negatively correlated under multi-passage injection, where redundancy and mild conflicts destabilize generation. We propose \textbf{Information Gain Pruning (IGP)}, a deployment-friendly reranking-and-pruning module that selects evidence using a generator-aligned utility signal and filters weak or harmful passages before truncation, without changing existing budget interfaces. Across five open-domain QA benchmarks and multiple retrievers and generators, IGP consistently improves the quality--cost trade-off. In a representative multi-evidence setting, IGP delivers about +12--20% relative improvement in average F1 while reducing final-stage input tokens by roughly 76--79% compared to retriever-only baselines.

The rapid rise of deepfake technology poses a severe threat to social and political stability by enabling hyper-realistic synthetic media capable of manipulating public perception. However, existing detection methods struggle with two core limitations: (1) modality fragmentation, which leads to poor generalization across diverse and adversarial deepfake modalities; and (2) shallow inter-modal reasoning, resulting in limited detection of fine-grained semantic inconsistencies. To address these, we propose ConLLM (Contrastive Learning with Large Language Models), a hybrid framework for robust multimodal deepfake detection. ConLLM employs a two-stage architecture: stage 1 uses Pre-Trained Models (PTMs) to extract modality-specific embeddings; stage 2 aligns these embeddings via contrastive learning to mitigate modality fragmentation, and refines them using LLM-based reasoning to address shallow inter-modal reasoning by capturing semantic inconsistencies. ConLLM demonstrates strong performance across audio, video, and audio-visual modalities. It reduces audio deepfake EER by up to 50%, improves video accuracy by up to 8%, and achieves approximately 9% accuracy gains in audio-visual tasks. Ablation studies confirm that PTM-based embeddings contribute 9%-10% consistent improvements across modalities.

As LLMs increasingly function as economic agents, the specific mechanisms LLMs use to update their belief with heterogeneous signals remain opaque. We design experiments and develop a Behavioral Kalman Filter framework to quantify how LLM-based agents update expectations, acting as households or firm CEOs, update expectations when presented with individual and aggregate signals. The results from experiments and model estimation reveal four consistent patterns: (1) agents' weighting of priors and signals deviates from unity; (2) both household and firm CEO agents place substantially larger weights on individual signals compared to aggregate signals; (3) we identify a significant and negative interaction between concurrent signals, implying that the presence of multiple information sources diminishes the marginal weight assigned to each individual signal; and (4) expectation formation patterns differ significantly between household and firm CEO agents. Finally, we demonstrate that LoRA fine-tuning mitigates, but does not fully eliminate, behavioral biases in LLM expectation formation.

Audio codecs power discrete music generative modelling, music streaming, and immersive media by shrinking PCM audio to bandwidth-friendly bitrates. Recent works have gravitated towards processing in the spectral domain; however, spectrogram domains typically struggle with phase modeling, which is naturally complex-valued. Most frequency-domain neural codecs either disregard phase information or encode it as two separate real-valued channels, limiting spatial fidelity. This entails the need to introduce adversarial discriminators at the expense of convergence speed and training stability to compensate for the inadequate representation power of the audio signal. In this work we introduce an end-to-end complex-valued RVQ-VAE audio codec that preserves magnitude-phase coupling across the entire analysis-quantization-synthesis pipeline and removes adversarial discriminators and diffusion post-filters. Without GANs or diffusion, we match or surpass much longer-trained baselines in-domain and reach SOTA out-of-domain performance on phase coherence and waveform fidelity. Compared to standard baselines that train for hundreds of thousands of steps, our model, which reduces the training budget by an order of magnitude, is markedly more compute-efficient while preserving high perceptual quality.

This paper presents an automated antenna design and optimization framework employing multi-objective genetic algorithms (MOGAs) to investigate various evolutionary optimization approaches, with a primary emphasis on multi-band frequency optimization. Five MOGA variants were implemented and compared: the Pareto genetic algorithm (PGA), non-dominated sorting genetic algorithm with niching (NSGA-I), non-dominated sorting genetic algorithm with elitism (NSGA-II), non-dominated sorting genetic algorithm using reference points (NSGA-III), and strength Pareto evolutionary algorithm (SPEA). These algorithms are employed to design and optimize microstrip patch antennas loaded with complementary split-ring resonators (CSRRs). A weighted-sum scalarization approach was adopted within a single-objective genetic algorithm framework enhanced with domain-specific constraint handling mechanisms. The optimization addresses the conflicting objectives of minimizing the return loss ($S_{11} < -10$~dB) and achieving multi-band resonance at 2.4~GHz, 3.6~GHz, and 5.2~GHz. The proposed method delivers a superior overall performance by aggregating these objectives into a unified fitness function encompassing $S_{11}$(2.4~GHz), $S_{11}$(3.6~GHz), and $S_{11}$(5.2~GHz). This approach effectively balances all three frequency bands simultaneously, rather than exploring trade-off solutions typical of traditional multi-objective approaches. The antenna was printed on a Rogers RT5880 substrate with a dielectric constant of 2.2 , loss tangent of 0.0009 , and thickness of 1.57~mm . Scalarization approach achieved return loss values of $-21.56$~dB, $-16.60$~dB, and $-27.69$~dB, with corresponding gains of 1.96~dBi, 2.6~dB, and 3.99~dBi at 2.4~GHz, 3.6~GHz, and 5.2~GHz, respectively.

Federated Learning (FL) that extracts data knowledge while protecting the privacy of multiple clients has achieved remarkable results in distributed privacy-preserving IoT systems, including smart traffic flow monitoring, smart grid load balancing, and so on. Since most data collected from edge devices are unlabeled, unsupervised Federated Clustering (FC) is becoming increasingly popular for exploring pattern knowledge from complex distributed data. However, due to the lack of label guidance, the common Non-Independent and Identically Distributed (Non-IID) issue of clients have greatly challenged FC by posing the following problems: How to fuse pattern knowledge (i.e., cluster distribution) from Non-IID clients; How are the cluster distributions among clients related; and How does this relationship connect with the global knowledge fusion? In this paper, a more tricky but overlooked phenomenon in Non-IID is revealed, which bottlenecks the clustering performance of the existing FC approaches. That is, different clients could fragment a cluster, and accordingly, a more generalized Non-IID concept, i.e., Non-ICD (Non-Independent Completely Distributed), is derived. To tackle the above FC challenges, a new framework named GOLD (Global Oriented Local Distribution Learning) is proposed. GOLD first finely explores the potential incomplete local cluster distributions of clients, then uploads the distribution summarization to the server for global fusion, and finally performs local cluster enhancement under the guidance of the global distribution. Extensive experiments, including significance tests, ablation studies, scalability evaluations, qualitative results, etc., have been conducted to show the superiority of GOLD.

This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future.

Learned Sparse Retrieval (LSR) methods construct sparse lexical representations of queries and documents that can be efficiently searched using inverted indexes. Existing LSR approaches have relied almost exclusively on uncased backbone models, whose vocabularies exclude case-sensitive distinctions, thereby reducing vocabulary mismatch. However, the most recent state-of-the-art language models are only available in cased versions. Despite this shift, the impact of backbone model casing on LSR has not been studied, potentially posing a risk to the viability of the method going forward. To fill this gap, we systematically evaluate paired cased and uncased versions of the same backbone models across multiple datasets to assess their suitability for LSR. Our findings show that LSR models with cased backbone models by default perform substantially worse than their uncased counterparts; however, this gap can be eliminated by pre-processing the text to lowercase. Moreover, our token-level analysis reveals that, under lowercasing, cased models almost entirely suppress cased vocabulary items and behave effectively as uncased models, explaining their restored performance. This result broadens the applicability of recent cased models to the LSR setting and facilitates the integration of stronger backbone architectures into sparse retrieval. The complete code and implementation for this project are available at: https://github.com/lionisakis/Uncased-vs-cased-models-in-LSR

In many deployed systems, new text inputs are handled by retrieving similar past cases, for example when routing and responding to citizen messages in digital governance platforms. When these systems fail, the problem is often not the language model itself, but that the nearest neighbors in the embedding space correspond to the wrong cases. Modern machine learning systems increasingly rely on fixed, high-dimensional embeddings produced by large pretrained models and sentence encoders. In real-world deployments, labels are scarce, domains shift over time, and retraining the base encoder is expensive or infeasible. As a result, downstream performance depends heavily on embedding geometry. Yet raw embeddings are often poorly aligned with the local neighborhood structure required by nearest-neighbor retrieval, similarity search, and lightweight classifiers that operate directly on embeddings. We propose PEARL (Prototype-Enhanced Aligned Representation Learning), a label-efficient approach that uses limited supervision to softly align embeddings toward class prototypes. The method reshapes local neighborhood geometry while preserving dimensionality and avoiding aggressive projection or collapse. Its aim is to bridge the gap between purely unsupervised post-processing, which offers limited and inconsistent gains, and fully supervised projections that require substantial labeled data. We evaluate PEARL under controlled label regimes ranging from extreme label scarcity to higher-label settings. In the label-scarce condition, PEARL substantially improves local neighborhood quality, yielding 25.7% gains over raw embeddings and more than 21.1% gains relative to strong unsupervised post-processing, precisely in the regime where similarity-based systems are most brittle.

Multimodal Small-to-Medium sized Language Models (MSLMs) have demonstrated strong capabilities in integrating visual and textual information but still face significant limitations in visual comprehension and mathematical reasoning, particularly in geometric problems with diverse levels of visual infusion. Current models struggle to accurately decompose intricate visual inputs and connect perception with structured reasoning, leading to suboptimal performance. To address these challenges, we propose SpatialMath, a novel Spatial Comprehension-Infused Symbolic Reasoning Framework designed to integrate spatial representations into structured symbolic reasoning chains. SpatialMath employs a specialized perception module to extract spatially-grounded representations from visual diagrams, capturing critical geometric structures and spatial relationships. These representations are then methodically infused into symbolic reasoning chains, facilitating visual comprehension-aware structured reasoning. To this end, we introduce MATHVERSE-PLUS, a novel dataset containing structured visual interpretations and step-by-step reasoning paths for vision-intensive mathematical problems. SpatialMath significantly outperforms strong multimodal baselines, achieving up to 10 percentage points improvement over supervised fine-tuning with data augmentation in vision-intensive settings. Robustness analysis reveals that enhanced spatial representations directly improve reasoning accuracy, reinforcing the need for structured perception-to-reasoning pipelines in MSLMs.

Training modern neural networks is increasingly fragile, with rare but severe destabilizing updates often causing irreversible divergence or silent performance degradation. Existing optimization methods primarily rely on preventive mechanisms embedded within the optimizer, offering limited ability to detect and recover from instability once it occurs. We introduce a supervisory runtime stability framework that treats optimization as a controlled stochastic process. By isolating an innovation signal derived from secondary measurements, such as validation probes, the framework enables automatic detection and recovery from destabilizing updates without modifying the underlying optimizer. We provide theoretical runtime safety guarantees that formalize bounded degradation and recovery. Our implementation incurs minimal overhead and is compatible with memory-constrained training settings.

The prevailing "parse-then-compress" paradigm in log compression fundamentally limits effectiveness by treating log parsing and compression as isolated objectives. While parsers prioritize semantic accuracy (i.e., event identification), they often obscure deep correlations between static templates and dynamic variables that are critical for storage efficiency. In this paper, we investigate this misalignment through a comprehensive empirical study and propose LogPrism, a framework that bridges the gap via unified redundancy encoding. Rather than relying on a rigid pre-parsing step, LogPrism dynamically integrates structural extraction with variable encoding by constructing a Unified Redundancy Tree (URT). This hierarchical approach effectively mines "structure+variable" co-occurrence patterns, capturing deep contextual redundancies while accelerating processing through pre-emptive pattern encoding. Extensive experiments on 16 benchmark datasets confirm that LogPrism establishes a new state-of-the-art. It achieves the highest compression ratio on 13 datasets, surpassing leading baselines by margins of 4.7% to 80.9%, while delivering superior throughput at 29.87 MB/s (1.68$\times$~43.04$\times$ faster than competitors). Moreover, when configured in single-archive mode to maximize global pattern discovery, LogPrism outperforms the best baseline by 19.39% in compression ratio while maintaining a 2.62$\times$ speed advantage.

Conversational AI systems require guardrails to prevent harmful outputs, yet existing approaches use static rules that cannot adapt to new threats or deployment contexts. We introduce Lattice, a framework for self-constructing and continuously improving guardrails. Lattice operates in two stages: construction builds initial guardrails from labeled examples through iterative simulation and optimization; continuous improvement autonomously adapts deployed guardrails through risk assessment, adversarial testing, and consolidation. Evaluated on the ProsocialDialog dataset, Lattice achieves 91% F1 on held-out data, outperforming keyword baselines by 43pp, LlamaGuard by 25pp, and NeMo by 4pp. The continuous improvement stage achieves 7pp F1 improvement on cross-domain data through closed-loop optimization. Our framework shows that effective guardrails can be self-constructed through iterative optimization.

Fine-tuning Large Language Models (LLMs) on sensitive datasets carries a substantial risk of unintended memorization and leakage of Personally Identifiable Information (PII), which can violate privacy regulations and compromise individual safety. In this work, we systematically investigate a critical and underexplored vulnerability: the exposure of PII that appears only in model inputs, not in training targets. Using both synthetic and real-world datasets, we design controlled extraction probes to quantify unintended PII memorization and study how factors such as language, PII frequency, task type, and model size influence memorization behavior. We further benchmark four privacy-preserving approaches including differential privacy, machine unlearning, regularization, and preference alignment, evaluating their trade-offs between privacy and task performance. Our results show that post-training methods generally provide more consistent privacy-utility trade-offs, while differential privacy achieves strong reduction in leakage in specific settings, although it can introduce training instability. These findings highlight the persistent challenge of memorization in fine-tuned LLMs and emphasize the need for robust, scalable privacy-preserving techniques.

This paper considers the development of an AI-based provably-correct mathematical proof tutor. While Large Language Models (LLMs) allow seamless communication in natural language, they are error prone. Theorem provers such as Lean allow for provable-correctness, but these are hard for students to learn. We present a proof-of-concept system (LeanTutor) by combining the complementary strengths of LLMs and theorem provers. LeanTutor is composed of three modules: (i) an autoformalizer/proof-checker, (ii) a next-step generator, and (iii) a natural language feedback generator. To evaluate the system, we introduce PeanoBench, a dataset of 371 Peano Arithmetic proofs in human-written natural language and formal language, derived from the Natural Numbers Game.

Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However, the presence of label noise in real scenarios poses a significant challenge in learning robust GNNs, and their effectiveness can be severely impacted when dealing with noisy labels on graphs, often stemming from annotation errors or inconsistencies. To address this, in this paper we propose a novel approach called ICGNN that harnesses the structure information of the graph to effectively alleviate the challenges posed by noisy labels. Specifically, we first design a novel noise indicator that measures the influence contradiction score (ICS) based on the graph diffusion matrix to quantify the credibility of nodes with clean labels, such that nodes with higher ICS values are more likely to be detected as having noisy labels. Then we leverage the Gaussian mixture model to precisely detect whether the label of a node is noisy or not. Additionally, we develop a soft strategy to combine the predictions from neighboring nodes on the graph to correct the detected noisy labels. At last, pseudo-labeling for abundant unlabeled nodes is incorporated to provide auxiliary supervision signals and guide the model optimization. Experiments on benchmark datasets show the superiority of our proposed approach.

Large reasoning models (LRMs) often generate long, seemingly coherent reasoning traces yet still produce incorrect answers, making hallucination detection challenging. Although trajectories contain useful signals, directly using trace text or vanilla hidden states for detection is brittle: traces vary in form and detectors can overfit to superficial patterns rather than answer validity. We introduce Answer-agreement Representation Shaping (ARS), which learns detection-friendly trace-conditioned representations by explicitly encoding answer stability. ARS generates counterfactual answers through small latent interventions, specifically, perturbing the trace-boundary embedding, and labels each perturbation by whether the resulting answer agrees with the original. It then learns representations that bring answer-agreeing states together and separate answer-disagreeing ones, exposing latent instability indicative of hallucination risk. The shaped embeddings are plug-and-play with existing embedding-based detectors and require no human annotations during training. Experiments demonstrate that ARS consistently improves detection and achieves substantial gains over strong baselines.

Quantum sensors offer significant advantages over classical devices in spatial resolution and sensitivity, enabling transformative applications across materials science, healthcare, and beyond. Their practical performance, however, is often constrained by unmodelled effects, including noise, imperfect state preparation, and non-ideal control fields. In this work, we report the first experimental implementation of a graybox modelling strategy for a solid-state open quantum system. The graybox framework integrates a physics-based system model with a data-driven description of experimental imperfections, achieving higher fidelity than purely analytical (whitebox) approaches while requiring fewer training resources than fully deep-learning models. We experimentally validate the method on the task of estimating a static magnetic field using a single-spin quantum sensor, performing Bayesian inference with a graybox model trained on prior experimental data. Using roughly 10,000 training datapoints, the graybox model yields several orders of magnitude improvement in mean squared error over the corresponding physics-only model. These results are broadly applicable to a wide range of quantum sensing platforms, not limited to single-spin systems, and are particularly valuable for real-time adaptive protocols, where model inaccuracies can otherwise lead to suboptimal control and degraded performance.

Centralized value learning is often assumed to improve coordination and stability in multi-agent reinforcement learning, yet this assumption is rarely tested under controlled conditions. We directly evaluate it in a fully tabular predator-prey gridworld by comparing independent and centralized Q-learning under explicit embodiment constraints on agent speed and stamina. Across multiple kinematic regimes and asymmetric agent roles, centralized learning fails to provide a consistent advantage and is frequently outperformed by fully independent learning, even under full observability and exact value estimation. Moreover, asymmetric centralized-independent configurations induce persistent coordination breakdowns rather than transient learning instability. By eliminating confounding effects from function approximation and representation learning, our tabular analysis isolates coordination structure as the primary driver of these effects. The results show that increased coordination can become a liability under embodiment constraints, and that the effectiveness of centralized learning is fundamentally regime and role dependent rather than universal.

Artificial Intelligence (AI) compilers are critical for efficiently deploying AI models across diverse hardware platforms. However, they remain prone to bugs that can compromise both compiler reliability and model correctness. Thus, ensuring the quality of AI compilers is crucial. In this work, we present a unified data-driven testing framework that systematically addresses stage-specific challenges in AI compilers. Specifically, OPERA migrates tests for AI libraries to test various operator conversion logic in the model loading stage. OATest synthesizes diverse optimization-aware computational graphs for testing high-level optimizations. HARMONY generates and mutates diverse low-level IR seeds to generate hardware-optimization-aware tests for testing low-level optimizations. Together, these techniques provide a comprehensive, stage-aware framework that enhances testing coverage and effectiveness, detecting 266 previously unknown bugs in four widely used AI compilers.

Graph neural networks (GNNs) have been widely used in various graph machine learning scenarios. Existing literature primarily assumes well-annotated training graphs, while the reliability of labels is not guaranteed in real-world scenarios. Recently, efforts have been made to address the problem of graph learning with label noise. However, existing methods often (i) struggle to distinguish between reliable and unreliable nodes, and (ii) overlook the relational information embedded in the graph topology. To tackle this problem, this paper proposes a novel method, Dual-Standard Semantic Homogeneity with Dynamic Optimization (DREAM), for reliable, relation-informed optimization on graphs with label noise. Specifically, we design a relation-informed dynamic optimization framework that iteratively reevaluates the reliability of each labeled node in the graph during the optimization process according to the relation of the target node and other nodes. To measure this relation comprehensively, we propose a dual-standard selection strategy that selects a set of anchor nodes based on both node proximity and graph topology. Subsequently, we compute the semantic homogeneity between the target node and the anchor nodes, which serves as guidance for optimization. We also provide a rigorous theoretical analysis to justify the design of DREAM. Extensive experiments are performed on six graph datasets across various domains under three types of graph label noise against competing baselines, and the results demonstrate the effectiveness of the proposed DREAM.

Large language models (LLMs) often rely on user-specific memories distilled from past interactions to enable personalized generation. A common practice is to concatenate these memories with the input prompt, but this approach quickly exhausts the limited context available in on-device LLMs. Compressing memories by averaging can mitigate context growth, yet it frequently harms performance due to semantic conflicts across heterogeneous memories. In this work, we introduce a clustering-based memory compression strategy that balances context efficiency and personalization quality. Our method groups memories by similarity and merges them within clusters prior to concatenation, thereby preserving coherence while reducing redundancy. Experiments demonstrate that our approach substantially lowers the number of memory tokens while outperforming baseline strategies such as naive averaging or direct concatenation. Furthermore, for a fixed context budget, clustering-driven merging yields more compact memory representations and consistently enhances generation quality.

This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recurrent Neural Networks (RNNs) family, i.e., Nonlinear Auto-Regressive with eXogenous inputs Echo State Networks (NARXESNs), the method allows to simultaneously select the optimal model class and learn model parameters from data through a new set-membership (SM) based procedure. The results show the effectiveness of the approach in identifying parsimonious yet accurate models suitable for control applications. Moreover, the proposed framework enables a robust training strategy that explicitly accounts for bounded measurement noise and enhances model robustness by allowing data-consistent evaluation of simulation performance during parameter learning, a process generally NP-hard for models with autoregressive components.

On-device large language models commonly employ task-specific adapters (e.g., LoRAs) to deliver strong performance on downstream tasks. While storing all available adapters is impractical due to memory constraints, mobile devices typically have sufficient capacity to store a limited number of these parameters. This raises a critical challenge: how to select representative adapters that generalize well across multiple tasks - a problem that remains unexplored in existing literature. We propose a novel method D2C for adapter clustering that leverages minimal task-specific examples (e.g., 10 per task) and employs an iterative optimization process to refine cluster assignments. The adapters within each cluster are merged, creating multi-task adapters deployable on resource-constrained devices. Experimental results demonstrate that our method effectively boosts performance for considered storage budgets.

Generative recommendation has recently emerged as a transformative paradigm that directly generates target items, surpassing traditional cascaded approaches. It typically involves two components: a tokenizer that learns item identifiers and a recommender trained on them. Existing methods often decouple tokenization from recommendation or rely on asynchronous alternating optimization, limiting full end-to-end alignment. To address this, we unify the tokenizer and recommender under the ultimate recommendation objective via differentiable soft item identifiers, enabling joint end-to-end training. However, this introduces three challenges: training-inference discrepancy due to soft-to-hard mismatch, item identifier collapse from codeword usage imbalance, and collaborative signal deficiency due to an overemphasis on fine-grained token-level semantics. To tackle these challenges, we propose UniGRec, a unified generative recommendation framework that addresses them from three perspectives. UniGRec employs Annealed Inference Alignment during tokenization to smoothly bridge soft training and hard inference, a Codeword Uniformity Regularization to prevent identifier collapse and encourage codebook diversity, and a Dual Collaborative Distillation mechanism that distills collaborative priors from a lightweight teacher model to jointly guide both the tokenizer and the recommender. Extensive experiments on real-world datasets demonstrate that UniGRec consistently outperforms state-of-the-art baseline methods. Our codes are available at https://github.com/Jialei-03/UniGRec.

Recent advances in Large Language Models (LLMs) have enabled the development of increasingly complex agentic and multi-agent systems capable of planning, tool use and task decomposition. However, empirical evidence shows that many of these systems suffer from fundamental reliability issues, including hallucinated actions, unexecutable plans and brittle coordination. Crucially, these failures do not stem from limitations of the underlying models themselves, but from the absence of explicit architectural structure linking goals, capabilities and execution. This paper presents a declarative, model-independent architectural layer for grounded agentic workflows that addresses this gap. The proposed layer, referred to as DALIA (Declarative Agentic Layer for Intelligent Agents), formalises executable capabilities, exposes tasks through a declarative discovery protocol, maintains a federated directory of agents and their execution resources, and constructs deterministic task graphs grounded exclusively in declared operations. By enforcing a clear separation between discovery, planning and execution, the architecture constrains agent behaviour to a verifiable operational space, reducing reliance on speculative reasoning and free-form coordination. We present the architecture and design principles of the proposed layer and illustrate its operation through a representative task-oriented scenario, demonstrating how declarative grounding enables reproducible and verifiable agentic workflows across heterogeneous environments.

The adoption of Large Language Models (LLMs) in scientific writing promises efficiency but risks introducing informational entropy. While "hallucinated papers" are a known artifact, the systematic degradation of valid citation chains remains unquantified. We conducted a forensic audit of 50 recent survey papers in Artificial Intelligence (N=5,514 citations) published between September 2024 and January 2026. We utilized a hybrid verification pipeline combining DOI resolution, Crossref metadata analysis, Semantic Scholar queries, and fuzzy text matching to distinguish between formatting errors ("Sloppiness") and verifiable non-existence ("Phantoms). We detect a persistent 17.0% Phantom Rate -- citations that cannot be resolved to any digital object despite aggressive forensic recovery. Diagnostic categorization reveals three distinct failure modes: pure hallucinations (5.1%), hallucinated identifiers with valid titles (16.4%), and parsing-induced matching failures (78.5%). Longitudinal analysis reveals a flat trend (+0.07 pp/month), suggesting that high-entropy citation practices have stabilized as an endemic feature of the field. The scientific citation graph in AI survey literature exhibits "link rot" at scale. This suggests a mechanism where AI tools act as "lazy research assistants," retrieving correct titles but hallucinating metadata, thereby severing the digital chain of custody required for reproducible science.

We study the problem of identifying an anomalous subset of streams under correlated noise, motivated by monitoring and security in cyber-physical systems. This problem can be viewed as a form of combinatorial pure exploration, where each stream plays the role of an arm and measurements must be allocated sequentially under uncertainty. Existing combinatorial bandit and hypothesis testing methods typically assume independent observations and fail to exploit correlation for efficient measurement design. We propose ECC-AHT, an adaptive algorithm that selects continuous, constrained measurements to maximize Chernoff information between competing hypotheses, enabling active noise cancellation through differential sensing. ECC-AHT achieves optimal sample complexity guarantees and significantly outperforms state-of-the-art baselines in both synthetic and real-world correlated environments. The code is available on https://github.com/VincentdeCristo/ECC-AHT

X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.

Human logic has gradually shifted from intuition-driven inference to rigorous formal systems. Motivated by recent advances in large language models (LLMs), we explore whether LLMs exhibit a similar evolution in the underlying logical framework. Using existential import as a probe, we for evaluate syllogism under traditional and modern logic. Through extensive experiments of testing SOTA LLMs on a new syllogism dataset, we have some interesting findings: (i) Model size scaling promotes the shift toward modern logic; (ii) Thinking serves as an efficient accelerator beyond parameter scaling; (iii) the Base model plays a crucial role in determining how easily and stably this shift can emerge. Beyond these core factors, we conduct additional experiments for in-depth analysis of properties of current LLMs on syllogistic reasoning.

The emergence of discourse-like tokens such as "wait" and "therefore" in large language models (LLMs) has offered a unique window into their reasoning processes. However, systematic analyses of how such signals vary across training strategies and model scales remain lacking. In this paper, we analyze token-level signals through token probabilities across various models. We find that specific tokens strongly correlate with reasoning correctness, varying with training strategies while remaining stable across model scales. A closer look at the "wait" token in relation to answer probability demonstrates that models fine-tuned on small-scale datasets acquire reasoning ability through such signals but exploit them only partially. This work provides a systematic lens to observe and understand the dynamics of LLM reasoning.

AI agents are increasingly used in software development, yet their interaction with CI/CD configurations is not well studied. We analyze 8,031 agentic pull requests (PRs) from 1,605 GitHub repositories where AI agents touch YAML configurations. CI/CD configuration files account for 3.25% of agent changes, varying by agent (Devin: 4.83%, Codex: 2.01%, p < 0.001). When agents modify CI/CD, 96.77% target GitHub Actions. Agentic PRs with CI/CD changes merge slightly less often than others (67.77% vs. 71.80%), except for Copilot, whose CI/CD changes merge 15.63 percentage points more often. Across 99,930 workflow runs, build success rates are comparable for CI/CD and non-CI/CD changes (75.59% vs. 74.87%), though three agents show significantly higher success when modifying CI/CD. These results show that AI agents rarely modify CI/CD and focus mostly on GitHub Actions, yet their configuration changes are as reliable as regular code. Copilot's strong CI/CD performance despite lower acceptance suggests emerging configuration specialization, with implications for agent training and DevOps automation.

Fast and accurate surrogates for physics-driven partial differential equations (PDEs) are essential in fields such as aerodynamics, porous media design, and flow control. However, many transformer-based models and existing neural operators remain parameter-heavy, resulting in costly training and sluggish deployment. We propose D-SENO (Dilated Squeeze-Excitation Neural Operator), a lightweight operator learning framework for efficiently solving a wide range of PDEs, including airfoil potential flow, Darcy flow in porous media, pipe Poiseuille flow, and incompressible Navier Stokes vortical fields. D-SENO combines dilated convolution (DC) blocks with squeeze-and-excitation (SE) modules to jointly capture wide receptive fields and dynamics alongside channel-wise attention, enabling both accurate and efficient PDE inference. Carefully chosen dilation rates allow the receptive field to focus on critical regions, effectively modeling long-range physical dependencies. Meanwhile, the SE modules adaptively recalibrate feature channels to emphasize dynamically relevant scales. Our model achieves training speed of up to approximately $20\times$ faster than standard transformer-based models and neural operators, while also surpassing (or matching) them in accuracy across multiple PDE benchmarks. Ablation studies show that removing the SE modules leads to a slight drop in performance.

AI coding agents are reshaping software development through both autonomous and human-mediated pull requests (PRs). When developers use AI agents to generate code under their own accounts, code authorship attribution becomes critical for repository governance, research validity, and understanding modern development practices. We present the first study on fingerprinting AI coding agents, analyzing 33,580 PRs from five major agents (OpenAI Codex, GitHub Copilot, Devin, Cursor, Claude Code) to identify behavioral signatures. With 41 features spanning commit messages, PR structure, and code characteristics, we achieve 97.2% F1-score in multi-class agent identification. We uncover distinct fingerprints: Codex shows unique multiline commit patterns (67.5% feature importance), and Claude Code exhibits distinctive code structure (27.2% importance of conditional statements). These signatures reveal that AI coding tools produce detectable behavioral patterns, suggesting potential for identifying AI contributions in software repositories.

Large Language Models (LLMs) have achieved rapid progress in Chinese language understanding, yet accurately evaluating their capabilities remains challenged by benchmark saturation and prohibitive computational costs. While static leaderboards provide snapshot rankings, they often mask the structural trade-offs between capabilities. In this work, we present ReLE (Robust Efficient Live Evaluation), a scalable system designed to diagnose Capability Anisotropy, the non-uniformity of model performance across domains. Using ReLE, we evaluate 304 models (189 commercial, 115 open-source) across a Domain $\times$ Capability orthogonal matrix comprising 207,843 samples. We introduce two methodological contributions to address current evaluation pitfalls: (1) A Symbolic-Grounded Hybrid Scoring Mechanism that eliminates embedding-based false positives in reasoning tasks; (2) A Dynamic Variance-Aware Scheduler based on Neyman allocation with noise correction, which reduces compute costs by 70\% compared to full-pass evaluations while maintaining a ranking correlation of $ρ=0.96$. Our analysis reveals that aggregate rankings are highly sensitive to weighting schemes: models exhibit a Rank Stability Amplitude (RSA) of 11.4 in ReLE versus $\sim$5.0 in traditional benchmarks, confirming that modern models are highly specialized rather than generally superior. We position ReLE not as a replacement for comprehensive static benchmarks, but as a high-frequency diagnostic monitor for the evolving model landscape.

Knowledge Editing (KE) has emerged as a promising paradigm for updating facts in Large Language Models (LLMs) without retraining. However, progress in Multilingual Knowledge Editing (MKE) is currently hindered by biased evaluation frameworks. We observe that existing MKE benchmarks are typically constructed by mechanically translating English-centric datasets into target languages (e.g., English-to-Chinese). This approach introduces translation artifacts and neglects culturally specific entities native to the target language, failing to reflect the true knowledge distribution of LLMs. To address this, we propose CLM-Bench, a culture-aware benchmark constructed using a native Chinese-first methodology. We curate 1,010 high-quality CounterFact pairs rooted in Chinese cultural contexts and align them with English counterparts. Using CLM-Bench, we conduct extensive experiments on representative LLMs (e.g., Llama-3, Qwen2) and reveal a significant Cross-lingual Misalignment: edits in one language function independently and fail to propagate to the other. We further provide a geometric explanation via layer-wise representation analysis, demonstrating that edit vectors for Chinese and English are nearly orthogonal -- residing in disjoint subspaces -- while mixed-lingual editing exhibits linear additivity of these vectors. Our findings challenge the effectiveness of current methods in cross-lingual transfer and underscore the importance of culturally native benchmarks.

Early-stage degradation in oscillatory systems often manifests as geometric distortions of the dynamics, such as phase jitter, frequency drift, or loss of coherence, long before changes in signal energy are detectable. In this regime, classical energy-based diagnostics and unconstrained learned representations are structurally insensitive, leading to delayed or unstable detection. We introduce GO-OSC, a geometry-aware representation learning framework for oscillatory time series that enforces a canonical and identifiable latent parameterization, enabling stable comparison and aggregation across short, unlabeled windows. Building on this representation, we define a family of invariant linear geometric probes that target degradation-relevant directions in latent space. We provide theoretical results showing that under early phase-only degradation, energy-based statistics have zero first-order detection power, whereas geometric probes achieve strictly positive sensitivity. Our analysis characterizes when and why linear probing fails under non-identifiable representations and shows how canonicalization restores statistical detectability. Experiments on synthetic benchmarks and real vibration datasets validate the theory, demonstrating earlier detection, improved data efficiency, and robustness to operating condition changes.

Modern enterprises increasingly adopt diverse technology stacks with various programming languages, posing significant challenges for static application security testing (SAST). Existing taint analysis tools are predominantly designed for single languages, requiring substantial engineering effort that scales with language diversity. While multi-language tools like CodeQL, Joern, and WALA attempt to address these challenges, they face limitations in intermediate representation design, analysis precision, and extensibility, which make them difficult to scale effectively for large-scale industrial applications at Ant Group. To bridge this gap, we present YASA (Yet Another Static Analyzer), a unified multi-language static taint analysis framework designed for industrial-scale deployment. Specifically, YASA introduces the Unified Abstract Syntax Tree (UAST) that provides a unified abstraction for compatibility across diverse programming languages. Building on the UAST, YASA performs point-to analysis and taint propagation, leveraging a unified semantic model to manage language-agnostic constructs, while incorporating language-specific semantic models to handle other unique language features. When compared to 6 single- and 2 multi-language static analyzers on an industry-standard benchmark, YASA consistently outperformed all baselines across Java, JavaScript, Python, and Go. In real-world deployment within Ant Group, YASA analyzed over 100 million lines of code across 7.3K internal applications. It identified 314 previously unknown taint paths, with 92 of them confirmed as 0-day vulnerabilities. All vulnerabilities were responsibly reported, with 76 already patched by internal development teams, demonstrating YASA's practical effectiveness for securing large-scale industrial software systems.

Watermarking methods have always been effective means of protecting intellectual property, yet they face significant challenges. Although existing deep learning-based watermarking systems can hide watermarks in images with minimal impact on image quality, they often lack robustness when encountering image corruptions during transmission, which undermines their practical application value. To this end, we propose a high-quality and robust watermark framework based on the diffusion model. Our method first converts the clean image into inversion noise through a null-text optimization process, and after optimizing the inversion noise in the latent space, it produces a high-quality watermarked image through an iterative denoising process of the diffusion model. The iterative denoising process serves as a powerful purification mechanism, ensuring both the visual quality of the watermarked image and enhancing the robustness of the watermark against various corruptions. To prevent the optimizing of inversion noise from distorting the original semantics of the image, we specifically introduced self-attention constraints and pseudo-mask strategies. Extensive experimental results demonstrate the superior performance of our method against various image corruptions. In particular, our method outperforms the stable signature method by an average of 10\% across 12 different image transformations on COCO datasets. Our codes are available at https://github.com/920927/ONRW.

Multilingual speech-text foundation models aim to process language uniformly across both modality and language, yet it remains unclear whether they internally represent the same language consistently when it is spoken versus written. We investigate this question in SeamlessM4T v2 through three complementary analyses that probe where language and modality information is encoded, how selective neurons causally influence decoding, and how concentrated this influence is across the network. We identify language- and modality-selective neurons using average-precision ranking, investigate their functional role via median-replacement interventions at inference time, and analyze activation-magnitude inequality across languages and modalities. Across experiments, we find evidence of incomplete modality invariance. Although encoder representations become increasingly language-agnostic, this compression makes it more difficult for the shared decoder to recover the language of origin when constructing modality-agnostic representations, particularly when adapting from speech to text. We further observe sharply localized modality-selective structure in cross-attention key and value projections. Finally, speech-conditioned decoding and non-dominant scripts exhibit higher activation concentration, indicating heavier reliance on a small subset of neurons, which may underlie increased brittleness across modalities and languages.

Large Vision-Language Models (LVLMs) are increasingly deployed in real-world intelligent systems for perception and reasoning in open physical environments. While LVLMs are known to be vulnerable to prompt injection attacks, existing methods either require access to input channels or depend on knowledge of user queries, assumptions that rarely hold in practical deployments. We propose the first Physical Prompt Injection Attack (PPIA), a black-box, query-agnostic attack that embeds malicious typographic instructions into physical objects perceivable by the LVLM. PPIA requires no access to the model, its inputs, or internal pipeline, and operates solely through visual observation. It combines offline selection of highly recognizable and semantically effective visual prompts with strategic environment-aware placement guided by spatiotemporal attention, ensuring that the injected prompts are both perceivable and influential on model behavior. We evaluate PPIA across 10 state-of-the-art LVLMs in both simulated and real-world settings on tasks including visual question answering, planning, and navigation, PPIA achieves attack success rates up to 98%, with strong robustness under varying physical conditions such as distance, viewpoint, and illumination. Our code is publicly available at https://github.com/2023cghacker/Physical-Prompt-Injection-Attack.

The emerging field of AI-generated art has witnessed the rise of prompt marketplaces, where creators can purchase, sell, or share prompts to generate unique artworks. These marketplaces often assert ownership over prompts, claiming them as intellectual property. This paper investigates whether concealed prompts sold on prompt marketplaces can be considered bona fide intellectual property, given that humans and AI tools may be able to infer the prompts based on publicly advertised sample images accompanying each prompt on sale. Specifically, our study aims to assess (i) how accurately humans can infer the original prompt solely by examining an AI-generated image, with the goal of generating images similar to the original image, and (ii) the possibility of improving upon individual human and AI prompt inferences by crafting combined human and AI prompts with the help of a large language model. Although previous research has explored AI-driven prompt inference and protection strategies, our work is the first to incorporate a human subject study and examine collaborative human-AI prompt inference in depth. Our findings indicate that while prompts inferred by humans and prompts inferred through a combined human and AI effort can generate images with a moderate level of similarity, they are not as successful as using the original prompt. Moreover, combining human- and AI-inferred prompts using our suggested merging techniques did not improve performance over purely human-inferred prompts.

Membership inference attacks (MIAs) pose a serious threat to the privacy of machine learning models by allowing adversaries to determine whether a specific data sample was included in the training set. Although federated learning (FL) is widely regarded as a privacy-aware training paradigm due to its decentralized nature, recent evidence shows that the final global model can still leak sensitive membership information through black-box access. In this paper, we introduce Res-MIA, a novel training-free and black-box membership inference attack that exploits the sensitivity of deep models to high-frequency input details. Res-MIA progressively degrades the input resolution using controlled downsampling and restoration operations, and analyzes the resulting confidence decay in the model's predictions. Our key insight is that training samples exhibit a significantly steeper confidence decline under resolution erosion compared to non-member samples, revealing a robust membership signal. Res-MIA requires no shadow models, no auxiliary data, and only a limited number of forward queries to the target model. We evaluate the proposed attack on a federated ResNet-18 trained on CIFAR-10, where it consistently outperforms existing training-free baselines and achieves an AUC of up to 0.88 with minimal computational overhead. These findings highlight frequency-sensitive overfitting as an important and previously underexplored source of privacy leakage in federated learning, and emphasize the need for privacy-aware model designs that reduce reliance on fine-grained, non-robust input features.

The scientific peer-review process is facing a shortage of human resources due to the rapid growth in the number of submitted papers. The use of language models to reduce the human cost of peer review has been actively explored as a potential solution to this challenge. A method has been proposed to evaluate the level of substantiation in scientific reviews in a manner that is interpretable by humans. This method extracts the core components of an argument, claims and evidence, and assesses the level of substantiation based on the proportion of claims supported by evidence. The level of substantiation refers to the extent to which claims are based on objective facts. However, when assessing the level of substantiation, simply detecting the presence or absence of supporting evidence for a claim is insufficient; it is also necessary to accurately assess the logical inference between a claim and its evidence. We propose a new evaluation metric for scientific review comments that assesses the logical inference between claims and evidence. Experimental results show that the proposed method achieves a higher correlation with human scores than conventional methods, indicating its potential to better support the efficiency of the peer-review process.

The advancement of Time Series Foundation Models (TSFMs) has been driven primarily by large-scale pre-training, but inference-time compute potential remains largely untapped. This work systematically investigates two questions: how do TSFMs behave under standard sampling-based inference scaling, and can controlled sampling diversity enhance performance? We first examine the properties of TSFMs under standard sampling often fail to adhere to scaling laws due to insufficient exploration of the solution space. Building on this, we then delve into diversified inference scaling via tailored time series perturbations to expand the generative distribution's support. We theoretically analyze the diversity-fidelity trade-off and derive a critical sample threshold for diversified sampling to outperform standard sampling. Extensive experiments across various TSFMs and datasets show proper diversified inference scaling yields substantial performance gains without parameter updates, establishing inference design as a critical, compute-efficient dimension of TSFM optimization. As an application, we propose RobustMSE, a rigorous metric to quantify the headroom performance of TSFM under a fixed budget. Overall, our findings clarify these factor interactions, enabling reliable performance via diverse large-scale inference time series in parallel environments without re-training TSFMs.

Data-driven methods for the solution of inverse problems have become widely popular in recent years thanks to the rise of machine learning techniques. A popular approach concerns the training of a generative model on additional data to learn a bespoke prior for the problem at hand. In this article we present an analysis for such problems by presenting quantitative error bounds for minimum Wasserstein-2 generative models for the prior. We show that under some assumptions, the error in the posterior due to the generative prior will inherit the same rate as the prior with respect to the Wasserstein-1 distance. We further present numerical experiments that verify that aspects of our error analysis manifests in some benchmarks followed by an elliptic PDE inverse problem where a generative prior is used to model a non-stationary field.

The quadratic complexity of standard attention mechanisms poses a significant scalability bottleneck for large language models (LLMs) in long-context scenarios. While hybrid attention strategies that combine sparse and full attention within a single model offer a viable solution, they typically employ static computation ratios (i.e., fixed proportions of sparse versus full attention) and fail to adapt to the varying sparsity sensitivities of downstream tasks during inference. To address this issue, we propose Elastic Attention, which allows the model to dynamically adjust its overall sparsity based on the input. This is achieved by integrating a lightweight Attention Router into the existing pretrained model, which dynamically assigns each attention head to different computation modes. Within only 12 hours of training on 8xA800 GPUs, our method enables models to achieve both strong performance and efficient inference. Experiments across three long-context benchmarks on widely-used LLMs demonstrate the superiority of our method.

This study uses Jordanian law as a case study to explore the fine-tuning of the Llama-3.1 large language model for Arabic question-answering. Two versions of the model - Llama-3.1-8B-bnb-4bit and Llama-3.1-8B-Instruct-bnb-4bit - were fine-tuned using parameter-efficient fine-tuning (PEFT) with LoRA adapters and 4-bit quantized models, leveraging the Unsloth framework for accelerated and resource-efficient training. A custom dataset of 6000 legal question-answer pairs was curated from Jordanian laws and formatted into structured prompts. Performance was evaluated using the BLEU and the ROUGE metrics to compare the fine-tuned models to their respective base versions. Results demonstrated improved legal reasoning and accuracy while achieving resource efficiency through quantization and optimized fine-tuning strategies. This work underscores the potential of adapting large language models for Arabic legal domains and highlights effective techniques for fine-tuning domain-specific tasks.

This study investigates whether readers prefer AI-generated short stories in Italian over one written by a renowned Italian author. In a blind setup, 20 participants read and evaluated three stories, two created with ChatGPT-4o and one by Alberto Moravia, without being informed of their origin. To explore potential influencing factors, reading habits and demographic data, comprising age, gender, education and first language, were also collected. The results showed that the AI-written texts received slightly higher average ratings and were more frequently preferred, although differences were modest. No statistically significant associations were found between text preference and demographic or reading-habit variables. These findings challenge assumptions about reader preference for human-authored fiction and raise questions about the necessity of synthetic-text editing in literary contexts.

Machine learning systems can produce personalized outputs that allow an adversary to infer sensitive input attributes at inference time. We introduce Robust Privacy (RP), an inference-time privacy notion inspired by certified robustness: if a model's prediction is provably invariant within a radius-$R$ neighborhood around an input $x$ (e.g., under the $\ell_2$ norm), then $x$ enjoys $R$-Robust Privacy, i.e., observing the prediction cannot distinguish $x$ from any input within distance $R$ of $x$. We further develop Attribute Privacy Enhancement (APE) to translate input-level invariance into an attribute-level privacy effect. In a controlled recommendation task where the decision depends primarily on a sensitive attribute, we show that RP expands the set of sensitive-attribute values compatible with a positive recommendation, expanding the inference interval accordingly. Finally, we empirically demonstrate that RP also mitigates model inversion attacks (MIAs) by masking fine-grained input-output dependence. Even at small noise levels ($σ=0.1$), RP reduces the attack success rate (ASR) from 73% to 4% with partial model performance degradation. RP can also partially mitigate MIAs (e.g., ASR drops to 44%) with no model performance degradation.

Large language models and deep neural networks achieve strong performance but suffer from reliability issues and high computational cost. This thesis proposes a unified framework based on spectral geometry and random matrix theory to address both problems by analyzing the eigenvalue structure of hidden activations. The first contribution, EigenTrack, is a real-time method for detecting hallucinations and out-of-distribution behavior in language and vision-language models using spectral features and their temporal dynamics. The second contribution, RMT-KD, is a principled compression method that identifies informative spectral components and applies iterative knowledge distillation to produce compact and efficient models while preserving accuracy. Together, these results show that spectral statistics provide interpretable and robust signals for monitoring uncertainty and guiding compression in large-scale neural networks.

Neural Radiance Fields (NeRF) have demonstrated remarkable performance in novel view synthesis. However, there is much improvement room on restoring 3D scenes based on NeRF from corrupted images, which are common in natural scene captures and can significantly impact the effectiveness of NeRF. This paper introduces NeRF-MIR, a novel neural rendering approach specifically proposed for the restoration of masked images, demonstrating the potential of NeRF in this domain. Recognizing that randomly emitting rays to pixels in NeRF may not effectively learn intricate image textures, we propose a \textbf{P}atch-based \textbf{E}ntropy for \textbf{R}ay \textbf{E}mitting (\textbf{PERE}) strategy to distribute emitted rays properly. This enables NeRF-MIR to fuse comprehensive information from images of different views. Additionally, we introduce a \textbf{P}rogressively \textbf{I}terative \textbf{RE}storation (\textbf{PIRE}) mechanism to restore the masked regions in a self-training process. Furthermore, we design a dynamically-weighted loss function that automatically recalibrates the loss weights for masked regions. As existing datasets do not support NeRF-based masked image restoration, we construct three masked datasets to simulate corrupted scenarios. Extensive experiments on real data and constructed datasets demonstrate the superiority of NeRF-MIR over its counterparts in masked image restoration.

Vision-language models (VLMs) are increasingly deployed in socially sensitive applications, yet their behavior with respect to disability remains underexplored. We study disability aware descriptions for person centric images, where models often transition from evidence grounded factual description to interpretation shift including introduction of unsupported inferences beyond observable visual evidence. To systematically analyze this phenomenon, we introduce a benchmark based on paired Neutral Prompts (NP) and Disability-Contextualised Prompts (DP) and evaluate 15 state-of-the-art open- and closed-source VLMs under a zero-shot setting across 9 disability categories. Our evaluation framework treats interpretive fidelity as core objective and combines standard text-based metrics capturing affective degradation through shifts in sentiment, social regard and response length with an LLM-as-judge protocol, validated by annotators with lived experience of disability. We find that introducing disability context consistently degrades interpretive fidelity, inducing interpretation shifts characterised by speculative inference, narrative elaboration, affective degradation and deficit oriented framing. These effects are further amplified along race and gender dimension. Finally, we demonstrate targeted prompting and preference fine-tuning effectively improves interpretive fidelity and reduces substantially interpretation shifts.

The integration of large language models (LLMs) into intelligent tutoring systems offers transformative potential for personalized learning in higher education. However, most existing learning path planning approaches lack transparency, adaptability, and learner-centered explainability. To address these challenges, this study proposes a novel Multi-Agent Learning Path Planning (MALPP) framework that leverages a role- and rule-based collaboration mechanism among intelligent agents, each powered by LLMs. The framework includes three task-specific agents: a learner analytics agent, a path planning agent, and a reflection agent. These agents collaborate via structured prompts and predefined rules to analyze learning profiles, generate tailored learning paths, and iteratively refine them with interpretable feedback. Grounded in Cognitive Load Theory and Zone of Proximal Development, the system ensures that recommended paths are cognitively aligned and pedagogically meaningful. Experiments conducted on the MOOCCubeX dataset using seven LLMs show that MALPP significantly outperforms baseline models in path quality, knowledge sequence consistency, and cognitive load alignment. Ablation studies further validate the effectiveness of the collaborative mechanism and theoretical constraints. This research contributes to the development of trustworthy, explainable AI in education and demonstrates a scalable approach to learner-centered adaptive instruction powered by LLMs.

Large language model (LLM) agents with extended autonomy unlock new capabilities, but also introduce heightened challenges for LLM safety. In particular, an LLM agent may pursue objectives that deviate from human values and ethical norms, a risk known as value misalignment. Existing evaluations primarily focus on responses to explicit harmful input or robustness against system failure, while value misalignment in realistic, fully benign, and agentic settings remains largely underexplored. To fill this gap, we first formalize the Loss-of-Control risk and identify the previously underexamined Intrinsic Value Misalignment (Intrinsic VM). We then introduce IMPRESS (Intrinsic Value Misalignment Probes in REalistic Scenario Set), a scenario-driven framework for systematically assessing this risk. Following our framework, we construct benchmarks composed of realistic, fully benign, and contextualized scenarios, using a multi-stage LLM generation pipeline with rigorous quality control. We evaluate Intrinsic VM on 21 state-of-the-art LLM agents and find that it is a common and broadly observed safety risk across models. Moreover, the misalignment rates vary by motives, risk types, model scales, and architectures. While decoding strategies and hyperparameters exhibit only marginal influence, contextualization and framing mechanisms significantly shape misalignment behaviors. Finally, we conduct human verification to validate our automated judgments and assess existing mitigation strategies, such as safety prompting and guardrails, which show instability or limited effectiveness. We further demonstrate key use cases of IMPRESS across the AI Ecosystem. Our code and benchmark will be publicly released upon acceptance.

Model editing has recently emerged as a popular paradigm for efficiently updating knowledge in LLMs. A central desideratum of updating knowledge is to balance editing efficacy, i.e., the successful injection of target knowledge, and specificity (also known as edit locality), i.e., the preservation of existing non-target knowledge. However, we find that existing specificity evaluation protocols are inadequate for this purpose. We systematically elaborated on the three fundamental issues it faces. Beyond the conceptual issues, we further empirically demonstrate that existing specificity metrics are weakly correlated with the strength of specificity regularizers. We also find that current metrics lack sufficient sensitivity, rendering them ineffective at distinguishing the specificity performance of different methods. Finally, we propose a constructive evaluation protocol. Under this protocol, the conflict between open-ended LLMs and the assumption of determined answers is eliminated, query-independent fluency biases are avoided, and the evaluation strictness can be smoothly adjusted within a near-continuous space. Experiments across various LLMs, datasets, and editing methods show that metrics derived from the proposed protocol are more sensitive to changes in the strength of specificity regularizers and exhibit strong correlation with them, enabling more fine-grained discrimination of different methods' knowledge preservation capabilities.

Motivated by the unique dispersions close to the two dimensional band crossing in a topologically charged nodal surface semimetal (NSSM) spectrum, we perform theoretical analysis of quantum tunnelling through a junction consisting of such NSSM, an insulator and a s-wave superconductor (acronymed NSSM-I-SC junction). In particular, for excitation energies both more and less than the superconducting gap potential $Δ$ we probe the normal and Andreev conductance for different incident orientations and thereby find the tunnelling electrical conductance through the heterostructure. The present work considers only the thin barrier limit which witness the conductance G to oscillate periodically with frequency $π$ as a function of the barrier strength, both in high and low doping limit. Such periodic behavior is also observed while calculating the thermal conductance $κ$ through the junction. Novelty of this problem is that the behavior of these G or $κ$ with insulator width are, in many respect, different compared to that from a normal metal - insulator - superconductor (NIS) junction on graphene or silicene. The findings can thus motivate experimentalists to culture renewed control over electric or thermal transport on topological materials.

We theoretically investigate dispersion of plasma waves propagating in a lateral plasmonic crystal based on a two-dimensional electron system with grating gates. Two specific configurations are analyzed: a system with single grating gate having ungated gaps and a double-grating-gate system. We calculate the dispersion relations for the fundamental and several higher-order plasma modes, classifying them as either ${\it bright}$ or ${\it dark}$ excitations. At the boundaries of the Brillouin zones, the dispersion of both types of excitations is shown to be quadratic, justifying introduction of effective bright and dark plasmon masses. In the low-frequency limit, the plasmonic crystal spectrum exhibits an acoustic plasma mode characterized by a certain velocity. We demonstrate that the effective plasmon mass and acoustic velocity are highly sensitive to both the crystal geometry (specifically the lattice filling factor) and the gate voltages, enabling wide-range tunability.

Magnetic hopfions are three-dimensional topological solitons -- knotted, vortex-like spin configurations. In chiral magnets, hopfions can appear as isolated structures or they can be linked to skyrmion strings. Previous studies employed a sophisticated protocol and a special sample geometry to nucleate such hopfions linked to one or a few skyrmion strings. Here, we introduce an electric-current-assisted nucleation protocol that is simple and independent of the sample shape and size. The resulting hopfions exhibit extraordinary stability in the presence of both positive and negative magnetic fields, in perfect agreement with micromagnetic simulations. We also present a comprehensive framework for classifying hopfions, skyrmions, and merons by deriving the corresponding homotopy group.

The nonlinear Nernst and Seebeck effects (NNE and NSE) offer promising routes for thermoelectric energy conversion in non-magnetic systems. While intrinsic mechanisms such as the nonlinear Drude and Berry-curvature-dipole terms are well established, extrinsic contributions to thermoelectric responses arising from disorder-induced asymmetric scattering remain comparatively less explored, despite growing experimental evidence of their dominance. Here, we develop a unified semiclassical theory of NNE and NSE that incorporates skew scattering and side-jump processes, identifying four distinct extrinsic contributions to NNE and two for NSE. A systematic symmetry analysis shows that these responses are allowed in time-reversal-symmetric non-magnets, PT-symmetric antiferromagnets, and non-centrosymmetric magnetic systems such as altermagnets. As a case study, we demonstrate that ABA-stacked trilayer graphene hosts large nonlinear Nernst and Seebeck responses dominated by extrinsic scattering, in excellent agreement with recent experiments. Our results establish the microscopic origin of these effects and provide guiding principles for designing high-efficiency nonlinear thermoelectric devices.

Are there hidden dynamical common patterns in the evolution of social and cultural history? While the growing availability of digitized social data invites us to answer this question, prevailing quantitative methods often rely on deterministic snapshots or average effects. Such approaches overlook the continuous and inherently uncertain nature of historical trajectories. In this paper, we propose a framework for modeling historical dynamics as stochastic processes described by stochastic differential equations (SDEs). By viewing historical change through the lens of continuous-time dynamics, this framework provides a natural language to describe how structural trends and inherent random fluctuations interact to shape societal evolution. This approach allows us to handle the uncertainty in fragmentary historical records, moving beyond the dichotomy of structural determinism versus pure chance. We demonstrate that adopting this stochastic perspective unlocks a rich suite of analytical capabilities unavailable to static models. Specifically, we introduce methods to: (1) quantify the irreversibility; (2) detect exogenous perturbations; (3) perform multiple imputation for missing historical records. This framework offers a unified methodology for dissecting the stability, contingency, and dynamics of historical change.

Understanding skyrmion-skyrmion interactions is crucial for effectively manipulating the motion of multiple skyrmions in racetrack and logic devices. However, the fundamental nature and microscopic origins of these interactions remain poorly understood. In this study, we investigate skyrmion-skyrmion interactions in chiral magnetic films and reveal that they possess intrinsic, anisotropic, and oscillatory characteristics. Specifically, we demonstrate that the attractive and repulsive forces between skyrmions oscillate with a well-defined period, akin to the Ruderman-Kittel-Kasuya-Yosida (RKKY) coupling observed between two magnetic moments in metals. Our analysis uncovers the essential physics behind a previously unrecognized universal wavy tail in the skyrmion spin texture. Notably, the resulting RKKY-like interaction between skyrmions is universal for all tilted skyrmions, irrespective of whether the titled easy-axis is from an external field or a crystalline magnetic anisotropy. These findings introduce a novel physical principle for the design of skyrmion molecules or skyrmion superstructures, which hold significant potential for applications in skyrmion-based spintronics and neuromorphic computing.

Coherent perfect absorption (CPA)-the time-reversed operation of lasing at threshold-relies on finely tuned interference and is intrinsically fragile to disorder and structural imperfections. Whether absorption can be endowed with topological protection, by analogy to topological lasing, has remained an open question. Here, we experimentally demonstrate a topological antilaser: the time-reversed counterpart of a topological laser, in which chiral edge modes of a photonic lattice enable perfect light absorption protected by topology. Using a nonreciprocal microwave network with low intrinsic loss, we show that the topological antilaser preserves near-unity absorption under strong disorder, and, unlike conventional antilasers, remains functional for arbitrary placements of dissipation and input ports, even when the lattice is strongly perturbed. This robustness arises from the disorder-immune propagation and stable spatial profile of the topological edge modes. Our results establish topologically protected absorption as the missing counterpart of topological lasing, opening new directions for studying robust energy dissipation, wave control, and coherent-absorption-based detection technologies.

We investigate an off-diagonal quasicrystal featuring simultaneous off-diagonal and diagonal quasiperiodic modulations. By analyzing the fractal dimension, we map out the delocalization-localization phase diagram. We demonstrate that delocalized and localized states can be distinguished via the Wigner distribution, while extended, critical, and localized phases are separated using the Wigner entropy. Furthermore, we explore the quantum thermodynamic properties, revealing that localized states facilitate the emergence of a quantum heater mode, alongside the appearance of a refrigerator mode. These findings enhance our understanding of localization phenomena and expand the thermodynamic applications of quasiperiodic systems.

We consider the minimal coupling of a Dirac semimetal Hamiltonian to a generic spin-texture in this work. A simple unitary transformation gauges away the spatial dependence in the exchange term, leading to the generation of effective corrections to the Dirac dispersion. A full function's worth of freedom is obtained as a result. Choosing different pitch vectors, we show that many forms of novel phenomena arise in such systems. For example, a linear pitch vector leads to the generation of type-I Weyl semimetal -- we observe the anomalous Hall effect and the chiral magnetic effect. The anomalous Hall coefficient requires a non-zero pitch vector whereas the CME is proportional to the exchange coupling. The band structure of the model in the presence of a magnetic field shows a Lifshitz transition. The introduction of a suitable time dependent pitch vector leads to the formation of nodal spheres in the Sambe space of effective Hamiltonians. This nodal sphere is robust to all orders in van-Vleck perturbation theory as proven explicitly.

We numerically study the three-dimensional turbulence in a minimal model of an active fluid--the Toner-Tu-Swift-Hohenburg equation. For small activity, we observe bacterial turbulence, while for large activity, we uncover hitherto unexplored regime of a turbulent flock where a global order coexists with turbulence. We present a simple closure model that predicts the turbulent flock and also qualitatively explains the transition to the bacterial turbulence regime via a transcritical bifurcation.

The drying of colloidal suspensions leads to complex deposition patterns, accompanied by instabilities such as cracking and delamination. In this study, we experimentally investigate the coupled influence of particle surface charge and substrate wettability on the evaporation dynamics, final deposition morphology, and crack patterns of sessile droplets containing silica nanoparticles. We examine the dynamics of two types of colloids, namely the negatively charged colloidal silica nanoparticles (Ludox TM50) and the positively charged silica nanoparticle (Ludox CL30), at concentrations ranging from 0.1 to 5.0 weight percentages, deposited on glass, polystyrene, and polytetrafluoroethylene (PTFE) substrates with distinct wettability. Side and top-view imaging techniques are employed to capture the evaporation process and analyze the resulting cracks. Our results reveal that the nature of the particle charge and substrate wettability significantly affect the evaporation mode, with transitions observed between constant contact radius (CCR), constant contact angle (CCA), and mixed modes. TM50-laden droplets consistently exhibit radial cracks, whereas CL30 droplets display more randomly oriented and irregular cracks. At higher particle concentrations, TM50 suspensions form thicker deposits that undergo delamination, particularly on highly wettable substrates like glass. Quantitative analysis reveals that crack spacing and length follow power-law relationships with particle concentration. Additionally, the delamination behavior is strongly influenced by both the particle concentration and the type of substrate. We propose a mechanistic framework to explain the role of particle-substrate interactions in governing the observed cracking and delamination behaviors.

Quantum error correction is essential for fault-tolerant quantum computation, yet most existing codes rely on local control and stabilizer measurements that are difficult to implement in systems dominated by collective interactions. Inspired by spin-GKP codes in PhysRevA.108.022428, we develop a general framework for Holstein-Primakoff spin codes, which maps continuous-variable bosonic codes onto permutation-symmetric spin ensembles via the Holstein-Primakoff approximation. We show that HP codes are robust to both collective and local-spin noise and propose an explicit measurement-free local error recovery procedure to map local noise into correctable collective-spin errors.

Molecular chirality interacting in a non-negligible manner with the spin angular momentum of subatomic particles, mainly electrons or photons, is the cause of a variety of spin-dependent filtering effects in quantum transport. Among them, spin-selective transport at room temperature is clearly one of the most promising properties in the quest for functional spintronic devices. In this context, two main effects have been experimentally investigated in the past 25 years and have attracted significant interest within the community: the so-called Electronic Magnetochiral Anisotropy (eMChA) and Chirality Induced Spin Selectivity (CISS). Despite extensive research, there is still a lack of consensus in the modeling of their microscopic mechanisms. As a consequence, it remains unclear whether the two are truly distinct or if they originate from a common physical cause. With the long-term goal of modelling the main different theories and to test them against the available experimental evidence, we programmed the core of an efficient kinetic Monte Carlo code. The current code models electron transport under an external voltage, distinguishes between $α$ and $β$ spin currents, and parametrizes molecules by their intrinsic electron mobility and the effective coupling between electron movement, spin and chirality. The code allows obtaining spin filtering values arising from the effective coupling between these three. We obtain an effect that vanishes at low voltages, with the asymmetry between positive and negative voltages typically found in electrical magnetochiral anisotropy experiments.

There is growing interest in characterizing magnetic order and dynamics in two-dimensional magnets, yet most efforts to date rely on external probes that interrogate the sample from tens of nanometers away and inevitably average over that length scale. Here we use internal, lattice-embedded Er3+ defects in CrSBr as atomic-scale probes, accessing their telecom-band photoluminescence with spectroscopy and temperature-dependent confocal imaging to read out magnetism from within the material. At room temperature we observe narrow, long-lived photoluminescence (PL) lines in the telecom band, characteristic of erbium emitters. Upon cooling to 3 K and reheating, the Er3+ PL intensity and excited-state lifetime display pronounced thermal hysteresis with a minimum near 132 K, at the reported antiferromagnetic (AFM) transition of CrSBr. Remarkably, we observe magnetic signatures persisting over a broader temperature range than expected from bulk benchmarks, suggesting nanoscale magnetic order that locally survives beyond the nominal phase boundary. Further, a moderate in-plane field of 0.3 T shifts the PL minimum by +8 K, which we tentatively associate to field-biased ferromagnetic correlations.

Phases with distinct thermodynamic properties must differ in their underlying distributions of microscopic structures. While ordered phases are readily distinguished by unit cells and space groups, the local structural basis differentiating amorphous phases is less apparent. Here, using a new probabilistic data-driven framework applied to molecular simulation data on water, we identify local collective variables that discriminate low-density and high-density amorphous (LDA and HDA) ices and characterize pressure-induced transitions between these phases. As expected, descriptors related to local density capably distinguish LDA and HDA; however, phase identity is surprisingly encoded within the first coordination shell. Furthermore, LDA transitions to HDA by a simple redistribution of LDA- and HDA-like environments with no evident intermediate structures, in accordance with a first-order-like transition that contrasts with the gradual evolution observed in other amorphous systems such as metallic glasses. These findings are robust across force fields, which themselves exhibit structural differences, and exemplify how other systems lacking obvious distinguishing features can be characterized.

Thin film deposition on weakly interacting substrates exhibits a unique growth mode characterized by initially strong island formation and rapidly increasing roughness, which reaches a maximum and subsequently decreases as the film returns to a smooth morphology. Here we show this rough-to-smooth growth mode experimentally for two molecular systems with substantially different geometries, namely, the effectively spherical buckminsterfullerene (C$_{60}$) and the disk-like 1,4,5,8,9,11-hexaazatriphenylenehexacarbonitrile (HATCN). This growth mode is explained by a geometrical model that captures the basic mechanisms of multilayer island growth, island coalescence, and formation of a continuous film. Additionally, kinetic Monte Carlo simulations with minimal ingredients demonstrate that this mode generally occurs for weakly interacting substrates, providing quantitative estimates of parameters that characterize adsorbate-adsorbate and adsorbate-substrate interactions. Both the model and simulations accurately describe the experimental data and highlight the generic nature of the phenomenon, independently of the details of the interactions and the molecular flux, which opens up a path for controlling nanoscale film roughness.

We theoretically demonstrate an orthogonal Einstein-de Haas effect, where the rotation of ferromagnetic materials is caused by the change of magnetization in the direction orthogonal to the rotation axis. This amounts to an anisotropic gyromagnetic ratio. To reveal its microscopic origin, we treat the spin-orbit coupling as a perturbation, integrate out the electronic degree of freedom, and show that in collinear ferromagnets the phonon angular momentum admits a dipolar structure in the spin-order space due to the constraint of the spin group symmetry. The spin-flipping and spin-conserving parts of the spin-orbit coupling contribute differently to such a dipolar structure. All these features are exemplified in a lattice electron-phonon model with ferromagnetic order and $C_{1h}$ point group symmetry. Our work lays the ground for revealing the connection between phonon angular momentum and general spin-order configurations.

Nanostructured transition metal oxides (TMO) are extensively explored materials for non-enzymatic glucose sensors. TMOs such as Iron oxides( α-Fe2O3, γ-Fe2O3, Fe3O4, etc.), NiO, CuO, Cr2O3, etc. have been utilized as electrocatalysts for glucose determination. Tremendous efforts have been put into identifying the impact of different morphologies of these materials on the glucose-sensing performance. The larger surface area of the flower and wire-shaped catalysts make them better performing amongst other morphologies. Interestingly, it is important to note that most of such studies are on standard Glassy Carbon electrodes. Further to enhance the Electrochemically active surface area (ECSA) of the electrode, Carbon nanomaterials such as reduced Graphene Oxide (r-GO) and Carbon Nanotubes (CNTs) are used as additives. Exfoliated Graphite paper electrodes offer better electrochemical characteristics than GCE electrodes due to their much larger ECSA. This study presents the non-enzymatic glucose sensing properties of NiO nanoflower-decorated Exfoliated Graphite electrodes. The amperometric detection of glucose shows a linear increase in current over a physiologically relevant wide range of 0-10 mM. The electrodes offer a better sensitivity of 304.12 microA per mM per cm square and a Limit Of Detection (LOD) of 100 microM. In addition, the electrodes showed high selectivity towards glucose in the presence of other interfering species such as Ascorbic acid, Fructose, Sucrose, and NaCl.

We use the Binary Intrinsic Dimension (BID), a geometrical measure designed for binary data, to analyze the Hopfield model, a paradigmatic spin system from statistical mechanics, machine learning and neuroscience. The BID allows us to characterize the phases and transitions of this system, and moreover it is robust against finite-size effects that interfere with the correct numerical estimation of the spin-glass order parameter ($q$). We observe that the BID scales linearly with system size in the retrieval and paramagnetic phases, where the correlations between spins are small, and exhibits sublinear scaling in the whole spin-glass phase, highlighting its correlated structure. Furthermore, we establish a direct relationship between the BID and the overlap distribution, unveiling a novel connection between the geometry of the state-space and standard spin order parameters.

We demonstrate a tunable Dirac-Weyl semimetal phase in two dimensions, realized by introducing in-plane d-wave altermagnetism into a Dirac system. This phase hosts both a central Dirac point and momentumseparated Weyl points connected by Fermi line edge states. The Weyl point positions--and thus the edge-state connectivity--can be continuously tuned by rotating the altermagnetic axis. In contrast, out-of-plane altermagnetism gaps part of the bulk spectrum while preserving a single Dirac point accompanied by chiral edge modes, as evidenced by quantized edge polarization. Our findings provide a tunable platform for manipulating Dirac-Weyl physics and topological edge transport in two dimensions.

Decoherence is often modeled using Markovian master equations that predict exponential suppression of coherence and are frequently used as effective bounds on quantum behavior in complex environments. Such descriptions, however, correspond to the singular physical limit of vanishing environmental memory. Here we formulate decoherence using a general time-nonlocal decoherence functional determined solely by the environmental force correlation function, with Markovian dynamics recovered explicitly as a limiting case. For arbitrary stationary environments with finite temporal correlations, we show that the decoherence functional exhibits quadratic short-time growth that is model-independent within the finite-memory class considered. Consequently, the decoherence time defined operationally-without assuming exponential decay-scales as the square root of the environmental correlation time, independent of the detailed form of the bath correlation kernel. These results are illustrated analytically for Gaussian-correlated, soft power-law, and Ornstein-Uhlenbeck environments. In the Ornstein-Uhlenbeck case, the non-Markovian dynamics admit an exact analytical closure, yielding a closed evolution equation for the coherence. Exact numerical simulations based on a pseudomode mapping confirm the predicted scaling and show that exponential decoherence emerges only in the memoryless limit. Beyond coherence decay, we distinguish decoherence rates from observable loss of quantum signatures by analyzing purity and von Neumann entropy dynamics. We show that suppression of a specific coherence element need not coincide with irreversible entropy production. Finally, we introduce an inferred-memory perspective in which the environmental correlation time is treated as an operationally extractable parameter from dynamical data.

Reaction-diffusion processes play an important role in a variety of physical, chemical, and biological systems. Conventionally, the kinetics of these processes are described by the law of mass action. However, there are various cases where these equations are insufficient. A fundamental challenge lies in accurately accounting for the microscopic correlations that inevitably arise in bimolecular reactions. While approaches to describe microscopic correlations in many specific cases exist, no general theory for multistage reactions has been established. In this article, we apply the quantum field theory approach to derive kinetic equations for general multistage reactive systems termed CMET (complete modified encounter theory). CMET can be formulated as a set of coupled partial differential equations that can be easily integrated numerically, thereby serving as a versatile tool for investigating reaction-diffusion processes. Across multiple case studies, we demonstrated that CMET reproduces the kinetics predicted by many other theories within their respective scopes of applicability.

Inference is a versatile tool that underlies scientific discovery, machine learning, and everyday decision-making: it describes how an agent updates a probability distribution as partial information is acquired from multiple measurements, reducing ignorance about a system's latent state. We define an inferential efficiency as the ratio of information gain to cumulative memory erasure cost, with inefficiency arising from unexploited correlations between the measured system and memories, and/or between memories and environment (noise). Using this efficiency, we benchmark two limiting measurement paradigms: sequential, in which the same memory is exploited iteratively, and parallel, in which many memories are exploited simultaneously. In both cases, the minimal erasure cost reflects correlations across memories: temporal in sequential, spatial in parallel. Remarkably, when all system-memory correlations are exploited for inference, both paradigms attain the same minimal erasure cost, even in the presence of noise. Conversely, the parallel paradigm performs better in the presence of unexploited correlations, stemming from hidden memories' degrees of freedom. This approach provides a quantitative, physically grounded criterion to compare inference strategies, determine their efficiency, and link target information gains to their minimal entropic cost.

Conventionally, symmetry-protected topological phases and band crossings are protected by global symmetries acting on the entire system. Here, we show that symmetries preserved only on a partial region of a system, termed local support symmetries, can protect topological features of the full system, even in the presence of symmetry-breaking couplings. We establish a unified framework by deriving explicit conditions for such protection in both insulating and metallic phases and show that destructive interference of Bloch wave functions plays a key role. Using representative tight-binding models, we demonstrate band crossings and topological bands protected by local support crystalline and time-reversal symmetries, and further present a realistic material realization in a fluorinated biphenylene network, where a band crossing is protected by a local support C$_2$ symmetry.

It's established that the nonlinear anomalous Nernst effect (NANE), originating from Berry curvature near the Fermi energy, is symmetry-permitted only when a single mirror symmetry exists in the transport plane of two-dimensional (2D) materials. Here, we show that an applied direct electric field can lift this symmetry constraint, enabling an electric-field-induced NANE emerge in time-reversal symmetric 2D systems with higher crystallographic symmetries. This electric-field-induced NANE arises from both Berry connection polarization, rooted in the electric-field-corrected Berry curvature, and the anomalous-velocity-modified nonequilibrium Fermi distribution function. Additionally, we propose an alternating temperature gradient as a driving force instead of the conventional steady one, ensuring experimental detection of NANE via second-harmonic measurement techniques. The behaviour of electric-field-induced NANE in the monolayer graphene has been theoretically and systematically investigated.

Understanding concentrated electrolytes requires a theory that spans local hydration and mesoscale interfacial assembly. We present an integrated workflow-SCOPE-that combines (i) enhanced sampling focused on a single Li+ ion, (ii) reweighting of biased trajectories to recover equilibrium microstate probabilities, and (iii) a chemical-potential correction that accounts for the limited reservoir of free water in finite simulation boxes. Applied to LiCl(aq) across 0.5-26 M and 283-313 K, this approach reveals a simple organizing principle: solvated ions dominate at low concentration; contact ion pairs emerge at intermediate strength; and aggregated Li-xCl clusters become most stable at the solubility limit. The resulting free-energy trends predict temperature-dependent solubility in close agreement with experiment and clarify the role of interfacial nucleation in precipitation. Beyond the simple LiCl(aq) salt considered here, SCOPE offers a transferable strategy for characterizing speciation and phase behavior in concentrated liquid systems where collective coordinates and rare events dominate.

In ultra-clean 2d materials electron viscosity is as important as Ohmic dissipation and electron transport exhibits hydrodynamic features. Using a simple framework of Brinkman equations we find that hydrodynamic electron flows exhibit a geometric skin effect: sharp obstacles locally enhance the current suppressing it far from the edges where the flow is unobstructed. This effect arises within hydrodynamic transport with finite momentum relaxation and does not rely on ballistic dynamics. Our results provide a natural hydrodynamic interpretation of edge-enhanced and double-bump current profiles observed in constricted geometries. By comparing with recent scanning NV magnetometry experiments on gated graphene, we demonstrate that such flow patterns are consistent with viscous hydrodynamics shaped by geometry, clarifying the role of geometric effects in the interpretation of electronic flow experiments.

Extended Self-Similarity (ESS) is a widely used tool for uncovering universal power-law scaling in systems dominated by nonlinear interactions. This work demonstrates that ESS scaling can also emerge in a system governed by purely linear physics: the propagation of coherent light in a multimode fiber. The system produces complex speckle patterns arising solely from deterministic linear mode interference. We analyze the intensity structure functions of these speckles and observe a robust extended scaling range. The measured scaling exponents align with the classical Kolmogorov scaling exponents. This finding establishes that the statistical signatures captured by ESS are not exclusive to nonlinear systems, revealing a broader applicability of this scaling framework to complex linear systems.

Starting from a second-order linear differential equation, we analyze the dynamical mechanisms of no behavior pattern (pure response), reaction and anticipation behaviors in traffic. As an emergence of the underlying dynamical evolution, the periodic evolution trajectories (3D hysteresis) in phase space ($v_i, v_j, d_{ji}$) exhibit fascinating characters. We investigate the emerging Time-Delay ($TD$) phenomena and the resulting analytical hysteresis, an equal frequency sets of Lissajous figures. By quantifying energy dissipation through individual and system perspectives, we demonstrate that $TD$ and Time-To-Collision ($TTC$) are direct metrics of zero-dissipation under equilibrium and synchronization states. Finally, a phase diagram based on $TD$ and $TTC$ is developed to bridge the dynamical behaviors in traffic across $\mathbb{R}^1$ and $\mathbb{R}^2$ spaces. Our results provide a theoretical foundation upon which many obscure mechanisms become self-evident, such as the $TD$-induced flip of the hysteresis (clockwise to counterclockwise in FD) and the crossed hysteresis, etc.

Optical chaotic synchronization between coupled nonlinear lasers underpins most chaos-based applications, including complex laser network dynamics, secure communication, key distribution, and reinforcement learning. In free-space links, however, chaotic synchronization is highly vulnerable to stochastic fluctuation induced by atmospheric turbulence, which results in temporal injection imbalances at symmetric receivers and triggers intermittent desynchronization. Here, we introduce a full Poincaré vector beam-enabled balanced-injection synchronization (BIS) mechanism, which passively mitigates coupling fluctuations and preserves injection symmetry through a complementary metasurface pair, without requiring any channel estimation or active control. Over a 3.2 km urban link under moderately strong turbulence, BIS suppresses coupling power fluctuations by a factor of 4.6 (from 0.4511 to 0.0975). It eliminates desynchronization events and increases the high-quality synchronization probability from 58.6% to 91.0%. This enables a record-high bit rate-distance product of 720 Gbps \cdot km, reducing communication interruption probability by up to 77% compared to Gaussian beam transmission. Our innovative strategy bridges the gap between nanophotonics and engineering optics, offering a new insight into advancing next-generation LiDAR, secure communication, and integrated sensing and communication systems in turbulent environments.

We introduce a finite scale geometric observable that quantifies the growth rate of localized sets under time evolution in dissipative dynamical systems. Defined at finite time and resolution without reference to symbolic dynamics or Markov partitions this observable converges, in uniformly hyperbolic systems, to a resolution dependent plateau whose logarithmic scaling coefficient equals the Kolmogorov Sinai entropy. In merely hyperbolic systems, it decays to zero, reflecting the absence of entropy production, while remaining well defined at finite scales. Numerical results for the Henon map and Feigenbaum point illustrate these behaviors. Our findings yield a finite scale geometric characterization of chaotic dynamics, consistent with classical entropy theory where applicable. We further demonstrate that the observable remains well defined in open intermittent systems, where trajectories escape and classical asymptotic invariants fail, revealing finite scale signatures of transient weak chaos.

Cellular automata (CA), originally developed as computational models of natural processes, have become a central subject in the study of complex systems and generative visual forms. Among them, the Ulam-Warburton Cellular Automaton (UWCA) exhibits recursive growth and fractal-like characteristics in its spatial evolution. However, exact self-similar fractal structures are typically observable only at specific generations and remain visually obscured in conventional binary renderings. This study introduces a Recursive Gradient Profile Function (RGPF) that assigns grayscale values to newly activated cells according to their generation index, enabling latent self-similar structures to emerge cumulatively in spatial visualizations. Through this gradient-based mapping, recursive geometric patterns become perceptible across scales, revealing fractal properties that are not apparent in standard representations. We further extend this approach to UWCA variants with alternative neighborhood configurations, demonstrating that these rules also produce distinct yet consistently fractal visual patterns when visualized using recursive gradient profile. Beyond computational analysis, the resulting generative forms resonate with optical and cultural phenomena such as infinity mirrors, video feedback, and mise en abyme in European art history, as well as fractal motifs found in religious architecture. These visual correspondences suggest a broader connection between complexity science, computational visualization, and cultural art and design.

The pedestrian flow is one of the most complex systems, involving large populations of interacting agents. Models at microscopic and macroscopic scales offer different advantages for studying related problems. In general, microscopic models can describe interaction forces at the individual level. Macroscopic models, on the other hand, provide analytical insights into global interactions and long-term overall dynamics, along with efficient numerical simulations and predictions. However, the relationship between models at different scales has rarely been explored. In this study, based on the original microscopic social force model with a reactive optimal route choice strategy, we first derive kinetic equations at the mesoscopic level. By varying the interaction force in different scenarios, we then derive several continuum models at the macroscopic level. Finally, numerical examples are given to evaluate the behaviors of the social force model and our continuum models.

We propose a formal framework for a noncommutative Kadomtsev--Petviashvili (KP) hierarchy which is covariant under the action of $SU(3)$ and compatible with a Lorentzian structure encoded in a twisted quaternionic (or Clifford) algebra. The starting point is a formal pseudodifferential operator $L$ built from an abstract derivation $D$ of Dirac type and coefficients in an associative algebra $\A$ that combines spin degrees of freedom (twisted quaternions, Clifford algebras) and color degrees of freedom (an internal $SU(3)$ factor, possibly realized via the octonions). In this way we obtain a hierarchy of formal partial differential equations which are Lorentz invariant and $SU(3)$ covariant and can be interpreted as integrable sectors of nonabelian gauge theories in $(3+1)$ dimensions and of their dimensional reductions.

Information can evolve as a physical consequence of non-equilibrium dynamics, even in the absence of genes, replication, or predefined fitness functions. We present Stability-Driven Assembly (SDA), a framework in which stochastic assembly combined with differential persistence biases populations toward longer-lived motifs. Assemblies that persist longer become more frequent and are therefore more likely to participate in subsequent interactions, generating feedback that reshapes the population distribution and implements fitness-proportional sampling, realizing evolution as a natural, emergent genetic algorithm (SDA/GA) driven solely by stability. We apply SDA/GA to chemical symbol space using SMILES fragments with recombination, mutation, and a heuristic stability function. Simulations show hallmark features of evolutionary search, including scaffold-level dominance, sustained novelty, and entropy reduction, yielding open-ended dynamics absent from equilibrium models with fixed transition rates. These results motivate an evolutionary ladder hypothesis where persistence-driven selection precedes genetic replication.

The mutual shadow area of pairs of surface regions is used for guiding the study of the spectral components and rank of their wave interaction, as captured by the corresponding moment matrix blocks. It is demonstrated that the mutual shadow area provides an asymptotically accurate predictor of the location of the singular value curve knee. This predicted knee index is shown to partition the interacting parts of the range and domain of blocks into two subspaces that can be associated with different wave phenomena: an "aperture" subspace of dimension that scales with the subdomains area (or length in 2-D) and a remainder "diffraction" subspace of dimension that scales much slower with the electrical length, depending on the geometric configuration. For interactions between open surface domains typical for the common hierarchical partitioning in most fast solvers, the latter can be attributed to the domain edges visible by its interacting counterpart. For interactions in 3-D with a small aspect angles between the source and observers, the diffraction subspace dimension is dominant in determining the rank until fairly large electrical lengths are reached. This explains the delayed asymptotic scaling of ranks and impressive fast solver performance observed in recent literature for seemingly arbitrary scatterers with no special geometric characteristics. In the extreme cases of "endfire" reduced dimensionality interactions, where the shadow area vanishes, the diffraction governs also the asymptotic rank, which translates to superior asymptotic solver performance.

Terahertz (THz) spectroscopy holds transformative potential for non-invasive sensing, yet characterizing individual nanoparticles in complex biological environments remains challenging due to the far-field diffraction limit. While near-field dipolar theory is well established, its application to characterizing/identifying nanoparticles immersed in complex media at THz frequencies is largely unexplored. This work utilizes numerical simulations of magneto-optical (MO) heterodimers -- comprising n-doped Indium Antimonide (n-InSb) and isotropic or birefringent particles (e.g., SiO2, GaSe) -- under counter-propagating, circularly polarized THz illumination. We demonstrate that while far-field observables like absorption cross-sections are often dominated by the MO-active particle, mechanical variables-specifically induced binding forces and spin/orbital torques-exhibit superior sensitivity for detecting "hidden" neighboring components. Because these mechanical signatures depend directly on near-field interactions, they provide higher information density regarding interparticle coupling. Key findings reveal material-specific spectral "hotspots" and "zeros" that serve as robust calibration markers even within dispersive biological surrogates. We show that the spin torque on non-MO particles is significantly modified by MO-neighbor proximity, a phenomenon controllable via static magnetic fields. Furthermore, these variables exhibit high angular sensitivity in perpendicular configurations. Our results provide a roadmap for using optomechanical signatures as high-resolution detectors for in-vivo diagnostics, signal transduction, and low-energy nanocircuit control.

We introduce a smooth B-spline discretization in polar coordinates on the unit disc that corrects the loss of regularity present at the origin caused by the coordinate singularity in standard tensor-product B-spline formulations. The method constructs "smooth polar splines" via a Galerkin projection of harmonic polar functions $S_l^{-m}(r,θ) := r^l \sin(mθ)$ and $S_l^{m}(r,θ) := r^l \cos(mθ)$, derived from the polar representation of Cartesian monomials, onto the central tensor-product B-spline basis in the innermost radial region. The radial component reproduces $r^l$ exactly for $0 \le l \leq p$, where $p$ is the B-spline degree, satisfying the near-origin regularity condition. However, exact compatibility with $C^\infty$-regularity at the origin is recovered only in the limit $Δθ\to 0$, when the angular component resolves all angular harmonics accurately. The smooth polar splines are linear combinations of standard tensor-product B-splines and lie in the same function space, enabling mapping between the $C^\infty$-regular subspace and the original discretization space via an exact prolongation operator and a corresponding restriction operator acting on the discrete variables. They match standard tensor-product B-splines away from the origin, preserve orthogonality among the newly constructed origin-centered basis functions, and maintain local support and sparse matrices. This smoothness and locality improve the conditioning of mass and stiffness matrices, conserve charge, and reduce statistical errors in particle-in-cell simulations near the origin, while eliminating spurious eigenvalues in eigenvalue problems. The approach provides a robust, high-order, and efficient adaptation of tensor-product B-splines for polar coordinates in physics simulations.

How cooperation originates and persists among self-interested individuals is a central question in the social and behavioural sciences. In the canonical two-dimensional spatial Prisoner's Dilemma with unconditional imitation introduced by Nowak and May (1992), simulations on a Moore lattice show an abrupt drop in cooperation near the temptation $T\approx5/3$, yet even under these harsh conditions cooperative structures can still arise. However, the nucleation rates of these motifs, and their contribution along the full cooperation curve had not been quantified. Here we show, using large-scale Monte Carlo simulations combined with automatic cluster classification, that on the Moore lattice for $T\ge5/3$ residual cooperation is sustained exclusively by $3\times3$ (or larger) rectangular cooperator bricks, whereas on degree-8 random-regular graphs for $T\gtrsim1.5$ it is dominated by star-like motifs (1 hub + 8 leaves). Once the dynamics becomes nucleation limited, the macroscopic cooperation level is therefore governed by the statistics of a few exceptionally resilient shapes, rather than by many different cooperator motifs. Furthermore, we show that the lattice cooperation collapse near $T=5/3$ is kinetic rather than critical: the reduction in cooperation is not due to a loss of growth capacity of rectangular bricks, but to the progressive destabilisation of the subcritical motifs that dominate just below this threshold. Our results show that residual cooperation at high temptation is a rare-event nucleation phenomenon governed by a small set of topological traps, and highlight the value of motif-level analysis for explaining and engineering cooperation in spatial, social, and technological networks.

We present cuGUGA, an operator-direct graphical unitary group approach (GUGA) configuration interaction (CI) solver in a spin-adapted configuration state function (CSF) basis. Dynamic-programming walk counts provide constant-time CSF ranking/unranking, and pretabulated segment factors enable constant-time evaluation of coupling coefficients. Two-electron contributions are organized through an intermediate-weight formulation that separates sparse generator enumeration from integral contraction and supports both dense and density-fitted/Cholesky backends. We further map the same primitives to GPUs by implementing the irregular DRT traversal and accumulation in custom CUDA kernels while delegating contractions to CUDA libraries. The implementation reproduces reference energies at the 10^{-11} Eh level and matches CPU/GPU sigma-vectors to 10^{-14}. On an RTX 4090, the GPU backend provides up to ~10x speedup over the CPU backend for smaller active spaces and multifold speedups on representative CASCI kernels. Speedup decreases as the active space grows because the workload becomes increasingly dominated by FP64 GEMM, which is not strongly accelerated on consumer GPUs. In addition, the cuGUGA CPU backend generally delivers >2x speedup over PySCF's determinant backend and >4x speedup over PySCF CSF backend.

Quantum-inspired algorithms can deliver substantial speedups over classical state-of-the-art methods by executing quantum algorithms with tensor networks on conventional hardware. Unlike circuit models restricted to unitary gates, tensor networks naturally accommodate non-unitary maps. This flexibility lets us design quantum-inspired methods that start from a quantum algorithmic structure, yet go beyond unitarity to achieve speedups. Here we introduce a tensor-network approach to compute the discrete Laplace transform, a non-unitary, aperiodic transform (in contrast to the Fourier transform). We encode a length-$N$ signal on two paired $n$-qubit registers and decompose the overall map into a non-unitary exponential Damping Transform followed by a Quantum Fourier Transform, both compressed in a single matrix-product operator. This decomposition admits strong MPO compression to low bond dimension resulting in significant acceleration. We demonstrate simulations up to $N=2^{30}$ input data points, with up to $2^{60}$ output data points, and quantify how bond dimension controls runtime and accuracy, including precise and efficient pole identification.

Ptychography spans from sub-angstrom to meter scales yet suffers from convergence instability and excessive data redundancy. Here we introduce self-correcting residual neural fields as a dose-efficient framework for electron, X-ray, and optical ptychography. Unlike approaches that split complex fields, our complex-valued architecture employs holomorphic phasor activation e^iωz to preserve intrinsic phase-amplitude coupling. We reformulate reconstruction as residual learning, where the network learns only corrections to physical priors rather than complete wavefields. By embedding the physical model as a differentiable layer within the network, we enable end-to-end automatic differentiation where experimental parameters are jointly corrected alongside the neural fields. We validate our scheme across conventional, near-field, coded, and Fourier ptychography and achieve record-breaking lensless resolution of 244-nm linewidth with visible light. Extending to electron wavelengths, we reveal synaptic connectivity in brain sections with superior performance over conventional approaches. Our framework provides a solution for high-throughput, dose-efficient nanoscopy across the electromagnetic spectrum.

We propose a new type of statistical interval obtained by weakening the definition of a p% credible interval: Having observed the interval (rather than the full dataset) we should put at least a p% belief in it. From a decision-theoretical point of view the resulting intervals occupy a middle ground between frequentist and fully Bayesian statistical intervals, both practically and philosophically: To a p% Bayesian credible interval we should assign (at least a) p% belief also after seeing the full dataset, while p% frequentist intervals we in general only assign a p% belief before seeing either the data or the interval. We derive concrete implementations for two cases: estimation of the fraction of a distribution that falls below a certain value (i.e., the CDF), and of the mean of a distribution with bounded support. Even though the problems are fully non parametric, these methods require only one-dimensional priors. They share many of the practical advantages of Bayesian methods while avoiding the complexity of assigning high-dimensional priors altogether. Asymptotically they give intervals equivalent to the fully Bayesian approach and somewhat wider intervals, respectively. We discuss promising directions where the proposed type of interval may provide significant advantages.

Sleep is thought to support memory consolidation and the recovery of optimal energetic regime by reorganizing synaptic connectivity, yet how plasticity across hierarchical brain circuits contributes to abstraction and energy efficiency remains unclear. Here we study a spiking multi-layer network alternating wake-like and deep-sleep-like states, with state-dependent dendritic integration and synaptic plasticity in a biologically inspired thalamo-cortical framework. During wakefulness, the model learns from few perceived examples, while during deep sleep it undergoes spontaneous replay driven by slow oscillations. Plasticity enabled not only within intra-layer connections, but also in inter-layer pathways, is critical for memory consolidation and energetic downshift. Compared to restricted plasticity, full inter-layer plasticity yields higher post-sleep visual classification accuracy and promotes the emergence of sharper class-specific associations. Furthermore, we introduce a biophysically grounded estimator of metabolic power expressing network energy consumption in ATP units, partitioned into baseline, synaptic maintenance, action potential, and transmission costs. We find that inter-layer plasticity in sleep leads to a larger reduction in firing rates, synaptic strength and synaptic activity, corresponding to a substantially larger decrease in power consumption. This work suggests promising elements to be integrated in neuromorphic/energy-efficient AI learning systems, supported by brain state-specific apical mechanisms.

Oscillatory rarefied gas flows are frequently encountered in MEMS, and their efficient numerical simulation remains a major challenge due to the time dependent nature of the problem and the high dimensionality of the Boltzmann kinetic equation. Here, we address this challenge by focusing on the periodic steady state and solving the resulting problem using the frequency domain general synthetic iterative scheme (GSIS). The key idea of GSIS is to simultaneously solve the mesoscopic kinetic equation and the macroscopic synthetic equation. The kinetic equation provides high-order constitutive relations, beyond those given by the Newton law of viscosity and the Fourier law of heat conduction, to the synthetic equation. In turn, the synthetic equation, which converges to the periodic steady state much faster than the kinetic equation, boosts the evolution of the kinetic equation toward the periodic steady state. As a result, super convergence is achieved, together with an asymptotic preserving property that allows the use of coarse spatial grids. The analytical Fourier stability analysis and the Chapman-Enskog expansion, together with challenging numerical simulations, are employed to demonstrate the fast convergence and asymptotic-preserving properties of GSIS, revealing that it can be three orders of magnitude faster than conventional kinetic schemes in near continuum flow regimes.

Atmospheric Methane Removal (AMR) is a third class of climate intervention, along with Carbon Dioxide Removal (CDR) and Solar Radiation Management (SRM). We show that, unlike CDR, the avoided warming by AMR is not durable due to methane's short atmospheric lifetime, although its temperature rebound upon termination is less abrupt than that of SRM. AMR's unique impact on air quality (tropospheric ozone) can be further modulated by background pollutant levels.

Qhronology is a novel scientific-computing package for studying quantum models of closed timelike curves (CTCs) and simulating general quantum information processing and computation. Written in Python, the program provides a comprehensive framework for analyzing quantum theories of antichronological time travel, including functionality to calculate quantum resolutions to temporal paradoxes. It also operates as a complete quantum circuit simulator, enabling the examination of quantum algorithms and protocols in both numerical and symbolic capacities. In this paper, we formally introduce Qhronology, beginning with discussion on aspects of its design philosophy and architecture. An overview of its basic usage is then presented, along with a collection of examples demonstrating its various capabilities within a variety of distinct contexts. Lastly, the performance of the package's circuit simulation component is characterized by way of some simple empirical benchmarking.

Biological soft tissues exhibit substantial inter-subject variability, making the automation of constitutive material modeling essential for patient-specific analysis and design. Such materials are not only highly nonlinear but also display intrinsic stochasticity arising from their complex and heterogeneous microstructure. Despite recent advances in data-driven constitutive modeling, most existing approaches remain deterministic and fail to quantify predictive uncertainty, thereby limiting their reliability in downstream mechanical analyses. In this work, we propose a probabilistic, data-driven constitutive modeling framework for anisotropic soft materials that explicitly accounts for uncertainty through conformalized quantile regression applied to tensor-valued fields. The proposed framework is built upon a strain-invariant, polyconvex formulation that ensures thermodynamic consistency and promotes robust predictive performance, including in extrapolative regimes. A key advantage of the proposed approach is its simplicity: it can be applied in a plug-and-play manner to endow existing deterministic models with probabilistic predictions, while remaining distribution-free and requiring no assumptions on the underlying data distribution. Moreover, the method is straightforward to train, scalable to models with a large number of parameters, and avoids Monte Carlo sampling at inference, making it computationally efficient and well suited for uncertainty propagation in large-scale mechanical simulations. The proposed method is validated using several benchmark datasets synthesized and collected from the literature.

Reaction-diffusion (RD) systems provide fundamental models for understanding self-organized spatiotemporal patterns across diverse natural and engineered settings, but reliable parameter estimation remains challenging, particularly when observations are sparse, noisy, and restricted to a subset of state variables. Based on physics-informed neural networks (PINNs), a physics-guided Curriculum Learning Identification via PINNs (CLIP) method is introduced in this work, for joint parameter inference and hidden state reconstruction. Leveraging the physical separability of RD systems, the CLIP training progresses from reaction-dominated regimes to full spatiotemporal dynamics using curriculum learning and an anchored widening transfer strategy. Across three canonical reaction-diffusion benchmarks, CLIP achieves more accurate and robust identification than baseline methods. Furthermore, the CLIP framework is successfully applied to infer the dynamics of Min system in bacteria, where only membrane bound species are observed and key kinetic rates span multiple orders of magnitude. Moreover, ablation experiments and loss landscape analyses provide mechanistic evidence that the curriculum stages and anchored transfer enhance trainability and convergence.

We consider the wake flow past a thin two-dimensional flat plate normal to the uniform stream and demonstrate that this flow is turbulent already at a relatively low Reynolds number of $Re = 400$. This is achieved by performing a careful comparison of the results of a DNS of this flow with experimental measurements of wake flows in the same geometric configuration at the Reynolds numbers of $Re=12500, 19700$. This comparison reveals that the distribution of several key quantities, including the mean velocity, Reynolds stresses and different effects contributing to the transport of the turbulent kinetic energy, are, up to measurement uncertainty, the same in these flows. Moreover, the wake flow at $Re = 400$ also features energy spectra characteristic of turbulent flows with intermittency detected in the distributions of the fluctuating strain and rotation rates. In contrast, these features are absent from the results of the DNS of the wake flow at $Re = 150$ where the distribution of the key quantities is also fundamentally different. These results show that the path to transition to turbulence in the wake past a thin flat plate is different from that in the wakes of canonical (i.e., circular or square) cylinders. We also identify possible physical mechanisms that may be responsible for these differences.

We propose a structural framework for the geometry of financial order books in which liquidity, supply, and demand are treated as emergent observables rather than primitive economic variables. The market is modeled as an inflationary relational system without assumed metric, temporal, or price coordinates. Observable quantities arise only through projection, implemented here via spectral embeddings of the graph Laplacian. A one-dimensional projection induces a price-like coordinate, while the projected density defines liquidity profiles around the mid price. Under a minimal single-scale hypothesis -- excluding intrinsic length scales beyond distance to the mid and finite visibility -- we show that projected supply and demand are constrained to gamma-like functional forms. In discrete data, this prediction translates into integrated-gamma cumulative profiles. We test these results using high-frequency Level~II data for several U.S. equities and find robust agreement across assets and intraday windows. Explicit comparison with alternative cumulative models using information criteria demonstrates a systematic preference for the integrated-gamma geometry. A minimal simulation of inflationary relational dynamics reproduces the same structure without invoking agent behavior or price formation mechanisms. These results indicate that key regularities of order-book liquidity reflect geometric constraints induced by observation rather than detailed microstructural dynamics. Supplementary Material is available at the arXiv submission.

Atom interferometers are used in a variety of applications, from measuring gravity and gravity gradients in the field to performing tests of fundamental physics in the lab. One method of increasing interferometer sensitivity is to produce a larger momentum difference between interferometer arms through the use of large momentum transfer methods, such as Bragg diffraction. However, Bragg diffraction introduces systematic effects in the accumulated interferometer phase that are challenging to characterize. A Bragg atom interferometer is described by the one-dimensional time-dependent Schrödinger equation (1D-TDSE). In this paper we show that for the case of Bragg diffraction the 1D-TDSE partial differential equation can be separated into several systems of ordinary differential equations, allowing for the use of adaptive step size Runge-Kutta methods. We compare the convergence of this method to the split-step and Crank-Nicolson methods, and present a method for further computational speed-ups using a lookup table.

Magnetic nanoparticles beyond the single-domain limit often develop vortex-like magnetization textures arising from the competition between exchange and magnetostatic energies. While such states are routinely studied using micromagnetic simulations, transparent analytical descriptions of vortex-mediated hysteresis and nucleation remain scarce. Here, we develop a semi-analytical minimal framework for vortex states in spherical magnetic nanoparticles. Guided by micromagnetic simulations, we introduce a parametrized vortex magnetization Ansatz based on hyperbolic functions that continuously interpolates between uniform and vortex states. In this way, we achieve a complexity reduction leading to a minimal Hamiltonian, which enables the efficient computation of magnetization curves and provides insight into vortex-mediated magnetization reversal. As an application, we derive analytical estimates for the critical vortex nucleation radius and field, recovering the functional form of Brown's classic result and extending it within a variational framework.

The discovery of high-performance organic photocatalysts for hydrogen evolution remains limited by the vastness of chemical space and the reliance on human intuition for molecular design. Here we present ChemNavigator, an agentic AI system that autonomously derives structure-property relationships through hypothesis-driven exploration of organic photocatalyst candidates. The system integrates large language model reasoning with density functional tight binding calculations in a multi-agent architecture that mirrors the scientific method: formulating hypotheses, designing experiments, executing calculations, and validating findings through rigorous statistical analysis. Through iterative discovery cycles encompassing 200 molecules, ChemNavigator autonomously identified six statistically significant design rules governing frontier orbital energies, including the effects of ether linkages, carbonyl groups, extended conjugation, cyano groups, halogen substituents, and amine groups. Importantly, these rules correspond to established principles of organic electronic structure (resonance donation, inductive withdrawal, $π$-delocalization), demonstrating that the system can independently derive chemical knowledge without explicit programming. Notably, autonomous agentic reasoning extracted these six validated rules from a molecular library where previous ML approaches identified only carbonyl effects. Furthermore, the quantified effect sizes provide a prioritized ranking for synthetic chemists, while feature interaction analysis revealed diminishing returns when combining strategies, challenging additive assumptions in molecular design. This work demonstrates that agentic AI systems can autonomously derive interpretable, chemically grounded design principles, establishing a framework for AI-assisted materials discovery that complements rather than replaces chemical intuition.

While large neural nets perform impressively on specific tasks, they are unreliable and unsafe, as is shown by the persistent hallucinations of large language models. This paper shows that large neural nets are intrinsically unreliable, because it is not possible to make or validate a tractable theory of how a neural net works. There is no reliable way to extrapolate its performance from a limited number of test cases to an unlimited set of use cases. To have confidence in the performance of a neural net, it is necessary to enclose it in a guardrail which is provably safe, so that whatever the neural net does, there cannot be harmful consequences. World models have been proposed as a way to do this. This paper discusses the scope and architecture required of world models. World models are often conceived as models of the physical and natural world, using established theories of natural science, or learned regularities, to predict the physical consequences of AI actions. However, unforeseen consequences of AI actions impact the human social world as much as the physical world. To predict and control the consequences of AI, a world model needs to include a model of the human social world. I explore the challenges that this entails. Human language is based on a Common Ground of mutual understanding of the world, shared by the people conversing. The common ground is an overlapping subset of each persons world model, including their models of the physical, social and mental worlds. LLMs have no stable representation of a common ground. To be reliable, AI systems will need to represent a common ground with their users, including physical, mental and social domains.

Infectious disease superspreading caused by heterogeneity in contact behavior has been observed to be an important determinant of epidemic dynamics and size in both empirical and theoretical settings. However, it has also been observed that the importance of this type of superspreading changes throughout an epidemic, generally in a decreasing manner as infections cascade from individuals with many contacts to those with fewer contacts. We provide an exact mathematical formulation of this phenomenon in strongly-immunizing (SIR) epidemics on static contact networks. Building on the edge-based modeling framework, we construct three metrics to track how superspreading changes through the course of an epidemic, respectively measuring infected nodes' contacts, exposures, and transmissions: (1) the mean degree of infected nodes, (2) the mean number of susceptible neighbors of infected nodes, and (3) the mean number of secondary cases that will be caused by newly infected nodes. We prove results about the behaviors of these metrics, highlighting the fact that their peak times all occur at less than half the time it takes for population-level infection prevalence to peak. This suggests that the importance of superspreading will be low when an epidemic is already near its peak, so contact-based control strategies are best employed as early in an outbreak as possible. We discuss implications for accurately measuring epidemiological parameters from incidence, mobility, contact tracing, and transmission data.

The incidence of advanced renal cell carcinoma(RCC) has been rising, presenting significant challenges due to the limited efficacy and severe side effects of traditional radiotherapy and chemotherapy. While combination immunotherapies show promise, optimizing treatment strategies remains difficult due to individual heterogeneity. To address this, we developed a Quantitative Cancer-Immunity Cycle (QCIC) model that integrates ordinary differential equations with stochastic modelling to quantitatively characterize and predict tumor evolution in patients with advanced RCC. By systematically integrating quantitative systems pharmacology principles with biological mechanistic knowledge, we constructed a virtual patient cohort and calibrated the model parameters using clinical immunohistochemistry data to ensure biological validity. To enhance predictive performance, we coupled the model with pharmacokinetic equations and defined the Tumor Response Index (TRI) as a quantitative metric of efficacy. Systematic analysis of the QCIC model allowed us to determine an optimal treatment regimen for the combination of bevacizumab and atezolizumab and identify tumor biomarkers with clinical predictive value. This study provides a theoretical framework and methodological support for precision medicine in the treatment of advanced RCC.

Wolbachia, a maternally transmitted endosymbiont, offers a powerful biological control strategy for mosquito-borne diseases such as dengue, Zika, and malaria. We develop an integro-difference equation (IDE) model that integrates Wolbachia's nonlinear growth with spatially explicit mosquito dispersal kernels to study invasion dynamics in heterogeneous landscapes. Analytical results establish the existence and uniqueness of monotone traveling waves and provide explicit estimates of invasion speeds as functions of dispersal and growth parameters. Four kernels: Gaussian, Laplace, exponential square-root, and Cauchy, represent a continuum from short- to long-range movement. Fat-tailed kernels generate faster, broader wavefronts, while compact ones limit spread. We also identify a critical bubble, the minimal localized profile required for sustained invasion. Numerical simulations in one- and two-dimensional domains confirm theoretical predictions and reveal parameter regimes governing invasion success. This framework quantifies how dispersal mechanisms shape Wolbachia's spread, thus informing targeted and efficient vector-control strategies.

We study a sampling problem for the abstract wavelet transform associated with the quasiregular representation of the $SE(2)$ group, for a modulated gaussian mother wavelet. This problem is motivated by the behavior of brain's primary visual cortex. We provide a characterization in terms of a dual Gramian matrix, and study numerically the relationships among the parameters defining the sampling and the mother wavelet.

Accurate brain tumor segmentation from multi-modal magnetic resonance imaging (MRI) is a prerequisite for precise radiotherapy planning and surgical navigation. While recent Transformer-based models such as Swin UNETR have achieved impressive benchmark performance, their clinical utility is often compromised by two critical issues: sensitivity to missing modalities (common in clinical practice) and a lack of confidence calibration. Merely chasing higher Dice scores on idealized data fails to meet the safety requirements of real-world medical deployment. In this work, we propose BMDS-Net, a unified framework that prioritizes clinical robustness and trustworthiness over simple metric maximization. Our contribution is three-fold. First, we construct a robust deterministic backbone by integrating a Zero-Init Multimodal Contextual Fusion (MMCF) module and a Residual-Gated Deep Decoder Supervision (DDS) mechanism, enabling stable feature learning and precise boundary delineation with significantly reduced Hausdorff Distance, even under modality corruption. Second, and most importantly, we introduce a memory-efficient Bayesian fine-tuning strategy that transforms the network into a probabilistic predictor, providing voxel-wise uncertainty maps to highlight potential errors for clinicians. Third, comprehensive experiments on the BraTS 2021 dataset demonstrate that BMDS-Net not only maintains competitive accuracy but, more importantly, exhibits superior stability in missing-modality scenarios where baseline models fail. The source code is publicly available at https://github.com/RyanZhou168/BMDS-Net.

We suggest a new approach to $β$-diversity in ecological systems, based on the energy of the graph sheaf Laplacian associated with the sample data. This scalar quantity is easily computable using methods of linear algebra. We show using simple examples that the energy is much more informative than the generally accepted definitions of $β$-diversity

Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches either fail to leverage unlabeled data from the target domain or rely on image-to-image translation, which can distort tissue structures and compromise model accuracy. In this work, we propose a semi-supervised domain adaptation (SSDA) framework that utilizes a latent diffusion model trained on unlabeled data from both the source and target domains to generate morphology-preserving and target-aware synthetic images. By conditioning the diffusion model on foundation model features, cohort identity, and tissue preparation method, we preserve tissue structure in the source domain while introducing target-domain appearance characteristics. The target-aware synthetic images, combined with real, labeled images from the source cohort, are subsequently used to train a downstream classifier, which is then tested on the target cohort. The effectiveness of the proposed SSDA framework is demonstrated on the task of lung adenocarcinoma prognostication. The proposed augmentation yielded substantially better performance on the held-out test set from the target cohort, without degrading source-cohort performance. The approach improved the weighted F1 score on the target-cohort held-out test set from 0.611 to 0.706 and the macro F1 score from 0.641 to 0.716. Our results demonstrate that target-aware diffusion-based synthetic data augmentation provides a promising and effective approach for improving domain generalization in computational pathology.

The accurate prediction of protein-ligand binding affinity is important for drug discovery yet remains challenging for multi-domain proteins, where inter-domain dynamics and flexible linkers govern molecular recognition. Current geometric deep learning methods typically treat proteins as monolithic graphs, failing to capture the distinct geometric and energetic signals at domain interfaces. To address this, we introduce DAGML (Domain-Aware Geometric Multimodal Learning), a hierarchical framework that explicitly models domain modularity. DAGML integrates a pre-trained protein language model with a novel domain-aware geometric encoder to distinguish intra- and inter-domain features, while a motif-centric ligand encoder captures pharmacophoric compatibility. We further curate a specialized multi-domain affinity benchmark, classifying complexes by binding topology (e.g., interface vs linker binders). Extensive experiments demonstrate that DAGML achieves a 21% reduction in MSE and a Pearson correlation of 0.726 compared to strong baselines. Ablation studies reveal that explicit modeling of domain interfaces is the primary driver of this improvement, particularly for ligands binding in the clefts between structural units. The code is available at https://github.com/jiankliu/DAGML.

Quantum Key Distribution (QKD) protocols rely on authenticated classical communication. Typical QKD security proofs are carried out in an idealized setting where authentication is assumed to behave honestly: it never aborts, and all classical messages are delivered faithfully with their original timing preserved. Authenticated channels that can be constructed in practice have different properties. Most critically, such channels may abort asymmetrically, such that only the receiving party may detect an authentication failure while the sending party remains unaware. Furthermore, an adversary may delay, reorder, or block classical messages. This discrepancy renders the standard QKD security definition and existing QKD security proofs invalid in the practical authentication setting. In this work we resolve this issue. Our main result is a reduction theorem showing that, under mild and easily satisfied protocol conditions, any QKD protocol proven secure under the honest authentication setting remains secure under a practical authentication setting. This result allows all existing QKD proofs to be retroactively lifted to the practical authentication setting with a minor protocol tweak.

The paper relates to a quantum radar deployment by the Born-Feynman path integrals approach based on quantum dots. The radar system comprises a quantum dot-based entangled photon generator, a transmission module, a delay line, a detection module, and a signal processing unit. The quantum dot-based entangled photon generator produces entangled photon pairs via spontaneous parametric down-conversion or stimulated emission. The signal transmission module, equipped with a microwave antenna and beamforming elements, directs the signal photon toward a target. The delay line module synchronizes the retained idler photon with the returning signal photon, preserving quantum coherence. The detection module collects the reflected signal photon and uses a cryogenically cooled superconducting nanowire single photon detector (SNSPD) for detection. Finally, the signal processing unit analyzes the quantum correlation between the scattered and idler photons to enable precise quantum state comparison.

We study the motion of charged particles with a magnetic dipole moment orbiting a Schwarzschild black hole immersed in an external paraboloidal magnetic field. The interaction between the particle's intrinsic magnetic moment and the black hole magnetosphere is modeled through a dipole coupling, and the equations of motion are derived using the Hamilton-Jacobi formalism. We analyze equatorial circular orbits, the innermost stable circular orbit, and epicyclic oscillations, showing that the magnetic field strength and coupling parameter produce competing effects on orbital stability and fundamental frequencies. These frequencies are applied to model high-frequency quasi-periodic oscillations within the relativistic precession framework. Using observational QPO data from stellar-mass, intermediate-mass, and supermassive black holes, we perform a Bayesian parameter estimation based on Markov Chain Monte Carlo techniques. The analysis constrains the black hole mass, magnetic field strength, field geometry, coupling parameter, and QPO orbital radius, highlighting the role of magnetospheric interactions in shaping both particle dynamics and timing properties of accreting black holes.

We propose an infrared mechanism for alleviating the Hubble constant tension, based on a small departure from entanglement equilibrium at the cosmological apparent horizon. If the horizon entanglement entropy falls slightly below the Bekenstein-Hawking value, we parametrize the shortfall by a fractional deficit $δ(a)$ evolving with the FLRW scale factor $a$. The associated equipartition deficit at the Gibbons-Hawking temperature then sources a smooth, homogeneous component whose density scales as $H^{2}/G$, with a dimensionless coefficient $c_{e}^{2}(a)$ of order unity times $δ(a)$. Because this component tracks $H^{2}$, it is negligible at early times but can activate at redshifts $z\lesssim 1$, raising the late time expansion rate by a few percent without affecting recombination or the sound horizon. We present a minimal three parameter activation model for $c_{e}^{2}(a)$ and derive its impact on the background expansion, effective equation of state, and linear growth for a smooth entanglement sector. The framework predicts a small boost in $H(z)$, a mild suppression of $fσ_{8}(z)$, and a corresponding modification of the low-$z$ distance-redshift relation. We test these predictions against current low-redshift data sets, including SN~Ia distance moduli, baryon acoustic oscillation distance measurements, cosmic chronometer $H(z)$ data, and redshift space distortion constraints, and discuss whether the $H_0$ tension can be consistently interpreted as a late-time, horizon-scale information deficit rather than an early universe modification.

In the standard circuit model, elementary gates are specified relative to a chosen tensor factorization and are therefore extrinsic to the ambient group $U(2^n)$. Writing $N=2^n$, we introduce an intrinsic descriptor layer in $U(N)$ by declaring as primitive the motions inside faithful embedded copies of $SU(2)$, leading to the phase-free dictionary $\mathcal{G}^{SU}_{\mathrm{elem}}(n)=\bigcup_{φ\in\Emb(SU(2),U(N))}φ(SU(2))$, and we also discuss the phase-inclusive $U(2)$ variant. We show that $\Emb(SU(2),U(N))$ decomposes into finitely many $U(N)$-homogeneous strata indexed by isotypic multiplicities, with stabilizers given by centralizers; the canonical two-level sector is organized by $\Gr_2(\C^N)$ up to a $PSU(2)$ gauge. Equipping $U(N)$ with the Hilbert--Schmidt bi-invariant metric, each embedded subgroup is totally geodesic. Using two-level QR/Givens factorization together with an explicit generation of diagonal tori by two-level phase rotations, we prove phase-free universality $\langle\mathcal{G}^{SU}_{\mathrm{2lvl}}(n)\rangle=SU(N)$ and hence $\langle\mathcal{G}^{SU}_{\mathrm{elem}}(n)\rangle=SU(N)$. Full universality in $U(N)$ follows by adjoining the abelian diagonal/global $U(1)$ factors (equivalently, by passing to the $U(2)$ two-level dictionary). Finally, we record a modular finite-alphabet interface by lifting Solovay--Kitaev approximation in $SU(2)$ through two-level embeddings.

Preparing quantum states whose amplitudes encode classical probability distributions is a fundamental primitive in quantum algorithms based on amplitude encoding and amplitude estimation. Given a probability distribution $\{p_k\}_{k=0}^{2^n-1}$, the Grover--Rudolph procedure constructs an $n$-qubit state $\ketψ=\sum_{k=0}^{2^n-1}\sqrt{p_k}\ket{k}$ by recursively applying families of controlled one-qubit rotations determined by a dyadic refinement of the target distribution. Despite its widespread use, the algorithm is often presented with informal correctness arguments and implicit conventions on the underlying dyadic tree. In this work we give a rigorous and self-contained analysis of the Grover--Rudolph construction: we formalize the dyadic probability tree, define the associated angle map via conditional masses, derive the resulting trigonometric factorizations, and prove by induction that the circuit prepares exactly the desired measurement law in the computational basis. As a complementary circuit-theoretic contribution, we show that each Grover--Rudolph stage is a uniformly controlled $\RY$ rotation on an active register and provide an explicit ancilla-free transpilation into the gate dictionary $\{\RY(\cdot),X,\CNOT(\cdot\to\cdot)\}$ using Gray-code ladders and a Walsh--Hadamard angle transform.

The entanglement entropy (EE) of any bipartition of a pure state can be approximately expressed as a sum of entanglement links (ELs). In this work, we introduce their exact extension, i.e. the entanglement hyperlinks (EHLs), a type of generalized mutual informations defined through the inclusion-exclusion principle, each of which captures contributions to the multipartite entanglement that are not reducible to lower-order terms. We show that any EHL crossing a factorized partition must vanish, and that the EHLs between any set of blocks can be expressed as a sum of all the EHLs that join all of them. This last result allows us to provide an exact representation of the EE of any block of a pure state, from the sum of the EHLs which cross its boundary. In order to illustrate their rich structure, we discuss some explicit numerical examples using ground states of local Hamiltonians. The EHLs thus provide a remarkable tool to characterize multipartite entanglement in quantum information theory and quantum many-body physics.

In the first part of the paper we study a perturbative model of the Rabi system of Quantum Optics. We therefore are able to describe, through Rellich's theory, an analytic expansion of finite families, but of any fixed length, of eigenvalues. In particular, we prove that for finite families of eigenvalues the Braak conjecture holds. In the second part we study the asymptotics of the Weyl spectral counting function of a class of systems that generalize the Quantum Rabi Model to an $N$-level atom ($N\geq3$) with $N-1$ cavity modes of the electromagnetic field.

Colour centres in silicon have great potential as single photon sources for quantum technologies. Some of them - like the T centre - also possess optically-active spins that enable spin-photon interfaces for generating entangled photons and multi-spin registers. This paper explores the generation of several types of colour centres in silicon for mass-manufacturable silicon-on-insulator quantum devices. We investigate how different processes in the device development affect the presence of the quantum emitters, including thermal annealing and fabrication steps for optical nanostructures. The study reveals coupled formation dynamics between different colour centres, identifies optimal parameters for annealing processes, and reports on the sensitivity to annealing duration and nanofabrication procedures for photonic integrated circuits. Furthermore, we discern stable optical signals from colour centres in silicon which have not been identified before.

We investigate quasinormal modes, late-time tails, and grey-body factors for massive scalar perturbations in the background of the Dymnikova regular black hole. By applying both the time-domain integration and the WKB method with Padé improvements, we show that the spectrum of massive fields differs qualitatively from the massless case. The oscillation frequency of the dominant mode grows with the field mass $μ$, while the damping rate decreases, suggesting the existence of quasi-resonances at sufficiently large $μ$. In the time domain, the late-time signal exhibits oscillatory tails with a power-law envelope, whose decay rate matches analytic expectations. Grey-body factors are also computed, showing strong suppression of radiation when mass is increased. Taken together, these results indicate that massive fields provide distinctive signatures of regular black holes and may serve as probes of near-horizon quantum corrections in the Dymnikova geometry.

Building on the relativistic Hamiltonian of Sonnleitner and Barnett arXiv:1806.00234 and its post-Newtonian extensions by Schwartz and Giuilini arXiv:1908.06929, we investigate composite atomic systems in dynamical gravitational backgrounds. Using a local inertial frame and a perturbed Minkowski metric, we derive curvature-dependent corrections to both center-of-mass and internal Hamiltonians for atoms interacting with weak gravitational waves. The resulting Hamiltonian contains distinct curvature couplings modifying the internal potential and affecting the center-of-mass dynamics. These contributions imply that internal-energy variations do not always reduce to mass renormalization and can induce genuine forces due to changes in momentum. The initial research was motivated by anomalous friction-like forces emerging in quantum optics, and clarified that the anomalous forces are mere relativistic corrections from mass-energy equivalence. Our results suggest that, with increasingly sensitive detectors, additional forces from gravitational wave interactions may become visible in future experiments.

We report on the first experimental realization of multi-material dielectric mirror coatings designed through a multi-objective optimization algorithm to simultaneously minimize thermal noise and optical losses. We validate this design strategy by fabricating and characterizing two distinct ternary systems: a SiNx -based proof-of-concept and a Ti:GeO2 -based system targeting lower optical losses. The performance of the SiN x coating shows remarkable agreement with predictions, demonstrating a noise amplitude spectral density reduction of 0.82 with respect to current reference coatings, and validating our design-to-fabrication pipeline. The Ti:GeO2 -based system achieves the crucial goal of sub-ppm absorption; its measured thermal noise, however, is higher than the theoretically predicted level of 0.71, extrapolated from single-layer material characterization. A dedicated tolerance analysis confirms that this discrepancy is not attributable to random thickness errors, emphasizing that further studies of the manufacturing process are needed to fully exploit this combination of materials. This work establishes a robust methodology for producing complex, high-performance optical coatings tailored for precision experiments.

We present a first-principles, non-perturbative worldline instanton analysis of vacuum decay in the non-abelian Yang-Mills root of a Schwarzschild background. We recover the gauge theory color-thermal spectrum as a topological winding mode. The double copy maps vacuum response from the gauge theory directly to the gravity theory. Furthermore, the decay exponent acquires a universal quadratic correction from color charge conservation, showing that the double copy correctly captures the non-linear backreaction as required for unitarity.

Quantum interferometers offer phase measurement capabilities that surpass the standard quantum limit (SQL), with phase sensitivity and quantum enhancement factor serving as key performance metrics. However, practical implementations face severe degradation of both metrics due to unavoidable losses, representing the foremost challenge in advancing quantum interferometry toward real-world applications. To address this challenge, we propose a coherent-amplifier-empowered quantum interferometer. The coherent amplifier dramatically suppresses the decay of both sensitivity and quantum enhancement under high-loss conditions, maintaining phase sensitivity beyond the original SQL even for losses exceeding 90%. Using an injected 4.2 dB squeezed-vacuum state in experimental demonstration, our scheme reduces the quantum enhancement degradation under 90% loss from 3.7 dB in a conventional quantum interferometer (CQI) to only 1.5 dB. More importantly, the phase sensitivity degradation under the same loss is limited to 4.0 dB, markedly outperforming the 11.2 dB degradation observed in a CQI. This improvement is enabled by the coherent amplifier's phase-sensitive photon amplification and its protection of the quantum state. This breakthrough in amplifier-empowered quantum interferometry overcomes the critical barrier to practical deployment, enabling robust quantum-enhanced measurements in lossy environments.

Recent cosmological observations, most notably from the DESI Data Release 2 (DR2) \cite{DESIDR2}, suggest a potential redshift evolution of the dark energy equation of state, reviving fundamental questions regarding the physical viability of the phantom regime ($w < -1$). In this Letter, a no-go result is established for a broad class of single-field effective scalar cosmologies where the kinetic response is modulated by the Hubble expansion rate, a structural feature characteristic of infrared-modified and nonlocal gravity theories \cite{DeserWoodard2007, Maggiore2014}. It is proved that imposing ghost freedom, defined by the positivity of the kinetic response function $\mathcal{M} \equiv \partialρ/\partial X > 0$, enforces the phantom divide ($w = -1$) as a \textit{stability-protected boundary} in the cosmological phase space. While the equation of state can approach this limit arbitrarily closely, continuous ghost-free evolution into the phantom regime is shown to be strictly forbidden. The dynamics are found to converge toward a de~Sitter-like attractor where $w \to -1$ emerges as a consequence of dynamical selection and stability rather than the fine-tuning of the scalar potential. These results suggest that the "hints" of evolution observed in DESI DR2 may reflect the underlying stability requirements of the effective field theory, providing a theoretical mechanism that prevents a physical transition into the phantom phase.

Quantum Machine Learning(QML) is developed by combining quantum mechanics principles with classical machine learning techniques in a hybrid framework that can give faster, exponential, more efficient power of quantum computing with the data driven intelligence. Quantum illumination(QI) is the quantum mechanical technique along with analysis of light matter interaction from source to detection end that connects quantum principle to hardware implementation. Superposition and entanglement control are deeply needed for the information-qubit processing in quantum computing. Improvement of measurement and performance are directly linked to detecting weak signal or intensity. This paper motivated that using quantum-enhanced technique how we can analysis previous superposition of qubit state which can clearly analyzed quantum interference diffraction patterns and its superposition using double slit experiment. Then constructed quantum neural network back propagation technique such that can give information of qubit position in any previous superposition state. Which is very import for any quantum optimization and search algorithm.

We establish, for the first time, an exact correspondence between Einstein-scalar-Maxwell theory and gauged Skyrme-Maxwell-Einstein models in (3+1) dimensions. By constructing the simplest consistent ansatz within the gauged Skyrme-Maxwell framework, we reveal a remarkable equivalence in a sector that admits nonvanishing, highly magnetized baryonic charge. This correspondence has a particularly appealing consequence: it transfers the full power of solution-generating techniques developed for electrovacuum systems-many of which naturally accommodate scalar fields to the considerably more intricate setting of gauged Skyrme-Maxwell theory minimally coupled to General Relativity. As a result, it opens the door to a systematic and much broader exploration of exact solutions in Skyrme-Maxwell-Einstein theory and of their potential applications in cosmology and astrophysics. Notably, the resulting configurations carry nonzero baryonic charge whenever the derivative of the hadronic profile along the magnetic field lines does not vanish. As an illustrative example, we apply this new dictionary to a rotating Kerr-Newman-like spacetime dressed with a scalar field. In the corresponding Skyrme-Maxwell-Einstein solution, the quantization of the baryonic charge enforces a quantization of the Kerr rotation parameter. We derive an upper bound on the baryonic charge in terms of the integration constants of the solution and show that, in the regime of small baryonic charge, the rotation parameter depends linearly on the baryonic charge.

Prompted by the longstanding interpretational controversy in quantum mechanics, quantum tunneling is heuristically addressed within the Everettian quantum multiverse. In this framework, the universal wavefunction splits into decohered reflected and transmitted branches under the environmetal effect after encountring a potential barrier. The observed tunneling is then experienced by the observer located in a tunneled world. The tunneling probability and the tunneling time are investigated in terms of the tunneled world relative weights and the branching duration, respectively. The macroscopic quantum tunneling, recently honored, is also discussed and the corresponding macroscopic tunneling time is approached based on the obtained results and known data.

We provide an operational interpretation of the multivariate Rényi divergence in terms of economic-theoretic tasks based on betting, risk aversion, and multiple lotteries. We show that the multivariate Rényi divergence $D_{\underlineα}(\vec{P}_X)$ of probability distributions $\vec{P}_X =(p^{(0)}_X,\dots,p^{(d)}_X)$ and real-valued orders $\underlineα = (α_0, \dots, α_d)$ quantifies the economic-theoretic value that a rational agent assigns to $d$ lotteries with odds $o^{(k)}_X \propto (p_X^{(k)})^{-1}$ ($k=1,\dots,d$) on a random event described by $p^{(0)}_X$. In particular, when the odds are fair and the rational agent maximises over all betting strategies, the economic-theoretic value (the isoelastic certainty equivalent) that the agent assigns to the lotteries is exactly given by $w^{\mathrm{ICE}}_{\underline{R}}=\exp[D_{\underlineα}(\vec{P}_X)]$, where $\underline{R}=(R_1,\dots,R_d)$ is a risk-aversion vector with $R_k = 1+α_k/α_0$ being the risk-aversion parameter for lottery $k$. Furthermore, we introduce a new conditional multivariate Rényi divergence that characterises a generalised scenario where the agent uses side information. We prove that this new quantity satisfies a data processing inequality which can be interpreted as the increment in the economic-theoretic value provided by side information; crucially, such a data processing inequality is a consequence of the agent's economic-theoretically consistent risk-averse attitude towards every lottery and vice versa. Finally, we apply these results to the resource theory of informative measurements in general probabilistic theories (GPTs). By establishing quantitative connections between information theory, physics, and economics, our framework provides a novel operational foundation for quantum state betting games with multiple lotteries in the realm of quantum resource theories.

We study the power spectral density of time delay fluctuations in an interferometer as a potential low-energy quantum gravitational observable. We derive a general expression for the spectrum in terms of the Wightman function of linear metric perturbations, which we then apply to a variety of cases. We analyze the intrinsic graviton fluctuations in the vacuum, thermal, and squeezed states, as well as the fluctuations induced by the vacuum stress-energy of a massless scalar field. We find that the resulting spectra are free of ultraviolet divergences and that, while thermal and squeezed states provide a natural amplification mechanism, the spectra remain suppressed by the Planck scale.

We investigate the existence of black bounce solutions in $2+1$ dimensions within the framework of $f(R)$ gravity. We analyze whether black bounce geometries originally obtained in general relativity can be consistently generalized to $f(R)$ theories and identify the matter sources capable of supporting such solutions. We also construct a new class of solutions by imposing a vanishing curvature scalar. In the matter sector, we consider models involving a coupling between a scalar field and nonlinear electrodynamics, while in the gravitational sector we analyze both the Starobinsky model and more general forms of $f(R)$. We further examine the viability conditions of the $f(R)$ models that give rise to these spacetimes, including the behavior of the scalaron mass. Finally, we study the associated energy conditions, in order to assess the degree of exoticity of the matter content required to sustain these black bounce solutions and how the $f(R)$ theory modifies the energy conditions.

Quantum cryptographic conferencing (QCC) allows multiple parties to establish common secure keys in quantum networks with information-theoretic security. However, the secure transmission distances of current QCC implementations are still limited to the metropolitan areas. Here, we experimentally demonstrate the three-intensity phase-matching (PM) QCC protocol considering finite-size effects by employing frequency-locking and phase-tracking techniques for three parties. The key distribution capability of the PM QCC protocol is demonstrated in the symmetric fiber channels with the distance from each party to the measurement site up to 100 km. The network adaptability of the PM QCC protocol is demonstrated in asymmetric fiber channels used to simulate fiber channel configurations in real networks. Thus, the feasibility of applying the PM QCC protocol to practical intercity quantum networks with both symmetric and asymmetric channels is verified.

We investigate the estimation of dephasing-induced decoherence in continuous-variable quantum systems using non-Gaussian probe states. By purifying the open system, we identify optimal probes, specifically squeezed cat and symmetric squeezed compass states, via quantum Fisher information. These results are in agreement with numerical simulation. In settings where the intra-cavity field is inaccessible and standard measurements are impractical, utilizing an ancilla approach where a qubit traverses or interacts with the cavity field, leading to measurement of the qubit, hence allowing estimation of the dephasing rate via Wigner function reconstruction or less costly marginal distribution.

The question of when the Kirkwood-Dirac quasiprobability serves as the most appropriate description for quantum measurements has remained unresolved, particularly across different measurement strengths. While known to generate anomalous weak values in the weak measurement regime and to reduce to classical probabilities under projective measurement, the physical mechanism governing its continuous transformation has been lacking. Here we demonstrate that the KD quasiprobability provides a general framework for all measurement regimes by identifying pointer-induced decoherence as the universal mechanism controlling this transition. We show that the decoherence factor F(t) simultaneously quantifies the loss of quantum coherence and interpolates the measurement strength from weak to strong. Within this framework, the KD quasiprobability naturally deforms from its full complex form-governing weak values-to the real, non-negative Wigner formula describing projective measurements, while maintaining informational completeness throughout the transition. Our work resolves the fundamental question of the KD distribution's applicability by establishing it as the universal framework that seamlessly connects all quantum measurement regimes through a physically transparent decoherence pathway.

The long-term security of public blockchains strictly depends on the hardness assumptions of the underlying digital signature schemes. In the current scenario, most deployed cryptocurrencies and blockchain platforms rely on elliptic-curve cryptography, which is vulnerable to quantum attacks due to Shor's algorithm. Therefore, it is important to understand how post-quantum (PQ) digital signatures behave when integrated into real blockchain systems. This report presents a blockchain prototype that supports multiple quantum-secure signature algorithms, focusing on CRYSTALS-Dilithium, Falcon and Hawk as lattice-based schemes. This report also describes the design of the prototype and discusses the performance metrics, which include key generation, signing, verification times, key sizes and signature sizes. This report covers the problem, background, and experimental methodology, also providing a detailed comparison of quantum-secure signatures in a blockchain context and extending the analysis to schemes such as HAETAE.

Quantum error correction is indispensable for scalable quantum computation. Although encoding logical qubits substantially enhances noise resilience, achieving logical error rates low enough for practical algorithms remains challenging on existing hardware. Here we introduce argument reweighting, a simple and broadly applicable post-selection decoding strategy that boosts the performance of maximum-likelihood-type decoders, including minimum-weight perfect matching and belief-propagation families. The method suppresses logical errors by performing additional decoding rounds under reweighted error models, enabling acceptance of high-confidence syndrome outcomes. Circuit-level simulations across multiple decoders and qLDPC codes show that argument reweighting substantially suppresses logical errors, requiring a rejection rate of only $1.44\times10^{-5}$ to reduce the logical error rate by almost two orders of magnitude for the $[[144,12,12]]$ bivariate bicycle code. These results establish argument reweighting as a practical and resource-efficient approach for enhancing quantum fault tolerance.

Simulating interactions between fermions and bosons is central to understanding correlated phenomena, yet these systems are inherently difficult to treat classically. Previous quantum algorithms for fermion-boson models exhibit computation costs that scale polynomially with the bosonic truncation parameter, $Λ$. In this work we identify the efficient unitary transformation enabling fast-forwarded evolution of the fermion-boson interaction term, yielding an interaction-picture based simulation algorithm with complexity polylogarithmic in $Λ$. We apply this transformation to explicitly construct an efficient quantum algorithm for the Hubbard-Holstein model and discuss its generalisation to other fermion-boson interacting models. This approach yields an important asymptotic improvement in the dependence on the bosonic cutoff and establishes that, for certain models, fermion-boson interactions can be simulated with resources comparable to those required for purely fermionic systems.

Grover's search algorithm provides a quadratic speedup over classical brute-force search in terms of query complexity and is widely used as a versatile subroutine in numerous quantum algorithms, including those for combinatorial problems with large search spaces. For such problems, it is natural to reduce the effective search space by incorporating problem constraints at the initialization step, which in Grover's algorithm can be achieved by preparing structured initial states that encode constraint information. In this work, we present a systematic framework with a simple preprocessing procedure for constraint-aware initialization in Grover's algorithm, focusing on problems with linear constraints. While such structured initial states can reduce the number of oracle queries required to obtain a solution, their preparation incurs additional circuit-level costs. We therefore offer a conservative circuit-level resource analysis, showing that the resulting constraint-aware initialization can improve resource efficiency in terms of gate counts and circuit depth. The validity of the framework is further demonstrated numerically using the exact-cover problem. Overall, our results indicate that this approach serves as a practical baseline for achieving more resource-efficient implementations of Grover's algorithm compared to the standard uniform initialization.

In this work, we study the phase of neutrino oscillation propagating along radial and non-radial paths in the Einstein$\text{-}$Hilbert$\text{-}$Bumblebee (EHB) gravity around global monopole field, by using the quantummechanical treatment, the expression of the corrected neutrino of probability of oscillation is obtained. Our results show that neutrino oscillation in the EHB gravity around global monopole field is different from that in Schwarzschild black hole, the Lorentz-violating parameter $a$ mainly affects the peak of the oscillation probability, and the global monopole $\overline { μ}$ and the lightest neutrino mass both affect the frequency of the oscillations of probabilities, which means that studying the neutrino oscillation phenomenon in curved spacetime may not only become a new technology for probing the properties of compact celestial bodies, but also help deepen our understanding of neutrinos.

Holography-inspired teleportation has recently emerged as a significant area of research in quantum many-body systems. In this work, we investigate the effects of $\mathcal{PT}$ symmetric non-unitary deformations on the traversability of the wormhole-inspired teleportation protocol modeled by coupled Sachdev-Ye-Kitaev systems prepared in a Thermofield Double state bath. By introducing balanced gain and loss terms to the boundary Hamiltonians, we identify a phase transition driven by spectral exceptional points, where the real energy eigenvalues of the effective Hamiltonian coalesce and bifurcate into complex conjugate pairs. We demonstrate that the $\mathcal{PT}$-broken phase acts as an amplifier, enabling exponential growth in the norm of the teleported signal while preserving the causal time window for the wormhole's traversability. A statistical study of disorder realizations reveals that the critical non-Hermiticity threshold $γ_c$ follows a log-normal distribution, reflecting the sensitivity of the transition to the microscopic level spacing of the chaotic SYK spectrum. Furthermore, we observe a ``Purification" effect deep in the broken phase, where the teleportation channel acts as an entanglement distiller, yielding near-perfect teleportation fidelity for post-selected states. Our results suggest that the non-Hermitian topology can be harnessed to enhance holographic quantum communication, providing a robust mechanism for signal amplification in noisy, minimal quantum many-body systems.

Establishing quantum speedup for computationally hard problems of practical relevance, particularly combinatorial optimization problems, remains a central challenge in quantum computation. In this work, we identify a structurally defined family of classically hard maximum independent set (MIS) instances, and design and analyze a non-stoquastic adiabatic quantum optimization algorithm that exploits this structure. The algorithm runs in polynomial time and achieves an exponential speedup over both transverse-field quantum annealing and state-of-the-art classical solvers on these instances, under assumptions supported by analytical and numerical evidence. We identify the essential quantum mechanism enabling the speedup as the use of a non-stoquastic XX-driver to access a larger sign-structured admissible subspace beyond the stoquastic regime, which allows sign-generating quantum interference to create smooth evolution paths that bypass tunneling. This identifies a distinctive quantum mechanism underlying the speedup and explains why no efficient classical analogue is likely to exist. In addition, our analysis produces scalable small-scale models, derived from our structural reduction, that capture the essential dynamics of the algorithm. These models provide a concrete opportunity for verification of the quantum advantage mechanism on currently available universal quantum computers.

Engineering quantum tunnelling in phase space has emerged as a viable method for creating a protected qubit with biased-noise properties. A promising approach is to combine a Kerr nonlinearity with multi-photon transitions, resulting in a system known as a Kerr parametric oscillator (KPO). In this work, we implement a three-photon KPO and explore its potential as a protected qutrit. We confirm quantum coherence by demonstrating three-photon Rabi oscillations and performing direct Wigner function measurements that reveal three-component cat-like states. We observe breathing-like dynamics in phase space, arising from exotic temporal interference between the qutrit and excited states. The frequency of this interference corresponds to the energy gap between the qutrit and excited manifolds, thereby providing an experimental hallmark of qutrit space protection. We also identify a higher-order pump term as the main mechanism suppressing photon occupation; mitigating this term is necessary to maximize protection. Our findings elucidate the basic quantum properties of the three-photon KPO and establish the first step toward its use as an alternative qutrit platform.

Marletto and Vedral [Phys. Rev. Lett. 125, 040401 (2020)] propose that the Aharonov-Bohm (AB) phase is locally mediated by entanglement between a charged particle and the quantized electromagnetic field, asserting gauge independence for non-closed paths. In this Comment, we critically analyze their model and demonstrate that the AB phase arises from the interaction with the vector potential \(\mathbf{A}\), not from entanglement, which is a byproduct of the quantum electrodynamics (QED) framework. We show that their field-based energy formulation, intended to reflect local electromagnetic interactions, is mathematically flawed due to an incorrect prefactor and yields \( +q \mathbf{v} \cdot \mathbf{A}_{\mathbf{s}} \) in the Coulomb gauge, conflicting with QED's \( -q \mathbf{v} \cdot \mathbf{A}_{\mathbf{s}} \). This equivalence to \( q \mathbf{v} \cdot \mathbf{A}_{\mathbf{s}} \) holds only approximately in the Coulomb gauge under static conditions, failing for time-dependent fields and other gauges, undermining their claim of a gauge-independent local mechanism. Furthermore, we confirm that the AB phase is gauge-dependent for non-closed paths, contradicting their assertion. Our analysis reaffirms the conventional explanation in the semi-classical picture, where the AB phase is driven by the vector potential \(\mathbf{A}\), with entanglement playing no causal role in its generation.

The Pusey-Barrett-Rudolph (PBR) theorem establishes $ψ$-onticity for individual quantum systems, but its standard formulation relies on the Preparation Independence Postulate (PIP). This has led to a prevalent view that rejecting PIP leaves open the possibility of $ψ$-epistemic models for individual systems. In this work, we show that this understanding is incomplete: once the PBR theorem establishes $ψ$-onticity for composite systems prepared in product states, the $ψ$-onticity of the individual subsystems follows directly from the tensor-product structure of quantum mechanics, without invoking PIP or any further auxiliary assumptions. This result removes a key auxiliary assumption from the PBR theorem, closes a persistent loophole for preserving $ψ$-epistemic models, and strengthens the conceptual foundations of $ψ$-ontology.

The Aharonov-Bohm (AB) effect highlights the fundamental role of electromagnetic potentials in quantum mechanics, manifesting as a phase shift for a charged particle in field-free regions. While well-established for static magnetic fluxes, the effect's behavior under time-varying fluxes remains an open and debated question. Employing the WKB method, we derive the AB phase shift for a time-dependent magnetic vector potential, demonstrating that for circular paths in the quasistatic regime, it is proportional to the time-averaged enclosed magnetic flux, \(Δφ_{\rm AB} = \frac{1}{T} \int_0^T e Φ(t) \, dt\), with the total phase shift, including kinetic contributions, equaling \(e Φ(0)\). For non-circular paths, the phase shift depends on both the flux history and path geometry, revealing the effect's hybrid nature involving gauge potentials and induced electric fields. We verify the consistency of our gauge choice with Maxwell's equations and discuss the implications for local versus nonlocal interpretations of the AB effect. We also generalize the results to scenarios with nonzero external magnetic fields, where the enclosed flux is through the actual electron paths, and for circular paths of radius $R$, the AB phase shift is also proportional to the time average of the enclosed flux \(Φ_{\rm enc}(R,t)\), with the total phase shift depending only on the initial enclosed flux \(e Φ_{\rm enc}(R,0)\); for general non-circular paths, the external magnetic field affects trajectories and phase accumulation through the Lorentz force, leading to additional path dependence. These findings clarify the role of gauge-dependent potentials and induced fields in the generalized AB effect, offering new theoretical insights and potential applications in quantum technologies.

We construct an explicit linear map from compact, conserved thermodynamic/effective-medium perturbations of the stress-energy tensor to the metric response in static, spherically symmetric spacetimes, and from there to geometric observables of direct relevance to horizon-scale imaging: the shadow radius and photon-sphere frequency. The response is expressed through $L^{1}$-bounded kernels written in a piecewise "local $+$ tail" form, which makes transparent the separation between near-photon-sphere sensitivity and far-zone contributions (including AdS tails). Under mild assumptions on the matter susceptibilities near a critical point, dominated convergence transfers the thermodynamic exponent to the geometric susceptibility, $γ_{\rm sh}=γ_{\rm th}$, with controlled analytic corrections. We further provide AdS far-zone bounds with explicit outside-support constants depending only on background geometric data at the photon sphere and shell geometry. A reproducible numerical pipeline with convergence diagnostics is presented and benchmarked.

Fault-Tolerant Quantum Computing (FTQC) relies on Quantum Error Correction (QEC) codes to reach error rates necessary for large scale quantum applications. At a physical level, QEC codes perform parity checks on data qubits, producing syndrome information, through Syndrome Measurement (SM) circuits. These circuits define a code's logical error rate and must be run repeatedly throughout the entire program. The performance of SM circuits is therefore critical to the success of a FTQC system. While ultimately implemented as physical circuits, SM circuits have challenges that are not addressed by existing circuit optimization tools. Importantly, inside SM circuits themselves errors are expected to occur, and how errors propagate through SM circuits directly impacts which errors are detectable and correctable, defining the code's logical error rate. This is not modeled in NISQ-era tools, which instead optimize for targets such as gate depth or gate count to mitigate the chance that any error occurs. This gap leaves key questions unanswered about the expected real-world effectiveness of QEC codes. In this work we address this gap and present PropHunt, an automated tool for optimizing SM circuits for CSS codes. We evaluate PropHunt on a suite of relevant QEC codes and demonstrate PropHunt's ability to iteratively improve performance and recover existing hand-designed circuits automatically. We also propose a near-term QEC application, Hook-ZNE, which leverages PropHunt's fine-grained control over logical error rate to improve Zero-Noise Extrapolation (ZNE), a promising error mitigation strategy.

Levitated nanodiamonds hosting a single nitrogen-vacancy (NV) center provide an ultra-low-frequency mechanical mode with widely tunable dissipation and spin backaction under microwave dressing and optical pumping. We demonstrate that the driven NV spin can be tuned to act as an inverted gain medium for the center-of-mass motion, thereby stabilizing an autonomous phonon maser. In the separation-of-timescales regime where spin dynamics is fast, adiabatic elimination yields a reduced mechanical master equation with closed-form, detuning-dependent transition rates and a sharp threshold given by the sign change of the phonon-number damping. For representative levitated-NV parameters, we find that a percent-level dressed-basis inversion is sufficient to reach the threshold, and the small-signal gain can exceed the intrinsic mechanical loss by orders of magnitude. Full master-equation simulations confirm above-threshold self-oscillation and a phase-diffusing, coherent steady state, whose saturation follows the Maxwell-Bloch prediction.

The quantum phase transitions provide a paradigm for studying collective quantum phenomena that are a result of competing non-commuting interactions. This paper will study the ground state properties and quantum critical dynamics of the one-dimensional transverse field Ising model through a combined perspective that includes exact diagonalisation, variational quantum eigensolver (VQE) simulations, and simulations on realistic physical quantum devices. We focus on a lattice of four spins, where we calculate the ground-state energies, magnetic order parameters and correlation functions at uniformly applied conditions, which is repeated by all systems. Precise diagonalisation provides both a benchmark, which is symmetry-conserving, and a depth-two, physics inspired variational approximation, which provides simulations accessible to hardware. The circuits that have been optimised identically are then placed on the IQM Garnet quantum processor, using a resource-efficient batched protocol. We find that the ground-state energies of shallow variational circuits are reliably captured by the circuit over the entire parameter space; the magnetic arrangement parameters and observables sensitive to correlation signal significantly more noise. The error analysis of quantitative analysis reveals a strong broadening of critical crossover on hardware, which is consistent with the noise attenuation of long-range correlations. These findings highlight the current capabilities as well as the fundamental limitations of noisy intermediate-scale quantum systems in modelling quantum critical phenomena as a benchmark to future enhancements in obtaining quantum hardware and quantum algorithms development.

Quantum voting protocols aim to offer ballot secrecy and publicly verifiable tallies using physical guarantees from quantum mechanics, rather than relying solely on computational hardness. This article surveys whether such quantum voting protocols are practical. We begin by outlining core mathematical ideas such as the superposition principle, the no-cloning theorem, and quantum entanglement. We then define a common system and threat model, identifying key actors, trust assumptions, and security goals. Representative protocol families are reviewed, including entanglement-based schemes with central tallying, self-tallying designs that enable public verification, and authority-minimized approaches that certify untrusted devices through observable correlations. Finally, we evaluate implementation challenges, including loss, noise, device imperfections, scalability, and coercion resistance, and discuss realistic near-term deployment scenarios for small-scale elections.

Motivated by quantum-gravity scenarios that replace the classical black hole singularity with a regular core, and by the possibility that the dark-energy sector may be scale dependent, we construct a broad class of nonsingular rotating black-hole spacetimes embedded in an improved de Sitter--like background with either constant or running $Λ$. Because the Newman--Janis algorithm is generically incompatible with a cosmological-constant fluid, we instead propose a generalized Kerr--Schild construction on a (possibly scale-dependent $Λ$) de Sitter seed, yielding a Carter-type metric characterized by a mass function and a $Λ$ function. Our construction provides a direct map from static, spherically symmetric regular models to their rotating counterparts. We derive sharp regularity conditions at the ring and we identify a minimal-order subclass. We analyze chronology and show that, for non-negative mass function and $Λ$ above a certain negative limit, the spacetimes are stably causal. For minimal-order geometries with non-negative mass, we prove that the weak energy condition must be violated. Finally, we illustrate the framework with an asymptotic-safety--inspired model and discuss horizon structure, surface gravities, and conformal diagrams. These results provide a controlled, observationally oriented arena to confront regular rotating black holes in dark-energy backgrounds with the rapidly improving gravitational-wave and horizon-scale imaging data.

In this work, we derive the conservative dynamics of non-spinning binaries in the massless scalar-tensor theories using the post-Minkowskian Effective Field Theory (EFT) approach. Our main result is an analytic expression of the scattering angle, computed up to third Post-Minkowskian order via two-loop Feynman diagrams. Our results are in perfect agreement with previous literature, in particular within the post-Newtonian limit.

Functional encryption is a powerful cryptographic primitive that enables fine-grained access to encrypted data and underlies numerous applications. Although the ideal security notion for FE (simulation security) has been shown to be impossible in the classical setting, those impossibility results rely on inherently classical arguments. This leaves open the question of whether simulation-secure functional encryption can be achieved in the quantum regime. In this work, we rule out this possibility by showing that the classical impossibility results largely extend to the quantum world. In particular, when the adversary can issue an unbounded number of challenge messages, we prove an unconditional impossibility, matching the classical barrier. In the case where the adversary may obtain many functional keys, classical arguments only yield impossibility under the assumption of pseudorandom functions; we strengthen this by proving impossibility under the potentially weaker assumption of pseudorandom quantum states. In the same setting, we also establish an alternative impossibility based on public-key encryption. Since public-key encryption is not known to imply pseudorandom quantum states, this provides independent evidence of the barrier. As part of our proofs, we show a novel incompressibility property for pseudorandom states, which may be of independent interest.

As the ability to integrate single photon emitters into photonic architectures improves, so does the need to characterize and understand their interaction. Here, we use a scanning diamond nanocrystal to investigate the interplay between the emission of room-temperature nitrogen-vacancy (NV) centers and a proximal topological waveguide. In our experiments, NVs serve as local, spectrally broad light sources which we exploit to characterize the waveguide bandwidth as well as the correspondence between light injection site and directionality of wave propagation. Further, we find that near-field coupling to the waveguide influences the spectral shape and ellipticity of the NV photoluminescence, hence allowing us to reveal nanostructured light fields with a spatial resolution defined by the nanoparticle size. Our results expand on the sensing modalities afforded by color centers, and portend novel opportunities in the development of on-chip, quantum optics devices leveraging topological photonics to best manipulate and readout single-photon emitters.

Induced gravity, defined as a globally scale-invariant ``first-generation'' scalar-tensor theory, is investigated within the framework of the thermodynamics of modified gravity theories. The ``temperature of gravity'' and its evolution equation are derived for this model, and the resulting expressions are used to analyse General-Relativity equilibrium states and to investigate the possible existence of an attractor mechanism toward Einstein's theory with a cosmological constant.

We consider the Diósi-Penrose problem but rather than postulating background gravitational fluctuations, we instead consider the quantum filter that arises from space-time homodyning the continuum of output quadrature described in the open quantum stochastic model presented here. This is described by a quantum Kushner-Stratonovich equation, typical of the form appearing in continuous-time collapse of the wave-function models in Quantum Decoherence Theory

In this paper, we explore a theoretical and observational study of the presence of viscosity in the Unimodular Gravity formalism, a pioneering approach that, to the best of our knowledge, has not been previously explored in this context. Specifically, we study a flat FLRW universe at late times, where matter experiences dissipative processes in the form of a bulk viscosity, in the framework of Eckart's theory, which is linked to the energy diffusion function $Q$ through the power law $ξ=ξ_{0}\left|Q\right|^{1/2}$, being $ξ_{0}$ a positive dimensionless parameter. By assuming the ansatz $Q=νH^{2}$, where $H$ is the Hubble parameter and $ν$ is a dimensionless arbitrary constant, we find analytical solutions for the cosmological evolution. We test these models against the most recent cosmological observations, including type Ia supernovae, baryon acoustic oscillations, cosmic chronometers, gravitational lensing, and black hole shadow data. Our results show that two of the tested models provide a significantly better fit to the data ($χ_{\text{min}}^{2}$) and remain as competitive as $Λ$CDM model according to the Bayesian Information Criterion. These findings, combined with the inherent ability of Unimodular Gravity to alleviate the cosmological constant problem, position dissipative UG as a robust and compelling alternative to the standard model, potentially suggesting that a very small but nontrivial energy nonconservation is compatible with the late-time observational data.

This work presents a pedagogical and self-contained derivation of the first-order effective Hamiltonian for the two-mode Jaynes-Cummings model in the dispersive regime. A perturbative unitary transformation removes nonresonant atom-field terms, revealing dispersive frequency shifts leading to an atom-induced effective beam-splitter interaction between the field modes. The resulting Hamiltonian is diagonalized through a simple geometric rotation in the two-mode bosonic space, providing a transparent interpretation of the underlying dynamics. The exposition emphasized clarity and physical insight, making effective Hamiltonian methods accessible for teaching and learning in multimode light-matter interactions.

We prove infinitesimal rigidity and integrability of the moduli space for Hermitian gravitational instantons. Together with the recent proof by Biquard, Gauduchon, and LeBrun of local rigidity for Hermitian instantons, this completes the picture of the moduli space of Hermitian gravitational instantons, both for the compact and non-compact cases. An important step in the proof is to show that provided certain boundary conditions hold, a curve of Riemannian metrics passing through a Hermitian non-Kähler Einstein metric is conformally Kähler to second perturbative order. This uses ideas of Wu and LeBrun.

We extend our recently developed semiclassical strong-field Herman-Kluk propagator (SFHK) method to calculate high-order harmonic generation (HHG) for atoms in intense lasers. We show that our method, based on a combination of the Herman-Kluk propagator and the strong-field approximation, can provide highly accurate results for both HHG yield and phase, nearly identical to those from the exact numerical solutions of the time-dependent Schrödinger equation. We provide detailed analyses of our method and its applications to the HHG process, particularly the recombination time. The main computational task in this method is to solve the classical Newton equations for the active electron in the combined atomic potential and laser-electron interaction. The motion of the centers of the electron wave packets, modeled by coherent states, is governed by independent classical trajectories so that the computation can therefore be parallelized very efficiently.

In Einstein equations we represent the energy-momentum tensor as the one ($T^{μν}$ ) of an ideal fluid plus the cosmological term. We consider time-dependent Newton ``constant" $G$, the cosmological term $Λ$ and non-conserved $T^{μν}$. The Bianchi identity imposes a relation between the energy-momentum (non)conservation and the variation of $G$ and $Λ$. If the energy-momentum $T^{μν}$ is conserved then both the Newton ``constant" $G$ and the cosmological term $Λ$ either do not change or both must depend on time. If the energy-momentum $T^{μν}$ is not conserved then the Bianchi identity implies a relation between the energy-momentum and a variation in time of either $G$ or $Λ$ (or both). We apply thermodynamics in order to express the non-conservation of the energy-momentum of an ideal fluid by entropy and relate the time variations of $G$ and $Λ$ to a change of entropy. Using the relation between a varying Newton constant G and the black hole entropy we derive a modified formula for the Schwarzschild black hole evaporation (a slower evaporation). Its life time is $\frac{9}{5}$ times larger than the one for a constant $G$. The average black hole's density remains constant when the black hole's radius shrinks to zero .

We study cosmological models using dynamical systems and averaging methods, encompassing flat and open FLRW geometries as well as the LRS Bianchi types I, III, and V. Under mild regularity and frequency-scaling assumptions, we obtain a near-identity conjugacy between the oscillatory flow and an averaged slow flow, with $\| \mathbf{x}(t)-\bar {\mathbf{x}}(t)\| =\mathcal{O}(H(t))$. The effective systems preserve the original asymptotics and yield geometry-dependent late-time attractor classifications. A corollary addresses the case in which the leading averaged vector field vanishes, so the system exhibits no autonomous drift at order $H^0$.

Entanglement, a defining feature of quantum mechanics, arises naturally from interactions between molecular systems. Yet the precise nature and quantification of entanglement in the products of molecular collisions and reactions remain largely unexplored. Here, we show that coupling between the external (motional) and internal degrees of freedom of the colliding molecules generates diverse forms of product-state entanglement: discrete-discrete, continuum-continuum, and hybrid discrete-continuum. We develop a general theoretical framework to quantify these entanglement forms directly from scattering S-matrix elements and identify a novel class of entangled states-multimode hybrid cat states, that exhibit multimode discrete-continuum entanglement. Although applicable at arbitrary collision energies, the formalism is illustrated in the ultracold and cold regimes for inelastic Rb+SrF and Rb+Sr$^+$ collisions, as well as the chemical reaction F+HD $\rightarrow$ HF+D, DF+H. We demonstrate that entanglement can be efficiently controlled near magnetic Feshbach resonances, paving the way for precise magnetic control of product-state entanglement generation in ultracold molecular collisions.

Recent observations of supermassive black holes (SMBHs) at high redshifts pose challenges to standard seeding mechanisms. Among competing models, the collapse of self-interacting dark matter (SIDM) halos provide a plausible explanation for early SMBH formation. While previous studies on modeling the gravothermal collapse of SIDM halos have primarily focused on non-relativistic evolution under the assumption of hydrostatic equilibrium, We advance this framework by relaxing the equilibrium assumption and additionally incorporating general-relativistic effects. To this end, we introduce the Misner-Sharp formalism to the SIDM context for the first time. Our model reproduces the standard hydrostatic models in the early long-mean-free-path (LMFP) regime, but displays interesting distinct behavior in the late short-mean-free-path (SMFP) regime, where intense outward heat flux drives a rapid expansion of the outer envelope, removing mass from the core and significantly decelerating the collapse. Our general relativistic treatment enables us to follow halo evolution to the final stage when the apparent horizon forms. Our simulation yields a seed black hole mass of approximately $3\times10^{-8}$ of the halo mass at horizon formation, suggesting that additional mechanisms such as baryonic effects are critical for seeding black holes that are sufficiently massive to account for SMBHs in the early Universe.

We consider the spherically reduced Einstein-Hilbert action, Einstein field equations and Schwarzschild spacetime modified by a renormalization-group (RG) scale-dependent gravitational Newton coupling, and present a systematic and operational approach to such an RG-improvement. The master field equations for spherically symmetric gravitational fields, recently constructed from two-dimensional Horndeski theory, allow us to retain partial contributions from higher-curvature truncations of the effective action, while preserving the second-order nature of the resulting field equations. Static RG-improved black-hole spacetimes with an effective gravitational coupling depending on the areal radius and the Misner-Sharp mass are derived as vacuum solutions to these master field equations, and are thereby identified as solutions to generally covariant two-dimensional Horndeski theories. We discuss explicitly the embedding of previous key works on RG-improvement into the newly developed formalism to illustrate its broad range of applicability. This formalism moreover allows us to establish explicitly the discrepancies in the outcomes of RG-improvement when implemented at the level of the action, in the field equations, or in the Schwarzschild solution.

This paper develops a geometric mechanics framework for the reduction of general relativistic hydrodynamic variational principles, from the variation of worldlines approach in 4D spacetime to 3-dimensional Eulerian descriptions. We consider a self-gravitating, barotropic fluid and obtain the Euler-Poincaré equations of the system by Lagrangian reduction. Using the decomposition of general relativity into 3 + 1 dimensions, with a direction of time defined, the gauge invariance of the action over spacetime diffeomorphisms permits a 3-dimensional description of the fluid diffeomorphism by gauge fixing. The configuration space thus mirrors the Newtonian case, and by employing the Euler-Poincaré theorem, we derive the Eulerian equations of motion, in the same form as the PDEs from Newtonian fluid dynamics. The equations of motion are then derived, where the fluid variables are measured in a separate frame of reference from the variables involving gravity. Furthermore, the general relativistic barotropic fluid exhibits a Kelvin-Noether circulation conservation, which we derive in both the inertial and moving frames of reference. Finally, potential applications to Numerical Relativity are discussed.

We present the thermodynamic properties of a noncommutative Reissner Nordstrom (NCRN) black hole (BH) modeled with Lorentzian distributions. The analysis is carried out using a Legendre invariant formalism called Geometrothermodynamics (GTD) which is applied to a quasihomogeneous system generated by the NCRN BH. This formalism enables the study of phase transitions by locating Ricci scalar singularities, from which the phase transition points are determined in terms of the thermodynamic variables. We also examine how the noncommutative (NC) parameter can be interpreted as a thermodynamic variable within quasi-homogeneous thermodynamic laws, highlighting its potential role on phase transitions beyond those well-known characterized by divergences in the heat capacity.

This article presents a concrete mathematical framework for the generation of entangled quantum states from classical stochastic processes. We demonstrate that any density operator $ρ_{AB}$ of a composite system can be derived from the correlations between two underlying stochastic processes, $X(t)$ and $Y(t)$, representing the random fluctuations of its subsystems. This construction utilizes a two-scale temporal scheme - micro and macro time - where quantum correlations emerge as macro-correlations derived from underlying micro-correlations. We propose the Double Covariance Model (DCM), which reproduces the fundamental properties of quantum theory by treating the quantum state as the fourth-order moment structure of an underlying classical probability space.

We develop a radial integral reformulation of finite distance gravitational lensing in optical geometry for static, spherically symmetric spacetimes. Starting from the Gauss-Bonnet characterization of the finite distance deflection angle, we adopt the Li-type curvature primitive identity [https://doi.org/10.1103/PhysRevD.101.124058] [2006.13047], which reduces the curvature-area contribution to a one-dimensional integral evaluated along the physical light ray. We then remove the remaining implicit orbit dependence by an explicit change of variables using the null first integrals, converting the Li line integral from $φ$-integration to $r$-integration and splitting the trajectory at the turning point (closest approach). The resulting formula expresses the deflection angle as a sum of two radial integrals over $[r_0,r_S]$ and $[r_0,r_R]$ plus the finite distance angular bookkeeping term, with a transparent normalization/cancellation structure for the curvature primitive. In Schwarzschild gauge, we provide a weak-field evaluation toolkit that reduces the computation to reusable families of standard radial integrals and gives compact expressions for the endpoint incidence angles in the optical metric. Worked examples include Schwarzschild and Kottler (Schwarzschild-de Sitter) spacetimes and a black hole immersed in perfect-fluid dark matter with a finite halo. For the finite-halo model we derive closed-form leading weak-deflection expressions for mixed endpoint configurations (source/receiver inside or outside the halo), illustrating the modularity of the radial Gauss-Bonnet pipeline.

The Galactic Center black hole environment gives us new opportunity to test deviation from General Relativity and black hole physics. In this work we analytically generate the shape of the Galactic Center black hole by using a recently developed exact stationary, axisymmetric and vacuum solution of $f(R)$ gravity theory. By using scalaron mass as a free parameter we find that the shadow shape along with displacement and asymmetry is sensitive to the scalaron mass, even after keeping the black hole spin low. We recognize scalaron mass which is compatible with Kerr like quadrupole moment and hence black hole "no-hair" theorem. The same mass scale is found to reproduce the PPN parameter ($γ$) constrained in the weak field limit of the solar system. Gravitational identifiers, the Kretschmann scalar ($κ$) and gravitational potential ($φ$) have been used to infer scalaron masses in the regime of S-stars which are found to be consistent with the limits obtained using shadow scales. We ensure that $f(R)$ gravity scalaron has an appropriate general relativistic limit in the horizon scale of the black hole. We also identify the possibility of scale invariance of the general relativistic limit.

Color center spins in 4H-SiC offer a rare combination of wafer-scale materials maturity with long spin coherence and chip-level photonics, making them promising building blocks for scalable quantum technologies. In particular, the silicon vacancy hosts an S=3/2 ground state, a native qudit that enables compact encodings and subspace-selective control, but also introduces spectator transitions: short, detuned pulses can coherently drive non-addressed level pairs and create crosstalk. Here we use broadband Ramsey interferometry to reveal and quantify such spectator-transition crosstalk. Experimentally, the Ramsey Fourier spectra display multiple lines beyond the addressed single-quantum transition. Analytically, we map each line to a pairwise energy difference between qudit levels of the rotating-frame Hamiltonian and assign its weight via compact amplitudes set by the prepared state and the microwave pulse parameters, predicting a deterministic six-branch structure. Numerical time-domain propagation with the experimental sampling reproduces the detuning map, and the measured peak positions coincide with the analytic branch lines without frequency fitting. Together these results provide a practical, spectator-aware framework for multilevel control in the silicon vacancy qudit. The approach offers clear guidance to suppress crosstalk or, conversely, to exploit spectator lines, for example as additional constraints for in situ pulse calibration and for phase-sensitive quantum state and process estimation.

Tensor form factors encode essential information about the internal spin structure and tensor dynamics of baryons. In this work, we investigate the tensor form factors of the baryon hyperons $Ω^-$, $Σ^{*+}$, and $Ξ^{*-}$ within the framework of QCD sum rules. For the $Σ^{*+}$ and $Ξ^{*-}$ baryons, both isosinglet and isovector tensor currents are considered, allowing us to disentangle flavor-dependent tensor contributions. The complete set of tensor form factors is numerically evaluated in the momentum transfer region $0<Q^2<10~\text{GeV}^2$. In addition, the quark tensor charges of the considered hyperons are extracted in the forward limit. The results provide new non-perturbative insight into the tensor structure and spin content of spin-$3/2$ baryons and offer valuable theoretical input for future phenomenological analyses and experimental studies.

Confinement of dynamical fields can be attributed to the absence of corresponding asymptotic states. Thermodynamical properties of such system are more appropriately formulated in terms of collective excitations of these fields, if they appear as particles. This mechanism is investigated in the mean-field quark model of confinement and hadronization. In this model, deconfinement and restoration of chiral symmetry happen simultaneously at certain critical temperature.

In this paper, we compare and analyze holographic timelike entanglement entropy with pseudo-entropy in quantum mechanics for a two-qubit system, considering transitions from a thermal state to an anisotropic thermal state at fixed temperature. We identify several common properties but also report important differences between these quantities.

In this work, we perform a comprehensive investigation of the production of high-spin $ω_J$ and $ρ_J$ mesons ($J=3,4,5$) in $π^{-}p$ reactions using an effective Lagrangian approach. By constructing the relevant $t$-channel processes and calibrating the model with a single adjustable parameter fitted to existing data, we successfully reproduce the measured total and differential cross sections for the $J=3$ states $ω_3(1670)$ and $ρ_3(1690)$. Within the same framework, we predict the production cross sections for the higher-spin partners $ω_4(2250)$, $ρ_4(2230)$, $ω_5(2250)$, and $ρ_5(2350)$. Our results show that these states exhibit measurable cross sections with characteristically forward-peaked angular distributions, underscoring their strong potential for observation in future $πp$ meson-beam experiments.

We construct a holographic c-function for sine dilaton gravity (sDG) in the domain wall gauge. We show the equivalence between sDG and the two copies of Liouville conformal field theory (LCFT) and compute the associated central charge. We reproduce the central charge of LCFT from the holographic c-function of sDG corresponding to the UV limit. In contrast, in the deep IR limit, the c-function flows to the pure JT gravity, where the central charge becomes identically zero.

Water Cherenkov Detectors (WCDs), traditionally employed in cosmic-ray detection, are repurposed here for precision soil moisture monitoring using cosmic-ray neutron sensing. This approach offers advantages over conventional neutron probes, including enhanced sensitivity to low moisture levels and the ability to cover larger soil volumes without subsurface intrusion. This study evaluates the feasibility of WCDs for agricultural neutron hydrometry, addressing challenges such as background suppression and data interpretation in heterogeneous soils. We present experimental results from controlled wet and dry soil-condition emulations, alongside Monte Carlo simulations using Geant4 with an atmospheric neutron spectrum to correlate signal variation with soil-moisture differences. By bridging particle physics and agronomy, WCDs could advance soil moisture monitoring, offering a non-invasive, scalable, and accurate alternative for optimizing agricultural water use. Preliminary findings suggest a transformative potential for sustainable farming, though further research is needed to enhance cost-efficiency and adaptability to diverse soil types.

Gauge-mediated supersymmetry-breaking models provide a strong motivation to search for a supersymmetric partner of the tau lepton (stau) with a macroscopic lifetime. Long-lived stau decays produce tau leptons that are displaced from the primary proton-proton interaction vertex, leading to an unconventional signature. This paper presents a search for the direct production of long-lived staus decaying within the CMS tracker volume in proton-proton collisions at $\sqrt{s}$ = 13 TeV, performed for the first time with an identification algorithm based on a graph neural network dedicated to displaced tau leptons. The data sample, corresponding to an integrated luminosity of 138 fb$^{-1}$, was recorded with the CMS experiment at the CERN LHC between 2016 and 2018. This search excludes, at 95% confidence level, stau masses, $m_\tildeτ$, in the 126$-$260 (906$-$425) GeV range for a proper decay length of 50 mm in the maximally mixed (mass-degenerate) scenario, while for $m_\tildeτ $ = 200 GeV, stau proper decay lengths are excluded in the range 21$-$94 (6$-$333) mm. These results improve the exclusion limits compared to previous searches, and extend the parameter space explored in the context of supersymmetry.

The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (2023$-$2024) conditions, the jet energy resolution improves by 10$-$20% for jets with transverse momentum between 30$-$100 GeV. Inference time is evaluated using simulated multijet events, with a median of 20 ms per event on an Nvidia L4 GPU, compared to approximately 110 ms for the standard CMS PF reconstruction.

In the physics program of the CMS experiment during the CERN LHC Run 3, which started in 2022, the long-lived particle triggers have been improved and extended to expand the scope of the corresponding searches. These dedicated triggers and their performance are described in this paper, using several theoretical benchmark models that extend the standard model of particle physics. The results are based on proton-proton collision data collected with the CMS detector during 2022$-$2024 at a center-of-mass energy of 13.6 TeV, corresponding to integrated luminosities of up to 123 fb$^{-1}$.

GPUs have become the dominant source of computing power for high performance computing and are increasingly being used across the High Energy Physics computing landscape for a wide variety of tasks. Though NVIDIA is currently the main provider of GPUs, AMD and Intel are rapidly increasing their market share. As a result, programming using a vendor-specific language such as CUDA can significantly reduce deployment choices. There are a number of portability layers such as Kokkos, Alpaka, SYCL, OpenMP and std::par that permit execution on a broad range of GPU and CPU architectures, significantly increasing the flexibility of application programmers. However, each of these portability layers has its own characteristics, performing better at some tasks and worse at others, or placing limitations on aspects of the application. In this presentation, we report on a study of application and kernel characteristics that can influence the choice of a portability layer and show how each layer handles these characteristics. We have analyzed representative heterogeneous applications from CMS (patatrack and p2r), DUNE (Wire-Cell Toolkit), and ATLAS (FastCaloSim) to identify key application characteristics that have different behaviors for the various portability technologies. Using these results, developers can make more informed decisions on which GPU portability technology is best suited to their application.

Potential experimental signatures of gluon generalized transverse momentum-dependent distributions (GTMDs) are proposed via exclusive heavy (axial-)vector meson production in lepton-proton collisions. Within the framework of collinear twist-3 factorization, we show that specific azimuthal-angle-dependent observables can provide sensitivity to the gluon GTMDs $F_{1,4}^g$ and $G_{1,1}^g$, which are related to partonic orbital angular momentum and spin-orbit correlations, respectively. These functions represent a unique sector of nucleon structure with no counterparts in the generalized parton distribution or transverse-momentum-dependent frameworks. We show that interference between different virtual-photon polarizations leads to distinct azimuthal modulations, including the polarization-independent $\cos 2φ$ and polarization-dependent $\sin 2φ$ terms, with $φ$ defined as the angle between the lepton scattering plane and the hadron production plane. These observables provide signatures of the elusive gluon GTMDs $F_{1,4}^g$ and $G_{1,1}^g$, opening a new channel to access the spin structure of the nucleon at the future Electron-Ion Collider.

These proceedings summarize the latest progress by the ATLAS Experiment at the LHC in measuring diboson production and related searches for physics beyond Standard Model via anomalous gauge couplings with the latest Effective Field Theory approach. The most recent measurements of $W^{+}W^{-} \to \ell^{+}ν\ell^{-}\barν$, $W^{\pm}Z \to 3\ell1ν$ and $Z(\to \ell^{+}\ell^{-})γ$ measurements with ATLAS full Run 2 dataset are presented, along with the highlights of the first evidence of $W^{+}W^{-}$ charge asymmetry, the first measurement of CP-violation sensitive observables in $WZ$, and, for the first time at the LHC, $SU(2)_L \otimes U(1)_Y$ fully gauge invariant anomalous neutral triple gauge coupling limits with $Z(\to \ell^{+}\ell^{-})γ$ process.

The ``Practical Dirac-Majorana Confusion Theorem'' said that phenomenological differences between Dirac and Majorana neutrinos are suppressed by $(m_ν/E)^2$ in lepton-number-conserving processes, making them experimentally indistinguishable at high energies. In this work, we propose a generalization of this theorem by introducing a New Physics vector boson ($Z'$) with CP-violating couplings. We demonstrate that the Majorana condition imposes that only CP-violating imaginary part contributes to the vector neutral interaction. Consequently, the difference between Dirac and Majorana neutrinos in cross-section becomes directly dependent on the CP-violating phase $φ$. We apply this framework to Coherent Elastic Neutrino-Nucleus Scattering (CE$ν$NS), showing that for spin-zero targets, the distinguishability of the neutrino nature is determined by the CP structure of the interaction.

We explain that the logic behind the derivation of the Noumi-Shiraishi function can be applied directly to the Baker-Akhiezer function (BAF). This amounts to changing an ansatz for BAF to a nested one, where the BAF of N + 1 variables is recursively expressed as a sum over BAFs of N variables. This may be seen as a generalization of symmetrization trick from [1], but for the generally non-symmetric BAF. We demonstrate that, for usual non-twisted (a = 1) BAFs, this method correctly reproduces the Noumi-Shiraishi formula directly from linear equations, resolving the ambiguity related to non-simple roots. For the first non-trivial twisted case (N = 3, a = 2) this method also fixes this ambiguity, moreover, answers for the first few layers of coefficients are in the form of direct quantization of [1].

We propose a diagnostic tool, a temperature estimator, for lattice gauge theory simulations. The estimator is obtained from the gradient and the Hessian of the Euclidean lattice action. It is gauge invariant, configuration-based, and independent of momentum-space information. These features enable direct checks of thermodynamic consistency in Monte Carlo simulations. We apply this tool to compact U(1) lattice gauge theories in one, two, and four dimensions. The results confirm the proposed estimator's ability to reproduce the input temperatures across different lattice ensembles. The estimator is sensitive to sampling inefficiencies and algorithmic artifacts, making it a useful diagnostic for large-scale simulations.

Using the Argonne National Laboratory-The University of Osaka (ANL-Osaka) DCC model of meson-nucleon reactions, we extend the study of Phys. Rev. C 79, 025206 (2009) and Phys. Rev. C 88, 045203 (2013) to predict the cross sections of the $πN \to ππN$ reactions. The model was constructed by fitting only the two-body reactions: $πN,γN \to πN, ηN, KΛ, KΣ$. Thus, the results for $πN \to ππN$ presented here are predictions of the ANL-Osaka DCC model, which serve to examine the extent to which the forthcoming data from J-PARC can be described. This study provides information for improving the extraction of nucleon resonances that have large decay widths to $ππN$ states. We present results for the total cross sections, invariant mass distributions, and angular distributions. We also identify the observables and energy regions where the higher mass nucleon resonances in the $S_{31}$, $P_{33}$, $D_{33}$, $F_{37}$, $D_{13}$, $D_{15}$, and $F_{15}$ partial waves can be most effectively investigated.

Detailed description of the calculation of the 2-loop self-energy for a scalar particle is presented. By employing a simple sector decomposition method, the ultraviolet divergent part is efficiently separated from the finite part. The resulting expression can be used for both analytic and numerical computation to renormalize the divergence and to provide finite results for physics.

Quarkyonic matter is expected to play a key role for the transition from hadronic matter to quark matter in compact stars. Within the framework of the relativistic mean field (RMF) model and equivparticle model with density-dependent quark masses, we construct the ``quark Fermi sea" with a ``baryon Fermi surface" to characterize the properties of the quarkyonic matter. In particular, we develop a comprehensive framework to account for the strangeness degrees of freedom, incorporating $Λ$, $Ξ$, and $Σ$ hyperons as well as strange quarks in a unified quarkyonic framework. Our calculations indicate that the inevitable emergence of hyperons softens the equations of state, leading to a reduction in the speed of sound around $n_{\rm b}\approx 2n_0$, and consequently reducing the masses and radii of neutron stars. When the quark-hadron phase transition is taken into account, the equation of state at high densities exhibits additional softening, leading to a maximum sound velocity of $v_{\rm max} \approx 0.6\,c$, which is close to the ultrarelativistic limit of $0.58\,c$, consistent with current astronomical observational constraints.

We propose a geometric formulation of heavy-quark transitions in which infrared-dressed states are adiabatically transported in the multidimensional recoil space and acquire Berry holonomies. Within this framework, single-step decays are governed by an abelian geometric phase and reproduce the standard Isgur-Wise behaviour, while sequential decays probe genuinely non-Abelian holonomies associated with a two-dimensional recoil space. The resulting geometric structure correlates different decay channels and provides a unified interpretation of mixing effects and quasi-degenerate states in heavy-quark phenomenology. This approach suggests that several long-standing puzzles arise as geometric consequences of infrared dressing rather than as accidental features of the microscopic dynamics.

In the present work, we analyze the properties of the transition form factors (TFFs) for the $γγ^*\to η^{(\prime)}$ process, employing the $η^{(\prime)}$-meson light-cone distribution amplitude (LCDA) derived within the light-cone sum rule framework. To this end, we adopt the quark-flavor mixing scheme for the $η^{(\prime)}$ meson, and compute the TFFs by systematically incorporating transverse-momentum corrections and contributions beyond the leading Fock state. We utilize light-cone harmonic oscillator models to parameterize the longitudinal and transverse behavior of the leading-twist light-cone wavefunction, for which the corresponding LCDA exhibits a unimodal profile. We further examine the potential contributions of intrinsic charm components to the scaled TFFs $Q^2 F_{ηγ}(Q^2)$ and $Q^2 F_{η^\prime γ}(Q^2)$. Leveraging a range of values for the decay constant $f_{η_{c_0}}$ and implementing the $η$-$η'$-$η_c$ and $η$-$η^\prime$-$G$-$η_c$ mixing mechanisms accordingly, together with the recently updated mixing angles, we investigate the impact of the intrinsic $c\bar{c}$ and gluonic component on these observables. In high-$Q^2$ regime, $Q^2 F_{η^\primeγ}(Q^2)$ exhibits a marked increase in sensitivity to the charm quark component, whereas $Q^2F_{ηγ}(Q^2)$ becomes notably stabilized. A detailed discussion of $χ^2/d.o.f$ and $p$-values indicates that the intrinsic charm quark component is important and yields a substantial, non-negligible contribution across the entire $Q^2$ range.

A comprehensive Bayesian analysis of the 3D dynamics of high-energy nuclear collisions is presented. We perform a systematic model-to-data comparison using simulations of large and small collision systems, and a broad range of measurements from the PHENIX, STAR, PHOBOS, and BRAHMS collaborations spanning nearly two decades of RHIC operations. In particular, we perform fully 3D multi-stage simulations including rapidity-dependent energy deposition with global energy conservation using the 3D Glauber model, along with relativistic viscous hydrodynamics with MUSIC. We calibrate the model on rapidity- and $p_T$-differential observables and analyze the respective constraints on initial state and transport properties they provide. We emphasize the additional constraints provided by rapidity-dependent measurements, the differences in large and small system calibrations, and the tension exhibited by particular observables. We use our calibrated model to make predictions of observables in p-Au and $^3$He-Au collisions. Furthermore, we facilitate direct comparison of experimental measurements by highlighting the dependence of flow measurements on the rapidity of the regions of interest and reference, as well as the importance of the centrality selection. In particular, we examine the apparent differences between the STAR and PHENIX $v_2$ and $v_3$ measurements in small systems.

We present JetFormer, a versatile and scalable encoder-only Transformer architecture for particle jet tagging at the Large Hadron Collider (LHC). Unlike prior approaches that are often tailored to specific deployment regimes, JetFormer is designed to operate effectively across the full spectrum of jet tagging scenarios, from high-accuracy offline analysis to ultra-low-latency online triggering. The model processes variable-length sets of particle features without relying on input of explicit pairwise interactions, yet achieves competitive or superior performance compared to state-of-the-art methods. On the large-scale JetClass dataset, a large-scale JetFormer matches the accuracy of the interaction-rich ParT model (within 0.7%) while using 37.4% fewer FLOPs, demonstrating its computational efficiency and strong generalization. On benchmark HLS4ML 150P datasets, JetFormer consistently outperforms existing models such as MLPs, Deep Sets, and Interaction Networks by 3-4% in accuracy. To bridge the gap to hardware deployment, we further introduce a hardware-aware optimization pipeline based on multi-objective hyperparameter search, yielding compact variants like JetFormer-tiny suitable for FPGA-based trigger systems with sub-microsecond latency requirements. Through structured pruning and quantization, we show that JetFormer can be aggressively compressed with minimal accuracy loss. By unifying high-performance modeling and deployability within a single architectural framework, JetFormer provides a practical pathway for deploying Transformer-based jet taggers in both offline and online environments at the LHC. Code is available at https://github.com/walkieq/JetFormer.

We investigate the pair production process $e^+e^- \to H^+H^-$ in the framework of the scotogenic model. The production mechanism receives contributions at tree level from photon and $Z$-boson exchange, as well as from $t$-channel exchange of the new singlet right-handed fermions $N_{1,2,3}$. where neutrino masses are generated radiatively and one of the singlet right-handed fermions serves as a viable dark matter candidate. We evaluate the individual contributions of these diagrams and compute the total production cross section after imposing all relevant theoretical and experimental constraints on the model parameters, including those associated with dark matter relic abundance and direct detection limits. Our results demonstrate that the dominant contribution to the cross section originates from the exchange of the singlet fermions $N_{1,2,3}$, particularly from the dark matter component of the spectrum. In addition, we examine the dependence of the cross section on the center-of-mass energy for several benchmark scenarios in the allowed parameter space. These predictions can be probed at future high-energy $e^+e^-$ colliders, providing a sensitive test of the scotogenic framework and the role of fermionic dark matter, as well as enabling more stringent constraints on the model parameters.

We investigate the entangling properties of the color flux tube between a static quark-antiquark pair in pure gauge Yang-Mills theory. In earlier works, we defined a gauge-invariant flux tube entanglement entropy (FTE$^2$), the excess entanglement entropy of a region of gluon fields that can be attributed to the color flux tube, and demonstrated that it is finite in the continuum limit. FTE$^2$ was shown to have two contributions, one from the vibrations of the QCD string, and the other from its internal (color) degrees of freedom. In this work, we further explore the internal color component in (2+1)D Yang-Mills theory for $SU(N_c)$ gauge groups, varying $2\le N_c\le5$. We identify a novel physical scale in the theory, the entanglement radius $ξ_0$. This radius characterizes the transverse extent of the flux tube that must be completely severed by an entangling region to capture the entanglement entropy of color degrees of freedom. The key feature underlying this phenomenon is its topological nature. This is revealed through systematic studies of multi-slab entangling regions in which FTE$^2$ changes sharply when boundaries of the slabs completely cross-cut the flux tube. We find that $ξ_0$ increases approximately linearly with $N_c$ and is independent of both Rényi replica number and the inter-quark separation length. We also study FTE$^2$ as a function of the entangling region's transverse displacement from the static quark pair and observe behavior consistent with a previously identified intrinsic width $λ$ of the flux tube, with an extracted value in agreement with the inverse mass of the lightest glueball for the gauge groups studied.

While deep learning is transforming data analysis in high-energy physics, computational challenges limit its potential. We address these challenges in the context of collider physics by introducing EveNet, an event-level foundation model pretrained on 500 million simulated collision events using a hybrid objective of self-supervised learning and physics-informed supervision. By leveraging a shared particle-cloud representation, EveNet outperforms state-of-the-art baselines across diverse tasks, including searches for heavy resonances and exotic Higgs decays, and demonstrates exceptional data efficiency in low-statistics regimes. Crucially, we validate the transferability of the model to experimental data by rediscovering the $Υ$ meson in CMS Open Data and show its capacity for precision physics through the robust extraction of quantum correlation observables stable against systematic uncertainties. These results indicate that EveNet can successfully encode the fundamental physical structure of particle interactions, which offers a unified and resource-efficient framework to accelerate discovery at current and future colliders.

The ``fortuitous'' Bogomol'nyi-Prasad-Sommerfield (BPS) sector states in gauge theory have been argued to furnish a description, through holography, of generic BPS black hole microstates. They are expected to be strongly chaotic, a necessary feature to capture the black hole dynamics. This dovetails nicely with the existence of various random matrix models of JT supergravity with extended supersymmetry, within which the BPS chaos must be contained as a subsector. This paper identifies and studies a simple random matrix model that underlies all known random matrix models of JT supergravity. It is argued that it captures many essential universal features of fortuitous BPS chaos. The model is topological, naturally interpolating between the Bessel and Airy models, where the gap energy $E_0$ controls the interpolation, and seems to have a simple intersection theory interpretation.

A powerful way to test models with feebly interacting particles in the MeV to GeV mass range is through proton beam-dump experiments. In this paper, we compare the current sensitivity of CERN's NA62 experiment running in beam-dump mode with that of a hypothetical experiment using the same detectors in a future CERN ECN3 beam-dump facility. When optimising such an experiment, the geometric setup is particularly relevant for the specific new-physics scenario under study, since different production mechanisms can generate different angular distributions of new particles. We show that even the most minimalistic reconfiguration of the existing NA62 experiment's detectors can already provide a very competitive sensitivity and collect data immediately after the beam is available.

We conjecture a precise relation between the superconformal indices of two theories defined in different spacetime dimensions. The first is the three-dimensional ABJM theory describing the worldvolume of parallel M2-branes in M-theory on $\mathbb{R}^{10,1}$. The second is the $\mathcal{N}=(2,0)$ theory in six dimensions which describes the worldvolume of parallel M5-branes in the same background. As we review, the existence of such a duality is closely related to Imamura's proposal for the giant graviton expansion of the three-dimensional index. We check our conjecture against various results for the two indices available in the literature. Using an existing proposal of Hristov for the ABJM superconformal index, we verify our conjecture to the first three orders in an expansion around the six-dimensional Cardy limit.

We show that the Unruh response can be interferometrically suppressed or restored in a single Unruh--DeWitt detector whose center-of-mass is prepared in a coherent superposition of two uniformly accelerated worldlines. The two paths remain physically disjoint; the detector is read out in a path-erasing basis so that no which-path information is revealed. If the detector's energy gap is path dependent during the interaction, the branch amplitudes for first-order excitation become operationally indistinguishable and therefore add coherently. With appropriate tuning -- matching the gap-to-acceleration ratios of the two branches and choosing a single relative phase -- the conditional first-order excitation amplitude cancels, while reversing the phase restores the response. We derive these conditions in two complementary formalisms and interpret the mechanism as a relativistic analogue of electromagnetically induced transparency, which we term worldline-induced transparency. We also treat finite switching times explicitly and quantify how imperfect matching produces a residual signal, yielding a tolerance window rather than an idealized infinitely sharp condition.

We introduce a novel modeling pipeline for strongly lensed point sources, using the GIGA-Lens framework, running on four A100 GPUs via the JAX platform. Using simulations, we demonstrate accurate and precise recovery of image positions, fluxes, and time delays, together with inference of complex lens mass distributions -- including the mass density slope, $γ$ -- from images of lensed point sources alone. We further show that we can achieve statistical uncertainty of $\sim 3.6\%$ ($\sim 2.5\, \mathrm{km\, s^{-1}/Mpc}$) on $H_0$ from a single system, with full forward modeling, i.e., simultaneous inference of all lens model parameters together with $H_0$. We apply our pipeline to two well-studied lensed SNe Ia, Zwicky and iPTF16geu. For SN iPTF16geu, unlike previous modeling efforts, we model only the images of the lensed point source (the SN) and do not use the lensed images of the extended host-galaxy. Nevertheless, we are able to infer all of the mass parameters modeled in earlier studies, and our best-fit values, including $γ$, are fully consistent with published results. In the case of SN Zwicky, taking the same approach, however, we obtain an alternative best-fit model compared to published results, underscoring the importance of fully exploring the model parameter space.

We develop a fully analytical framework for predicting the one-point probability distribution function (PDF) of dispersion measures (DM) for fast radio bursts (FRBs) using the baryonification (BFC) model. BFC provides a computationally efficient alternative to expensive hydrodynamical simulations for modelling baryonic effects on cosmological scales. By applying the halo mass function and halo bias, we convolve contributions from individual halos across a range of masses and redshifts to derive the large-scale structure contribution to the DM PDF. We validate our analytical predictions against consistency-check simulations and compare them with the IllustrisTNG hydrodynamical simulation across a range of redshifts up to $z = 5$, demonstrating excellent agreement. We demonstrate that our model produces consistent results when fitting gas profiles and predicting the PDF, and vice versa. We show that the BFC parameters controlling the gas profile, particularly the halo mass scale ($M_\mathrm{c}$), mass-dependent slope ($μ$), and outer truncation ($δ$), are the primary drivers of the PDF shape. Additionally, we investigate the validity of the log-normal approximation commonly used for DM distributions, finding that it provides a sufficient description for a few hundred FRBs. Our work provides a self-consistent model that links gas density profiles to integrated DM statistics, enabling future constraints on baryonic feedback processes from FRB observations.

It is still not clear which environmental processes operate in filaments. Given the ubiquity of filaments and their importance in feeding clusters, a proper understanding of these mechanisms is crucial to a more complete picture of galaxy evolution. To investigate them, we need large galaxy samples with resolved imaging. For this study, we analyse resolved H$α$ maps of 685 galaxies inside and outside the filaments around the Virgo cluster in addition to extensive measurements of integrated physical properties. We create a pipeline to decompose the H$α$ images into individual clumps. We find that the number and average size of clumps in a galaxy are well-defined functions of distance and angular resolution. In particular, the power-law relation between the number of clumps and the distance of a galaxy is consistent with a fractal structure of star forming regions. We formulate an algorithm to compare filament and non-filament galaxies after removing observational differences. Although we do not have any conclusive evidence for a difference in clump size distributions between filament and non-filament galaxies, we do find that filament galaxies have slightly more peripheral clumps than their non-filament counterparts.

The impact of the symmetry energy on the properties of compact stars is analyzed considering constraints from nuclear physics and astrophysics. A compact star can be a neutron star composed only of nuclear matter or a hybrid star with a quark core. Two typical models (soft and stiff) are considered for the nuclear equation of state, and for the hybrid one, a parameterized first-order phase transition approach, completed with a linear quark matter equation of state, is implemented. We show that the phase transition reduces the tension between GW170817 and NICER observations, and we illustrate the impact of the symmetry energy for the understanding of the nature of the binary system in GW170817. We also confirm our previous findings that the GW170817 waveform is best described as a binary HS with a low-density onset of stiff quark matter. This could also be interpreted as a quarkyonic cross-over.

The detection of likely thermal ultraviolet emission from a few old neutron stars suggests that at least one internal heating mechanism is present in these stars. One proposed mechanism is rotochemical heating, in which the continuous contraction of the neutron star due to its spin-down produces chemical imbalances that induce Urca reactions, and the latter deposit heat in the neutron star core. If the protons in the star are superconducting, their energy gap suppresses the reactions, except in microscopic magnetized regions (such as quantized flux tubes) in which the protons act as if they were normal. Therefore, the strength of the internal magnetic field controls the rate at which reactions proceed and thus affects the thermal evolution of the neutron star. Here, we present the first comprehensive study of the effect of an internal magnetic field in the superconducting interior on rotochemical heating. We simulate the evolution of neutron stars for different internal magnetic field strengths and neutron energy gaps, comparing the results to Hubble Space Telescope observations of old neutron stars. All the observational data can be accounted for if the proton energy gap is large ($\sim 1.5\,\mathrm{MeV}$) and the neutron energy gap is small ($\lesssim 0.1\,\mathrm{MeV}$) or vanishing, while the millisecond pulsar PSR~J0437$-$4715 needs to have a very weak internal magnetic field. Our results suggest that neutron-star cores are characterized by a large proton pairing gap and a small or vanishing neutron gap, and that millisecond pulsars have very weak internal magnetic fields. Under these conditions, rotochemical heating alone can account for the observed thermal emission of old neutron stars.

We show that Schwarzschild primordial black holes (PBHs) formed in the radiation-dominated era can grow extremely rapidly through $\textit{radiative absorption}$ governed by the full Stefan-Boltzmann law. By introducing a principle of isonomy - ensuring identical particle species dependence for Hawking emission and absorption - we find that, whenever the temperature of the PBH environment is larger than the PBH horizon temperature, PBHs generically gain mass. In particular, for PBH masses following the critical collapse mass-scaling law with critical exponent $γ_\mathrm{crit}$, with $γ_\mathrm{crit} \in (0.33, 0.49)$, the aforementioned radiative absorption mass growth mechanism produces a striking effect: PBHs forming with a mass $10^6M_\odot$ during BBN can reach $\mathcal{O}(10^{10} M_\odot)$ within $\mathcal{O}(10^{6} \mathrm{s})$ ($\sim $ 58 days). Interestingly enough, small deviations from $γ_\mathrm{crit}$, depending itself on the number of relativistic species present in the primordial plasma, yield a continuous PBH mass spectrum providing us ultimately with a single, Standard-Model-based explanation for the origin of stellar-mass, intermediate-mass, and supermassive black holes (SMBHs), and naturally accounting for the early appearance of SMBHs. The Schwarzschild treatment presented here can be extended to spherically symmetric cosmological black holes, indicating that radiative absorption is a dominant and previously overlooked PBH growth channel in the early Universe.

We investigate how the orbital evolution and mass distribution of infalling satellite galaxies shape the phase-space and radial distributions of intracluster light (ICL) relative to the underlying cluster dark matter (DM) halo. Using N-body simulations, we follow the tidal stripping and orbital evolution of satellite galaxies as they are accreted into a live cluster halo, systematically varying satellite-to-host mass ratio and orbital circularity. We measure the specific orbital energy and angular momentum of stripped stellar and DM material, finding that the stripped stars consistently occupy lower-energy and lower-angular momentum regions of phase-space than the stripped DM. The magnitude of this difference increases strongly towards more equal satellite--to--host mass ratios, while the dependence on orbital circularity is weak. We construct a predictive model for the phase-space properties of stripped stars and DM from a whole infalling satellite population and find that the resulting phase-space difference between the components are driven primarily by the characteristic mass of the infalling satellite stellar mass function. We find that the ICL is always more centrally concentrated than the DM. The magnitude of this offset depends on the characteristic mass and increases towards higher characteristic masses. Comparisons with four independent cosmological hydrodynamical simulations show that, once the infalling satellite stellar mass function is matched, the model reproduces the radial stellar-to-DM density profile offsets to better than the inter-simulation scatter. This demonstrates that the radial relationship between the ICL and the DM distribution is largely governed by satellite demographics. With adequate constraints on the infalling satellite population, ICL density profiles can therefore be used as informative tracers of the underlying radial DM distribution in clusters.

We recently used a large set of Monte Carlo simulations of globular clusters (GCs) to define new fully empirical parameters (named A5, P5, and S2.5) able to trace the internal dynamical evolution of dense stellar systems. These parameters are specifically designed to quantify the steepness of the cumulative radial distribution of stars in the innermost region of the host system, which tends to progressively increase with dynamical aging due to core contraction. Following the original definitions, here we measure A5 and P5 in a sample of 40 Galactic GCs homogeneously surveyed through HST photometric observations. In agreement with the predictions of our simulations, the largest values of A5 and P5 are found for the most dynamically evolved GCs, i.e., those previously classified as post-core collapse systems based on the shape of their density profile, and those characterized by the shortest central relaxation times. Moreover, the new dynamical parameters here measured strongly correlate with A+rh, another fully empirical, independent parameter that traces the dynamical age of star clusters through the level of central segregation of blue straggler stars.

Galaxy clusters are the largest virialized structures in the Universe and are predominantly dominated by dark matter. The hydrostatic mass and the mass obtained from gravitational lensing measurements generally differ, a discrepancy known as the hydrostatic mass bias. In this work, we derive the hydrostatic mass of galaxy clusters within the framework of Rastall gravity and investigate its implications under two scenarios: (i) the absence of dark matter and (ii) the existence of dark matter. In the first scenario, Rastall gravity effectively reduces the hydrostatic mass, bringing it closer to the observed baryonic mass. The best linear fit yields a slope $\mathbf{M}=1.07\pm0.11$, indicating a near one-to-one correspondence between the two masses. In the second scenario, Rastall gravity helps to alleviate the hydrostatic mass bias. The linear fit between the Rastall hydrostatic mass and the observed lensing mass results in a best-fit slope $\mathbf{M}=1.01\pm0.16$, which is very close to unity. These results suggest that Rastall gravity provides a statistically favorable framework for addressing mass discrepancies in galaxy clusters.

Energetic particles interact with the plasma surrounding them, resonating with certain plasma waves to stabilize them while destabilizing others, and changing the character of the background turbulence in ways that have not been fully quantified or understood. Interaction with the turbulent background plasma is key to the acceleration of many types of energetic particles including high-energy cosmic rays, solar energetic particles, and pick-up ions. This is a process that would ideally be described by a kinetic model, a type of model that follows a probability distribution function (PDF) for all particles in 7-dimensional space. Because of the high dimensionality of a kinetic model, such simulations use the largest computational resources available, and are yet unable to simulate a realistic number of particles, reach the large scales necessary for astrophysical problems, or use high-precision numerical methods. Two available alternatives to kinetic plasma models have been explored: a multi-fluid model, and a hybrid fluid/Fokker-Planck model. These methods are hampered by the physical modeling of the coupling. We develop a new model, which follows the PDF for all particles; this can be viewed as a step toward physical realism above a multi-fluid MHD model, while also being more computationally efficient than a kinetic model. The equations we develop model both the background plasma and the energetic particles self-consistently. Over the last decade, similar PDF methods have been developed to a high level of sophistication to model reactive flows and turbulent combustion for engineering applications. For treatment of the feedback of the energetic particles on a background plasma, a PDF closure approach should evaluate the mean characteristics, including the density, with better statistical quality than will particle-sampling procedures.

The revision of the fourth Fermi Large Area Telescope (LAT) catalog of gamma-ray point sources (rev4FGL) revealed that the gamma-ray sky is populated by emerging populations of jetted active galactic nuclei (AGN) other than blazars and radio galaxies. Narrow-Line Seyfert 1, Seyfert 1, intermediate, and Seyfert 2 galaxies, changing-look AGN, plus a number of ambiguous or unclassified sources. After a short historical introduction on the gamma-ray observations of Seyfert-type AGN, I explore the main statistical properties of 1477 jetted AGN from the rev4FGL with spectroscopic redshift, and also the cross-match with Very Large Baseline Array (VLBA) radio observations at 15~GHz from the Monitoring Of Jets in Active galactic nuclei with VLBA Experiments (MOJAVE) program. I then discuss the difference between gamma and non-gamma jetted AGN, and the implications on the classification.

Methanol (CH$_{3}$OH) is a key complex organic molecule (COM) in the interstellar medium, widely used as a tracer of dense gas and hot molecular cores (HMCs). Using high-resolution ALMA observations from the ATOMS survey, we investigate the excitation and abundance of methanol nuclear spin isomers and their relationship to chemical complexity in massive star-forming cores. We identify 20 methanol transitions, including A- and E-type lines in the v=0 state and E-type lines in the v$_{t}$=1 state, and detect 94 HMC candidates. Rotational temperature analysis under the LTE assumption yields average values of 194 $\pm$ 33 K for CH$_{3}$OH-E v$_{t}$=1, 178 $\pm$ 33 K for CH$_{3}$OH-A v=0, and 75 $\pm$ 33K for CH$_{3}$OH-E v=0. Emission from COMs other than methanol is detected in 87 of the 94 cores, with the CH$_{3}$OH-E v$_{t}$=1 line intensity showing a strong correlation with the channel detection ratio (CDR). These results demonstrate that CH$_{3}$OH-E v$_{t}$=1 lines are reliable tracers of HMCs and chemical complexity, and that the CDR provides a robust indicator of molecular richness. The temperature difference between A- and E-type methanol transitions is driven by anomalously strong J(2,J-2)$-$J(-1,J-1) lines, highlighting the importance of analyzing methanol symmetry types separately.

Cross-correlations between 21cm observations and galaxy surveys provide a powerful probe of reionization by reducing foreground sensitivity while linking ionization morphology to galaxies. We quantify the constraining power of 21cm-Galaxy cross-power spectra for inferring neutral hydrogen fraction $x_\mathrm{HI}(z)$ and mean overdensity $\langle 1+δ_\mathrm{HI} \rangle(z)$, exploring dependence on field of view, redshift precision $σ_z$, and minimum halo mass $M_\mathrm{h,min}$. We employ our simulation-based inference framework EoRFlow for likelihood-free parameter estimation. Mock observations include thermal noise for 100h SKA-Low with foreground avoidance and realistic galaxy survey effects. For a fiducial survey ($\mathrm{FOV}=100\,\mathrm{deg}^2$, $σ_z=0.001$, $M_\mathrm{h,min}=10^{11}\mathrm{M}_\odot$), cross-power spectra yield unbiased constraints with posterior volumes (PV) of $\sim$10% relative to priors. Cross-power measurements reduce PV by 20-30% versus 21cm auto-power alone. With foreground avoidance, spectroscopic redshift precision is essential; photometric redshifts render cross-correlations uninformative. Notably, cross-power spectra constrain ionizing source properties, the escape fraction $f_\mathrm{esc}$ and star formation efficiency $f_*$, which remain degenerate in auto-power (PV $>$60%). Tight constraints require either deep surveys detecting faint galaxies ($M_\mathrm{h,min} \sim 10^{10}\mathrm{M}_\odot$) with moderate foregrounds, or conservative mass limits with optimistic foreground removal (PV $<$15%). 21cm-Galaxy cross-correlations enhance morphology constraints beyond auto-power while enabling previously inaccessible source property constraints. Realizing full potential requires precise redshifts and either faint galaxy detection limits or improved 21cm foreground cleaning.

Black holes are the simplest possible objects, characterised by only mass and spin. We see them via accretion, so there is one more fundamental parameter which is the mass accretion rate. Here I will review how the data from both stellar and supermassive black holes can be fit into a framework where there is a major spectral transition at $\dot{m}=L/L_{\rm Edd}\sim 0.01$ where the optically thick disc is replaced by a hot flow. This dramatic spectral change also affects the expected properties of thermal and radiatively powered winds, matching the overall properties of winds seen in new XRISM data from the stellar mass binaries, though there can also be additional UV and dust driven winds in supermassive black holes. The radio data in stellar and supermassive black holes are clear that the hot flow (not the disc) connects to the radio jet, and the radio-X-ray 'fundamental plane' can be qualitatively understood if the radio quiet AGN and stellar mass black holes have low to moderate spins, with the jet power set as a constant fraction of the accretion power. A small fraction of AGN (radio loud) instead have much higher (factor $100-1000\times$) radio-to-X-ray ratio at the same black hole mass and mass accretion rates. I speculate that these have higher jet power due to high black hole spin. I review the multiple issues still remaining in this picture, most of which are connected to the geometry and nature of the X-ray corona, and the conflicting constraints on this which come from reflection spectroscopy and polarimetry.

Bow-shock pulsar wind nebulae are valuable sources to investigate the dynamics of relativistic pulsar winds and the mechanisms by which they are converted into cosmic-ray leptons at the highest energies. The Lighthouse Nebula is one such object, famous for the high velocity of its pulsar and a long misaligned X-ray jet that is understood as a specific escape channel for the most energetic particles. We aim to get a better understanding of how the bulk of non-thermal particles are released into the interstellar medium. We focus on GHz radio observations, which probe lower-energy particles that are dominant in number and long-lived, thus offering a picture of how escape proceeds in the long run. We analyze 10.5h of MeerKAT observations in the 0.9-1.7GHz band. MeerKAT observations reveal a highly structured synchrotron nebula downstream of pulsar PSR J1101-6101. A cometary tail is detected up to beyond 5pc from the pulsar, while a system of multiple transverse two-sided emission streaks is observed for the first time. No radio counterpart of the misaligned X-ray jet is seen. The radio streaks are interpreted as the occasional charge-independent release of energetic leptons from the tail into the surrounding medium, as a result of dynamical instabilities and reconfiguration in the downstream flow. The intensity layout suggests that most of the particle content of the nebula is discharged into the ambient medium within several parsec. Once escaped, particles light up the ambient magnetic field, which appears to have a coherence length of at least a few parsec. The length and persistence of the streaks indicate a low level of magnetic turbulence, possibly slightly enhanced with respect to average cosmic-ray transport conditions in the Galaxy. Such a confinement may result from self-generated turbulence by resonant streaming instability, or be due to past activity of the progenitor star.

The nature of Dark Matter (DM) is one of the most outstanding mysteries of modern astrophysics. While the standard Cold DM (CDM) model successfully explains observations on most astrophysical scales, DM particles have not yet been detected, leaving room for a plethora of different models. In order to identify their observable signatures, we use the AIDA-TNG cosmological simulation suite to predict the distributions of gas and neutral hydrogen (HI) in the CDM, Self-Interacting DM (SIDM), velocity-dependent SIDM (vSIDM), and Warm DM (WDM) models. We find that the DM models investigated have very limited impact on the median gas and HI profile of haloes. In particular, for the most massive haloes ($M_{\rm vir}\sim10^{14}\,\mathrm{M}_\odot$), we find that DM self-interactions can shallow the central potential and thereby enhance gas cooling. We find that, in all models, the halo-to-halo variation in the HI profiles is explained by AGN feedback, and that the specific characteristics of DM model is largely subdominant. Nevertheless, we detect some systematic difference in the case of SIDM, with more HI surviving close to the centre with respect to other models. We provide fitting functions for the gas and HI profiles. We investigate the galaxy-Ly$α$ cross-correlation function (\galacc) for different halo masses, redshift and observation strategies. We find that at $z=0$ vSIDM can be distinguished from CDM in haloes with $10^{12}\lesssim M_{\rm vir}\lesssim10^{13}\,{\rm M}_\odot$, while SIDM1 can be distinguished from CDM in haloes with $M_{\rm vir}\gtrsim10^{13}\,{\rm M}_\odot$. We estimate that statistically-robust detection requires sampling $\sim160$ haloes with $\sim20$ sightlines each, a task that can be achieved with current and future facilities like WEAVE, 4MOST, PFS, ELT and WST.

The assembly history of the Galactic bulge is intimately tied to the formation of the proto-Milky Way, yet reconstructing this early phase is difficult because mergers and secular evolution have erased most of its original structure. Among present-day stellar systems, only globular clusters retain the ancient signatures needed to trace these primordial building blocks. Here we present the most detailed characterization to date of Tonantzintla 2, a prime candidate for a relic of the Milky Way's primordial bulge. It is a moderately metal-rich globular cluster projected onto the bulge that has remained largely unexplored despite its potential to constrain the early formation of the inner Milky Way. We derive its fundamental parameters using proper motion-corrected Hubble Space Telescope WFC3 and ACS photometry. By applying an isochrone fitting to very clean data, we obtain an age of 13.58 Gyr, a reddening E(B-V) = 1.44, a metallicity [M/H]=-0.68, and a heliocentric distance of d = 7.38 kpc. A complementary chemical-abundance analysis of seven member stars from APOGEE high-resolution spectroscopy reveals an enrichment pattern consistent with an in-situ origin. Tonantzintla 2 is among the oldest globular clusters studied in the literature, and the oldest so far analyzed in the Galactic bulge. Its age places a stringent constraint on the onset of the bulge formation, implying that star formation in the inner Galaxy began within ~0.2 Gyr of the Big Bang and that Tonantzintla 2 represents an exceptional relic of the Milky Way's earliest chemical enrichment.

Previous VLBI kinematic studies of the blazar 3C 66A have unveiled complex jet kinematic behaviors. Using follow-up high-resolution VLBI observations and archival data, we investigate the morphology and the variations in orientation and core flux density of the 3C 66A jet to gain a deeper insights into its kinematic behavior and physical origins. We performed KVN and VERA array (KaVA) observations at 22/43 GHz over three epochs in 2014 and collected 109 sets of Very Long Baseline Array (VLBA) archival data at 43 GHz between 1996 - 2025. We imaged the parsec-scale jet and parameterized it using circular Gaussian fittings to the UV visibilities. Finally, we derived the inner jet PA and the core flux densities for the VLBA data. The jet presents a twisted morphology in the KaVA maps. The PA of the fitted Gaussian components is in the range between 170 deg and 195 deg. Our kinematic analysis using the VLBA data indicates that the PA oscillates with an amplitude of 7.77 pm 0.79 deg and a period of 10.94 pm 0.22 years, presented for the first time in this work. This oscillation is topped by a continuous clockwise shift of the PA by -0.83 pm 0.07 deg/year. We also identified a strong core flux variability with possible periodicity and a 2 sigma correlation between the core flux density and the inner jet PA change. We discuss possible physical models that could explain the observed features for this object; in particular, a supermassive black hole binary (SMBHB) system, Lense Thirring (LT) effect, and jet or disk instabilities. The oscillation and continuous shift of the PA and the possible radio flux periodicity, together with the optical flux periodicity of approximately 2 years that had previously been confirmed in several independent studies, favor a jet precession scenario driven by orbital motion and disk-orbit misalignment in a SMBHB system.

The decay of magnetically dominated turbulence exhibits robust inverse transfer of magnetic energy even in the absence of net magnetic helicity, challenging traditional cascade-based phenomenology. While recent studies suggest that magnetic reconnection governs the evolution of such systems, a comprehensive understanding has been lacking. Here we test a reconnection-mediated model for decaying magnetic turbulence in two-dimensional (strict-2D), 2.5D, and three-dimensional (3D) systems with both helical and nonhelical initial conditions. We show that the magnetic-energy decay timescale scales with the Lundquist number in a manner consistent with Sweet-Parker-type reconnection rather than Alfvenic or purely resistive timescales. We develop a broken power-law model for the magnetic energy spectra and provide analytic predictions for the temporal evolution of energy across both sub-inertial and inertial ranges, which are confirmed by high-resolution simulations. In nonhelical turbulence, these results favor anastrophy as the dominant constraint over helicity fluctuations. Using Minkowski functionals to analyze reconnecting current sheets in real space, we find that the structures controlling the decay are substantially smaller than the global magnetic correlation scale, implying local Lundquist numbers well below the system-scale value. This explains the weak sensitivity of global decay laws to current-sheet resolution and that the current-sheet aspect ratios converge toward Sweet-Parker predictions only at sufficiently high resolution. Together, these results establish magnetic reconnection as the organizing principle underlying inverse transfer, spectral evolution, and decay in magnetically dominated turbulence, providing a unified picture applicable across dimensionality and helicity regimes with direct implications for astrophysical plasmas.

The energy released by active galactic nuclei (AGNs) is considered to have a profound impact on the cold gas properties of their host galaxies, potentially heating or removing the gas and further suppressing star formation. To understand the feedback from AGN radio activity, we investigate its impacts on the cold gas reservoirs in AGNs with different radio activity levels. We construct a quasar sample with a mean $z\sim1.5$ and a mean $L_{\rm bol}\sim10^{45.8}\ \rm erg\ s^{-1}$, all with Herschel detections to enable estimates of the total gas mass through the galactic dust continuum emission. The sample is then cross-matched with radio catalogs and divided into radio loud (RL) quasars, radio-detected radio quiet (RQ) quasars and radio-undetected quasars based on their radio loudness. Through spectral energy distribution (SED) fitting, we find the radio-detected RQ quasars exhibit evidence of gas deficiency with host galaxies possessing $\sim 0.3$ dex lower dust and gas masses compared to the other two groups, despite being matched in $M_{\rm BH}$, $L_{\rm bol}$, $M_{*}$ and SFR. Furthermore, evidence from optical spectra shows that both the fraction and velocity of outflows are higher in the radio-detected RQ group, suggesting a connection between the ionized gas outflows and the moderate radio activity. These results suggest that the AGN feedback could be more efficient in AGNs with weak/moderate radio emission than in those without radio detection or those with strong radio emission. Further high-resolution observations are needed to understand the interaction between the interstellar medium and the weak/moderate AGN radio activity.

Third-order statistics provide information beyond two-point measures, but extracting this information requires accurate and consistent modelling. We measure and detect the three-point correlation function and third-order aperture-mass statistics of intrinsic alignments (IA) for galaxies and for haloes with $M_{\rm halo} > 10^{13}\,{\rm M}_\odot$ in the $(2.8\,\mathrm{Gpc})^3$ simulation volume of the FLAMINGO hydrodynamical simulation suite. We model the third-order aperture-mass statistics and show that on large scales both the galaxy and halo samples are well described by the tree-level effective field theory (EFT) of IA across the three dark matter density-shape combinations and a wide range of triangle configurations, with the alignment amplitude consistent with that inferred from two-point statistics. We compare the full EFT to several other models: a version neglecting the velocity-shear term, the non-linear alignment model (NLA), and to a reduced EFT assuming co-evolution relations that follow from the assumption that alignment is linear in Lagrangian space. The first two models yield biased constraints on the alignment amplitude, but the reduced EFT performs remarkably well, achieving a low reduced chi-squared and minimal bias. We examine the redshift and mass dependence of the higher-order bias parameters, finding that the linear Lagrangian bias assumption is approximately satisfied across the explored halo mass and redshift ranges for both galaxies and haloes, suggesting that the galaxies broadly follow the alignment properties of their host haloes. These co-evolution relations can be valuable for photometric shear surveys, where limited constraining power on IA parameters favours models with fewer free parameters.

With increasing sensitivity of the gravitational wave (GW) detectors, we expect a significant rise in the detectable GW events. To process, analyse and identify such large amounts of GW signals arising from mergers of Binary Black Holes (BBH), we need both speed and accuracy. In the search for (massive) BBH signals, the biggest hurdle is posed by the various non-gaussian noise transients called glitches. Compared to our previous work, which used a simple convolutional neural network to distinguish BBHs from Blip glitches, this work uses transfer learning with InceptionNetV3 to distinguish BBHs from six types of most popular glitches from the third observing run of LIGO. While the glitches are real and identified via GravitySpy, the BBH signals are simulated and then injected into the real detector noise for each of the two LIGO detectors. We generate Sine-Gaussian Projection (SGP) maps by cross-correlating data with Sine-Gaussian functions of varied quality factors ($Q$) and central frequencies ($f_0$) and projected on the $Q$ - $f_0$ plane. We find that SGP maps make it easier to distinguish BBHs from glitches that look very similar to BBHs in the Time-Frequency maps like the Blips, while also maintaining significant morphological differences between BBHs and the more frequent glitches - Scattered Light and Fast Scattering. Our network has an accuracy of $87%$, a TPR of 0.83 for an FPR of 0.1 on our test dataset. It is also robust, retaining its level of accuracy, when tested on real BBH events identified in the first three observing runs of LIGO. Our proposed method shows the viability of using the SGP maps and neural networks for fast identification of GW events improving the efficiency of standard search pipelines.

We present forecasts for the $E_G$ statistic using redshift distributions of realistic mock galaxy samples from the upcoming Chinese Space Station Survey Telescope (CSST). The dominant uncertainty in $E_G$ stems from the redshift space distortion parameter $β$, whose precision limits the overall constraining power. Our analysis shows that CSST will nevertheless achieve $E_G$ constraints at the few-percent level (3%-9%) over $0 < z < 1.2$, an improvement by a factor of several to an order of magnitude over current observations. Within the $μ-Σ$ modified gravity framework, the parameter $Σ_0$, associated with the effective gravitational constant of the Weyl potential, can be constrained to $\sim 5\%$ precision. In a plausible scenario where upcoming spectroscopic surveys determine $β$ to 1\% accuracy, $E_G$ constraints tighten to the percent level, and $Σ_0$ becomes measurable at $\sim 1\%$. These results demonstrate that CSST will serve as a powerful facility for testing gravity and underscore the essential synergy between photometric weak lensing and spectroscopic surveys in probing cosmic acceleration.

The origin mechanism of the cosmic-ray knee region remains an unresolved mystery, with acceleration, interaction, and propagation models drawing significant attention. The latest experimental observations of the PeV total spectrum, composition energy spectrum, and anisotropy-particularly the precise measurements of the proton spectrum by the LHAASO experiment-have provided crucial breakthroughs in uncovering its origin. Based on the latest LHAASO measurements of the proton energy spectrum, combined with cosmic-ray spectral and anisotropy data, this study proposes that the spectral index variation in the knee region arises from changes in the propagation coefficient. By introducing a knee position $\rm \mathcal{R}_{knee}$ and an index variation $\rm δ_{knee}$, we construct a rigidity-dependent double-power-law diffusion model to reproduce the knee-region spectral structure. Through modifications to the diffusion coefficient, we successfully replicate the observed knee-region spectral structure in the LHAASO proton spectrum and calculate the corresponding anisotropy. Under current data and model dependencies, a joint analysis of the energy spectrum and anisotropy does not support the propagation origin model of the cosmic-ray knee at a 95\% confidence level. We hope that future LHAASO experiments will provide precise measurements of the energy spectra and anisotropies of various nuclei in the knee region, thereby offering a definitive test of the propagation model as the origin mechanism of the knee-region spectral structure.

This study characterizes the physical and kinematic properties within the innermost 500 au region of the L1527 bipolar outflow, a Class 0/I low-mass protostar using JWST MIRI/MRS spectroscopy across 5-28 micron at 0.2-1.0 arcsec resolution. We identify emission lines from molecular and ionized species and analyze their spatial morphology using line integrated intensity maps. We derive gas temperature and column density through excitation diagram analysis of H2 rotational lines and compared results with shock models. The observations reveal extended molecular hydrogen emission tracing the bipolar outflow, with the H2 gas temperatures distributed into warm (~550 K) and hot (~2500 K) components, likely originating from moderate velocity J-type shocks and some UV irradiation. We detect forbidden atomic and ionized emission lines of [Ni ii], [Ar ii], [Ne ii], [Ne iii], [S i], and [Fe ii] showing spatially extended morphology. Double peaked emission profiles were seen in [Ar ii], [Ne iii], and [Fe ii], in the eastern region, suggesting that the high velocity component traces a fast, highly ionized jet. Radial velocity map derived from [Ne ii] emission shows the eastern region to be redshifted and the western region blueshifted, contrary to earlier interpretations. The analysis of the MIRI/MRS observations reveals the presence of molecular, atomic, and ionized emission lines in this low-mass protostar connected with active outflow signatures. The most striking feature discovered is the presence of a poorly collimated high velocity ionized jet, embedded within a broader wide-angle molecular outflow likely driven by a disk wind. The co-existence of these components supports a stratified outflow structure and suggest L1527 exhibits unique jet-launching characteristics atypical for its early evolutionary stage.

The Fe K$α$ fluorescence line at 6.4 keV is a powerful probe of cold matter surrounding X-ray sources and has been widely used in various astrophysical contexts. The X-ray microcalorimeter spectrometer onboard XRISM can measure line shifts with unprecedented precision of $\sim$0.2 eV, equivalent to a line-of-sight velocity of $\sim$10 km s$^{-1}$. At this level of accuracy, however, several factors that influence the line energy must be carefully considered prior to astrophysical interpretation. One such important factor is the ionization degree, Fe$^{q+}$. The K$α$ line shifts redward by $\sim$4 eV as $q$ increases from 0 (neutral) to 8 (Ar-like). Additionally, the accompanying Fe K$β$ line at 7.06 keV shifts blueward by $\sim$30 eV from $q=0$ to 8. We demonstrate that this effect is actually observable in the XRISM data of the high-mass X-ray binary Centaurus X-3 (Cen X-3). We advocate that the differential energy shift between the K$α$ and K$β$ line provides a robust estimate of $q$ by decoupling from other effects that shift the two lines in the same direction. We derived $q \sim 5$ (Sc-like) for the fluorescing matter by comparing the observation with atomic structure calculations of our own and in the literature. By accounting for the derived charge state and the corresponding shift in the rest-frame line energy, we made corrections for this effect and reached a consistent residual shift among the K$α$, K$β$, and the optical measurement attributable to the systemic velocity of the system. Consequently, we obtained a new constraint on the location of the cold matter. This ionization effect needs to be assessed in all use cases of the Fe K$α$ line shift beyond Cen X-3, and the proposed metric is generally applicable to all of them.

We report the detection of Lyman continuum (LyC) photons from three massive ($\text{M}_{*}>10^{10}\:\text{M}_{\odot}$) spiral galaxies at a redshift of nearly 1 in the AstroSat UV Deep Field South. Notably, all three systems are viewed at low inclination (i.e., nearly face-on), prompting an investigation into the role of galaxy orientation in the detectability of LyC emission from disk systems. Two of the three galaxies, however, host active galactic nuclei (AGNs), adding complexity to the interpretation of the LyC signal. We present a detailed analysis of the likely star-forming case, and report tentative evidence that a face-on viewing angle may enhance the likelihood of LyC detection in disk galaxies. This represents the first detection of LyC emission from well-characterized spiral galaxies at high redshift, offering a new window into LyC escape mechanisms in such systems. Our findings highlight the need to consider geometric factors and anisotropic escape pathways facilitated by feedback processes alongside more traditional density-bounded scenarios that imply isotropic escape.

Using a sample of $\sim$126,000 late-type galaxies (LTGs) from SDSS, we analyzed stellar mass as a function of dynamical mass. Stellar masses were estimated using eight SPS models with constant IMFs, while dynamical masses were derived from seven formulations based on Newtonian dynamics and virial equilibrium, incorporating both stellar and gas velocity dispersions. We account for key factors affecting dynamical mass estimation, including inclination, colour, concentration, and Sérsic index. We find that the difference between dynamical and stellar mass ($Δ\log \mathbf{M}$) ranges from nearly zero to $\sim$95% of the dynamical mass, depending on mass and redshift. $Δ\log \mathbf{M}$ appears to decreases with increasing redshift, but exhibits a saddle-like shape at low mass and low redshift-especially in disk-dominated LTGs-transitioning into a steep, linear trend at higher masses and redshifts. In the high-mass regime, the behavior resembles that of early-type galaxies. Moreover, our results indicate that this evolution is not discrete but follows a continuous transition between morphological regimes. Dark matter within LTGs is at most equal to $Δ\log \mathbf{M}$, depending on the impact of the IMF and SPS on stellar mass estimation. Although SPS-based stellar masses do not include the gas component, previous studies have shown that galaxies with log($\mathbf{M_{Stellar}/M_{Solar}}) > 10$ at $z \leq 0.3$ are predominantly stellar-mass dominated. Most galaxies in our sample fall within this regime, minimizing the impact of gas exclusion. Our findings go beyond the scope of single galaxies, providing insight into the nearby Universe and highlighting the influence of dark matter in determining the Universe's structure and evolution.

Dark matter (DM) halos in $Λ$ Cold DM cosmological simulations are triaxial. Most exhibit figure rotation. We study 40 isolated halos with stellar disks from the TNG50 simulation suite across $\sim 4$~Gyr to understand whether and how a triaxial halo's tumbling and orientation relative to the disk can drive warps. We measure a warp angle $ψ$ and find even our isolated disks are all at least slightly warped, with each galaxy's maximum $ψ> 1.8^{\circ}$. We perform a modified cross-correlation analysis between $ψ$ and the figure rotation pattern speed, as well as the misalignment between the disk spin axis and (a) the figure rotation axis, (b) the halo minor axis, and (c) the gas angular momentum axis. We use snapshots spanning a lookback time $t_{lb} ~4$ Gyr with 25 linearly-spaced lags from $ 0 - 2.33$ Gyr. We do not find evidence for a consistent lag between the onset of a warp and any of the aforementioned factors on the population level. However, we find significant correlations between individual time-series at various lags. These maximum correlation coefficients were significantly offset from random chance at the population level, suggesting that several of these factors do correlate with disk warping in specific situations. By examining four case studies whose maximum correlation coefficients were significantly higher than random chance, we establish clear qualitative relationships between these factors and warps. While a non-warped galaxy typically shows minimal halo tilt and figure rotation, warped galaxies can have strong/weak tilts and/or strong/weak figure rotation. Keywords: Disk galaxies(391), Galaxy dynamics(591), Hydrodynamical simulations(767), Galaxy DM halos(1880)

(Abridged) Recent JWST observations of Cassiopeia A (Cas A) reveal unprecedented ejecta substructure, including a web of filaments and the enigmatic "Green Monster" (GM), characterized by nearly circular holes and rings. These features provide new constraints on supernova (SN) explosion physics and ejecta-circumstellar medium (CSM) interactions. We present high-resolution three-dimensional hydrodynamic and magnetohydrodynamic simulations of a neutrino-driven SN explosion tailored to Cas A, following the system from core collapse to an age of $\sim 1000$ yr. The models include key physical processes such as hydrodynamic instabilities, Ni-bubble effects, radiative cooling, non-equilibrium ionization, and electron-ion temperature equilibration. Our results show that the filamentary ejecta network naturally forms during the early explosion due to the interaction of neutrino-driven bubbles and instabilities, retaining a memory of the initial conditions before being progressively modified by the reverse shock. The GM morphology is reproduced by the interaction of dense ejecta clumps with an asymmetric, forward-shocked CSM shell, with radiative cooling enhancing fragmentation and generating the observed holes and rings. Overall, our study demonstrates that Cas A's complex morphology reflects both the imprint of the explosion mechanism and subsequent ejecta-CSM interactions.

We report the discovery of an extremely metal-poor galaxy at a redshift of z = 3.654, identified through infrared spectroscopy using the James Webb Space Telescope (JWST). This galaxy, CAPERS-39810, exhibits a metallicity of 12 + log(O/H) = $6.73\pm0.13$, indicative of its primitive chemical composition, resembling the early stages of galaxy formation in the Universe. We use JWST NIRSpec/MSA for spectroscopic analysis, complemented by photometric data from the COSMOS2025 catalog. Our analysis employs the R3 strong-line diagnostic method to estimate metallicity, due to the lack of auroral lines in the spectrum. The galaxy's emission lines, including Hb, [O III], Ha and He I, are clearly detected. The rest-frame equivalent widths of the strong hydrogen recombination lines are EW_0(Hb) = $184\pm48$ Åand EW_0(Ha) = $1144\pm48$ Å. Furthermore, we perform detailed spectral energy distribution modeling to derive a galaxy logarithmic stellar mass of $8.02^{+0.22}_{-0.34}$ $M_\odot$. This discovery adds to the growing body of evidence for the existence of very low-metallicity galaxies existed at cosmic noon of $z\approx3$, which are crucial for understanding the processes of chemical enrichment and star formation in young galaxies at the cosmic noon.

Scenarios such as the QCD axion with the Peccei-Quinn symmetry broken after inflation predict an enhanced matter power spectrum on sub-parsec scales. These theories lead to the formation of dense dark matter structures known as minihalos, which provide insights into early Universe dynamics and have implications for direct detection experiments. We examine the mass loss of minihalos during stellar encounters, building on previous studies that derived formulas for mass loss and performed N-body simulations. We propose a new formula for the mass loss that accounts for changes in the minihalo profile after disruption by a passing star. We also investigate the mass loss for multiple stellar encounters. We demonstrate that accurately assessing the mass loss in minihalos due to multiple stellar encounters necessitates considering the alterations in the minihalo's binding energy after each encounter, as overlooking this aspect results in a substantial underestimation of the mass loss. We further extend our analysis to the Galactic environment by more accurately incorporating multiple stellar encounters and dynamical relaxation timescales, simulating minihalo orbits in the Galactic potential. Our results show stellar interactions are more destructive than previously estimated, reducing minihalo mass retention at the solar system to ~30%, compared to earlier estimates of ~60%. This enhanced loss arises from cumulative energy injections when relaxation periods between stellar encounters are accounted for. The altered minihalo mass function implies a larger fraction of axion dark matter occupies inter-minihalo space, potentially increasing the local axion density and improving haloscope detection prospects. This thesis highlights the significance of detailed modeling of stellar disruptions in shaping the axion dark matter distribution.

We report on optical spectroscopic and photometric follow-up observations of the eROSITA discovered transient SRGt 062340.2-265751 and show that it displays the characteristics of a nova-like cataclysmic variable (CV), with possible indications of being a magnetic system. We try to put better constraints on the classification of SRGt 062340.2-265751 using optical time-resolved spectroscopic and photometric observations to find any periodicities in the system. From these periodicities we can classify the CV sub-type that it belongs to. Spectroscopic observations revealed a very low amplitude, K $\sim$ 14 km s$^{-1}$, in the radial velocity of the H$β$ and H$γ$ emission lines, suggesting that the system is likely observed at a low inclination angle. High-speed photometric observations revealed highly stochastic variability, characteristic of many magnetic cataclysmic variable systems. A probable 3.645 $\pm$ 0.006 hour orbital period was found by applying Lomb-Scargle period analysis to the H$β$ and H$γ$ emission line radial velocities. A 24.905 $\pm$ 0.065 min period was found from photometric observations, which we associate with the white dwarf spin. However, it was also found that the photometry revealed multiple periodicities from night to night. TESS observations in three sectors did not reveal any of the periodicities found from ground-based observations, but did show a prominent period in only one sector, which might be attributed to a positive superhump period. These multiple periodicities as well as the HeII $λ$4686 and Bowen blend emission lines seen in the spectra indicate that SRGt 062340.2-265751 is likely a nova-like CV, and might belong to the VY Scl sub-type.

Despite its proximity, the mass of the supermassive black hole (SMBH) in the spiral galaxy M81 (NGC~3031) has remained uncertain, with previous dynamical measurements being unreliable. We present the first robust stellar-dynamical measurement of its mass using high-resolution, two-dimensional kinematics from JWST/NIRSpec observations of the central $3''\times3''$. By tracing stellar motions in the near-infrared, our data penetrate the obscuring nuclear dust and allow for the separation of stellar light from the non-thermal AGN continuum. We modeled the kinematics using JAM within a Bayesian framework, exploring a comprehensive suite of models that systematically account for uncertainties in the point-spread function, orbital anisotropy, and stellar mass-to-light ratio. This ensemble modeling approach demonstrates that a central dark mass unambiguously drives the central rise in velocity dispersion. The models yield a robust SMBH mass of $M_{\rm BH} = (4.78^{+0.07}_{-0.10})\times10^7$ M$_\odot$. This result resolves a long-standing uncertainty in the mass of M81's black hole and provides a crucial, reliable anchor point for SMBH-galaxy scaling relations.

We present a rest-ultraviolet to infrared spectral energy distribution (SED) analysis of 63 heavily reddened quasars (HRQs) at redshifts z=0.7-2.7 and with dust extinctions E(B-V)=0.4-1.8. Our analysis demonstrates that SEDs with red optical and blue UV continua are very common in HRQs, with more than 82 per cent of the sample showing a UV-excess relative to the reddened quasar continuum. We model the SEDs by combining a reddened quasar and an unobscured scattered light component, though contributions from a star-forming host galaxy cannot be ruled out. The average scattering fraction is small (0.3 per cent). Higher scattering fractions are ruled out by the (i-K)=2.5 colour-cut used to select HRQs which pre-dates the discovery of the JWST "Little Red Dot" (LRD) population. Hence, LRDs generally have bluer UV continua. Nevertheless, four HRQs satisfy the LRD UV/optical continuum slope selections and are therefore massive, cosmic noon analogues of LRDs. Analysis of the near-infrared SEDs of HRQs reveals a deficit of hot dust relative to blue quasars, similar to what is observed in LRDs. This suggests HRQs trace a phase where strong AGN feedback processes eject dust from the inner torus. The UV scattering fraction of HRQs is weakly correlated with the amount of hot dust emission and anti-correlated with the line-of-sight extinction, E(B-V). This is consistent with the hot dust acting as the scattering medium, and the line-of-sight extinction being dominated by dust on interstellar medium scales in the host galaxy.

The Westerlund 1 (Wd 1) is the most massive known young star cluster in the Galaxy, and an extended $γ$-ray source HESS J1646-458 surrounding it has been detected up to 80 TeV in the very high energy, implying that cosmic rays (CRs) are accelerated effectively in the region. However, the dominant radiation process contributing to the $γ$-ray emission is not well constrained. In the present work, we develop a model of CR acceleration at the termination shock in the superbubble inflated by the interaction of the cluster wind from the Wd 1 with the surrounding interstellar medium. We then calculate the flux and radial profile of $γ$ rays produced by the inelastic collisions of the hadronic CRs with the ambient gas. Our results with reasonable parameters can explain well the spectrum and radial profile of the $γ$-ray emission of HESS J1646-458, and consequently the $γ$-ray emission of HESS J1646-458 is likely to be of hadronic origin.

Individual radio pulses from a pulsar are directly linked to the underlying emission processes and the associated magnetic field geometry within its magnetosphere. Thus, single-pulse studies across frequencies can provide crucial insights into the physics of radio emission. Multiple studies have investigated single-pulse correlations in PSR B0329+54 with widely separated discrete frequencies, reporting the broadband nature of pulsar emission. However, understanding the frequency evolution of these correlations has been limited by poor frequency sampling, and the physical origin of these correlations remains unexplored. We present a detailed study of single-pulse correlations in PSR B0329+54 at low radio frequencies using the upgraded Giant Meterwave Radio Telescope (uGMRT), with well-sampled time series spanning 300-1460 MHz. We derived an inverted flux spectrum for this pulsar, with a turnover near 470 MHz. We used flux-calibrated and scintillation-corrected single pulses to study correlations across frequencies. Our results show that maximum correlations consistently occur near the longitude of the central component, with correlation strength exceeding 69\% for all frequency combinations, while outer components exhibit correlations above 46\%. These findings indicate very strong inter-frequency correlations, with no anticorrelations detected. No cross-component correlations were observed; only corresponding components correlate across frequencies. The longitudes of maximum correlation do not coincide with the intensity peaks of the average profile. We also examine how correlations vary with frequency at selected fiducial longitudes. The observations reported in this work favor curvature radiation from relativistic charge bunches in the pulsar plasma; however, reproducing the correlation curves along with spectra remains an open challenge.

We present constraints on quintessence dark energy models using the observational detection of the Integrated Sachs-Wolfe (ISW)--thermal Sunyaev-Zeldovich (tSZ) cross-correlation dataset. Our analysis compares three classes of quintessence dynamics: thawing, tracker, and scaling-freezing with the standard $Λ$CDM cosmology. Through a comprehensive likelihood analysis, we derive best-fit values and 68\% confidence intervals for key cosmological parameters, finding $Ω_{\rm m} = 0.322^{+0.027}_{-0.030}$ and $σ_8 = 0.735^{+0.045}_{-0.035}$ for $Λ$CDM, with deviations in alternative models consistent within $1σ$. For the thawing model, we consider an exponential potential with slope $λ= 0.736^{+0.270}_{-0.227}$, while for the tracker and scaling-freezing models, we use inverse axion-like and double exponential potentials, respectively. Observationally, the tracker model yields $n = 5.651^{+1.625}_{-1.604}$ and $f = 0.258^{+0.149}_{-0.096}$, and the scaling-freezing model gives $λ_1 = 0.405^{+0.293}_{-0.322}$ and $λ_2 = 23.226^{+7.975}_{-7.258}$. The dimensionless tSZ amplitude ($\widetilde{W}^{\rm SZ}$) and cosmic infrared background (CIB) parameters are tightly constrained across all models, providing additional insights into astrophysical foregrounds. Our results demonstrate the effectiveness of ISW--tSZ cross-correlations as a probe of dark energy dynamics, with the Thawing quintessence model yielding the lowest $χ^2_{\rm min}$ among the tested scenarios, and highlight the need for future high-precision measurements to distinguish between quintessence models and $Λ$CDM.

Polarized thermal emission from Galactic dust is the dominant foreground for CMB polarization measurements at high frequencies, with its statistical properties set by the interplay between turbulence and magnetic fields in the multiphase interstellar medium (ISM). Variations in turbulence regime and density-magnetic-field alignment across the warm (WNM), unstable (UNM), and cold (CNM) neutral media should imprint distinct signatures on the power spectra and $EE/BB$ power ratio, yet the relative phase contributions remain poorly constrained. Using high-resolution 3D magnetohydrodynamic simulations of a turbulent multiphase ISM coupled with synthetic dust polarization maps, we quantify phase-dependent turbulence, anisotropy, and alignment properties. We find that the trans-Alfvénic and transonic WNM and UNM are strongly anisotropic, exhibiting tight alignment of density and velocity structures with the local magnetic field. In contrast, the super-Alfvénic and supersonic CNM displays reduced anisotropy and weak alignment. These dynamical differences are reflected in the statistical scaling of fluctuations: the square root of the second-order velocity structure function exhibits a slope near $1/3$ in the WNM, near $1/2$ in the CNM, and intermediate in the UNM. Our synthetic observations reproduce the polarization power spectra measured by Planck. We find that polarization from UNM dust yields spectral indices most consistent with Planck, whereas WNM and CNM dust produce steeper and shallower spectra, respectively. The WNM yields $EE/BB>2$, the UNM gives $EE/BB\sim2$, and the CNM yields $EE/BB\approx1$. These results indicate that UNM dust could be the dominant contributor to the polarized foreground. We present predictions at 150 GHz to improve foreground separation.

This review of the rapid-neutron-capture (i.e. r-) process starts with determining the Solar System r-abundance pattern via first obtaining (and subtracting) the contribution from the slow-neutron capture (s-) process. We emphasize the extensive work in this area by our late colleague Roberto Gallino and continue in an overview, concentrating on attempts to reproduce the solar r-process pattern with historical site-independent approaches, based on nuclear physics far from stability. In a second step we address the existing proposals for astrophysical sites. Among stellar observations we start with available observations of individual events before analyzing low-metallicity stars, which witness r-process contributions in the early Galaxy.We conclude with a comparison of observations and model predictions, focusing on our present ability to identify the responsible individual astrophysical sites by their imprint in Galactic evolution.

Ordinary matter-including particles such as protons and neutrons-accounts for only about one sixth of all matter in the Universe. The rest is dark matter, which does not emit or absorb light but plays a fundamental role in galaxy and structure evolution. Because it interacts only through gravity, one of the most direct probes is weak gravitational lensing: the deflection of light from distant galaxies by intervening mass. Here we present an extremely detailed, wide-area weak-lensing mass map, covering 0.77 deg x 0.70 deg, using high-resolution imaging from the James Webb Space Telescope (JWST) as part of the COSMOS-Web survey. By measuring the shapes of 129 galaxies per square arcminute-many independently in the F115W and F150W bands-we achieve an angular resolution of 1.00 +/- 0.01 arcmin. Our map has more than twice the resolution of earlier Hubble Space Telescope maps, revealing how dark and luminous matter co-evolve across filaments, clusters, and under-densities. It traces mass features out to z ~ 2, including the most distant structure at z ~ 1.1. The sensitivity to high-redshift lensing constrains galaxy environments at the peak of cosmic star formation and sets a high-resolution benchmark for testing theories about the nature of dark matter and the formation of large-scale cosmic structure

Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of machine learning models for photometric redshift estimation on a broader range of galaxy types. Typical models are trained with ground-truth redshifts from spectroscopy. We test the utility and effectiveness of two approaches for combining spectroscopic redshifts and redshifts derived from multiband ($\sim$35 filters) photometry, which sample different types of galaxies compared to spectroscopic surveys. The two approaches are (1) training on a composite dataset and (2) transfer learning from one dataset to another. We compile photometric redshifts from the COSMOS2020 catalog (TransferZ) to complement an established spectroscopic redshift dataset (GalaxiesML). We used two architectures, deterministic neural networks (NN) and Bayesian neural networks (BNN), to examine and evaluate their performance with respect to the Legacy Survey of Space and Time (LSST) photo-$z$ science requirements. We also use split conformal prediction for calibrating uncertainty estimates and producing prediction intervals for the BNN and NN, respectively. We find that a NN trained on a composite dataset predicts photo-$z$'s that are 4.5 times less biased within the redshift range $0.3<z<1.5$, 1.1 times less scattered, and has a 1.4 times lower outlier rate than a model trained on only spectroscopic ground truths. We also find that BNNs produce reliable uncertainty estimates, but are sensitive to the different ground truths. This investigation leverages different sources of ground truths to develop models that can accurately predict photo-$z$'s for a broader population of galaxies crucial for surveys such as Euclid and LSST.

Detached eclipsing binary stars (DEBS) are currently the best source of accurate and precise fundamental stellar parameters. This makes DEBS crucial targets for constraining the impact of various physical processes on stellar structure and evolution. Long-period binaries are particularly interesting because their separation minimises interactions between the components. This makes long-period binaries more comparable to single stars. However, the current sample of DEBS with high precision stellar parameters are dominated by short-period systems (e.g. ~90% of the Gaia DR3 eclipsing binaries have periods < 5 days). Facilities capable of performing detailed studies of long-period DEBS will be essential to further improve our understanding of stellar structure and evolution. Such facilities would need to be able to obtain spectroscopic observations of more distant objects at high resolution and cadence. 2-8m class telescopes with echelle spectrographs and an ability to monitor a large sample of stars would be required.

We investigate galaxies in the GARDEN (Galaxies at All Redshifts Deciphered and Explained with the NIRSpec MSA) survey that show auroral emission lines, enabling spatially resolved measurements of electron temperature and direct oxygen abundances. Two galaxies have spectra suitable for this analysis: CANDELS 8005 at z=3.794 and CANDELS 7986 at z=4.702. For both, we measure auroral and key nebular emission-line fluxes across their full extent, allowing direct-method oxygen abundance determinations in individual spaxels. These observations demonstrate the viability of deep JWST/NIRSpec MSA spectroscopy for spatially resolved chemical analyses at high redshift, aided by weak nebular continua and low interstellar extinction. We derive global direct abundances of 12 + log(O/H) = 8.008 (+0.025, -0.027) for CANDELS 8005 and 7.89 (+0.027, -0.028) for CANDELS 7986. Emission-line diagnostics indicate neither galaxy hosts an active galactic nucleus. A first-order kinematic analysis suggests a potential merger in CANDELS 8005. The direct abundances agree with strong-line estimates from our data and recent high-redshift calibrations. We build emission line, radial velocity, strong-line abundance, electron temperature, and direct abundance maps for both galaxies. From these maps, we measure linear radial metallicity gradients of -0.111 (+0.026, -0.025) dex/kpc for CANDELS 8005 (statistically significant) and -0.093 +/- 0.088 dex/kpc for CANDELS 7986, where the large uncertainties limit significance. These results represent the first detection of a radial metallicity gradient from direct-method abundances with measurements taken in galaxies at z>0, supporting inside-out galaxy growth with feedback-regulated chemical enrichment.

The spatial and kinematic structure of the circumgalactic medium (CGM) remains poorly constrained observationally. In this article we compute the clustering of CIV absorption systems at cosmic noon using quasar pairs. We analyze VLT/UVES and Keck/HIRES high-resolution spectra (R = 45000) of a sample of 8 projected and 4 lensed quasar pairs that probe transverse separations, $Δr$, from sub-kpc to a few Mpc, over the redshift range 1.6 < z < 3.3. We detect and fit Voigt profiles to a total of 141 CIV systems, corresponding to 620 velocity components across all quasar lines-of-sight. We compute the two-point correlation function of CIV, $ξ(Δv, Δr)$, where $Δv$ is the velocity difference between components across all available scales. We find a strong dependence of $ξ(Δr)$ with $Δr$ at all velocities. $ξ(Δr)$ reaches a sharp peak at the smallest scales analyzed here, $Δr\approx 0.1$ kpc, decreases steadily up to $Δr\approx 5$ kpc and remains flat up to $Δr\approx 500$ kpc, where it begins to decrease again. By fitting power-laws to the projected transverse correlation function $Ξ(Δr)$, we infer two coherence lengths: $r_1 = 654^{+100}_{-87}$ kpc, which we interpret as a representative size for the CIV enriched regions at $z\approx 2$, and $r_2 = 4.70^{+1.60}_{-1.19}$ kpc for the individual CIV-bearing "clouds". Projecting instead in $Δr$, we find consistent amplitudes of $ξ(Δv)$ with previous work using quasars and extended background sources. Our results suggest that CIV may be a good tracer of not only the small, internal structure of the circumgalactic medium, but also of the way in which galaxies cluster at cosmic noon.

Our Galaxy, Andromeda and their companion dwarf galaxies form the Local Group. Most of the mass in and around it is believed to be dark matter rather than gas or stars, so its distribution must be inferred from the effect of gravity on the motion of visible objects. Modelling efforts have long struggled to reproduce the quiet Hubble flow around the Local Group, as they require unrealistically little mass beyond the haloes of the two main galaxies. Here we revisit this using $Λ$CDM simulations of Local Group analogues with initial conditions constrained to match the observed dynamics of the two main haloes and the surrounding flow. The observations are reconcilable within $Λ$CDM, but only if mass is strongly concentrated in a plane out to 10 Mpc, with the surface density rising away from the Local Group and with deep voids above and below. This configuration, dynamically inferred, mirrors known structures in the nearby galaxy distribution. The resulting Hubble flow is quiet yet strongly anisotropic, a fact obscured by the paucity of tracers at high supergalactic latitude. This flattened geometry reconciles the dynamical mass estimates of the Local Group with the surrounding velocity field, thus demonstrating full consistency within the standard cosmological model.

We present an improved semi-analytical model to predict density profiles of self-interacting dark matter (SIDM) halos and apply it to constrain the self-scattering cross section using SPARC galaxy rotation curves. Building on the isothermal Jeans approach, our model incorporates (i) velocity-dependent cross sections, (ii) an empirical treatment of core collapse, and (iii) enhanced robustness for identifying solutions. These advances allow us to fit a large sample of galaxies, including systems with baryon-dominated centers often excluded in earlier studies. We find that roughly 1/6 of galaxies admit both a core-growth and a core-collapse solution, while the rest favor a unique evolutionary state. Joint constraints across the sample reveal clear velocity dependence: the allowed parameter space forms an L-shaped degeneracy, where both nearly constant, low cross sections ($σ_0\sim2\,{\rm cm}^2$/g, $ω\gtrsim500\,$km/s) and strongly velocity-dependent models ($σ_0\sim100\,{\rm cm}^2$/g, $ω\sim60\,$km/s) are viable. Adopting the core-growth interpretation yields best-fit values $σ_0\simeq5\,{\rm cm}^2$/g and $ω\simeq250\,$km/s. Our constraints are remarkably consistent with previous results derived from a variety of independent probes. Compared to cold dark matter (CDM) models, SIDM outperforms simple adiabatic-contraction profiles and rivals empirical feedback-based CDM profiles, yet shows no correlation with stellar-to-halo mass ratio, a proxy for feedback strength, offering a distinct explanation for dwarf galaxy diversity. Moreover, SIDM does not affect galaxy-halo scaling relations significantly and makes concentration systematically lower. Our results highlight SIDM as a compelling framework for small-scale structure, while future low-mass kinematic data will be crucial for breaking degeneracies in velocity-dependent cross-section models.

Aims. We aim to investigate the connection between star formation histories (SFHs) and the inner dark matter density profiles of simulated galaxies. In particular, we test whether the burstiness and temporal distribution of star formation influence the formation of cored versus cuspy dark matter profiles. Methods. We homogeneously analysed simulated galaxies from the NIHAO and FIRE-2 projects. For each galaxy, we derived dark matter density profiles and measured the logarithmic slope in the inner region of the dark matter halo (1-2% of R$_{\rm vir}$). To characterise star formation burstiness, we introduced a criterion based on comparing the star formation rate (SFR) averaged over two distinct timescales. We further quantified the duration of SFHs by computing $M_{\star, \rm post}$ / $M_{\star, \rm pre}$, the ratio of stellar mass formed after versus before the epoch of reionisation at redshift z $\sim$ 6.5. Results. Homogeneous analysis reveals that inner slope versus stellar-to-halo mass ratio trends for NIHAO and FIRE-2 galaxies are in much better agreement than reported in previous works. The burstiness and duration of the SFH explain the scatter in the inner slope versus stellar-to-halo mass ratio relation, revealing that galaxies with above average burstiness and more extended SFHs are more efficient at developing cored dark matter profiles. In contrast, galaxies with smoother SFHs and earlier stellar mass assembly tend to maintain cuspier dark matter profiles. We present an analytic expression that improves predictions for the inner slope using the parameter $M_{\star \rm,post}$ / $M_{\star \rm,pre}$, which reduces the mean squared error in both simulation suites relative to previous formulations based solely on the stellar-to-halo mass ratio.

How galaxies formed and evolved in the expanding Universe is the main science goal of Near-Field Cosmology research. Studies of the properties of galaxies' resolved stars open a widow on their ancient galactic components, probing star formation during epochs more than 10 billion years ago. Extragalactic Planetary Nebulae (xPNe) can help decipher the signatures of mergers and interactions persisting over many dynamical times by tracing elemental abundances coupled with their kinematics and spatial distributions. With new facilities, reaching higher angular resolution, area coverage and sensitivity, one can use xPNe to map Oxygen and Argon element abundances, in addition to their kinematics, to extend the Galactic Archeology investigation to the oldest stellar aggregates in our Local Universe.

Comet C/2025 N1 or 3I/ATLAS is the third confirmed interstellar object. It has passed perihelion on 2025 October 29, and is currently on a path to leave the solar system. During its outbound journey, it will pass close to Jupiter at a distance of 0.358 au. NASA JPL \textsc{Horizons} has updated the non-gravitational parameters of the comet based on the CO$_2$ sublimation model, where $g(r)= 1/r^2$. In this research note, we use the non-gravitational accelerations from \textsc{Horizons} together with symmetric and asymmetric H$_2$O sublimation models derived using \texttt{Find\_Orb} software. We calculate the resulting perijove distances and compare them with our earlier results at epoch JD 2460867.5.