# Research Papers — 2025-10 · bundle page 149 of 217

> Full-text markdown of papers #7401–7450 of 10843 published in 2025-10, newest first. AI-readable mirrors of the full 10843-paper 2025-10 archive (50 papers per file).

Canonical page: https://aipod.dev/papers/archive/2025-10/page/149
[← Previous bundle](https://aipod.dev/papers/archive/2025-10/page/148.md)
[Next bundle →](https://aipod.dev/papers/archive/2025-10/page/150.md)

### HiBBO: HiPPO-based Space Consistency for High-dimensional Bayesian Optimisation

Authors: Junyu Xuan, Wenlong Chen, Yingzhen Li
Published: 2025-10-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.08965v1

Bayesian Optimisation (BO) is a powerful tool for optimising expensive blackbox functions but its effectiveness diminishes in highdimensional spaces due to sparse data and poor surrogate model scalability While Variational Autoencoder (VAE) based approaches address this by learning low-dimensional latent representations the reconstructionbased objective function often brings the functional distribution mismatch between the latent space and original space leading to suboptimal optimisation performance In this paper we first analyse the reason why reconstructiononly loss may lead to distribution mismatch and then propose HiBBO a novel BO framework that introduces the space consistency into the latent space construction in VAE using HiPPO - a method for longterm sequence modelling - to reduce the functional distribution mismatch between the latent space and original space Experiments on highdimensional benchmark tasks demonstrate that HiBBO outperforms existing VAEBO methods in convergence speed and solution quality Our work bridges the gap between high-dimensional sequence representation learning and efficient Bayesian Optimisation enabling broader applications in neural architecture search materials science and beyond.

---

### Unleashing Perception-Time Scaling to Multimodal Reasoning Models

Authors: Yifan Li, Zhenghao Chen, Ziheng Wu, Kun Zhou, Ruipu Luo, Can Zhang, Zhentao He, Yufei Zhan, Wayne Xin Zhao, Minghui Qiu
Published: 2025-10-10
Categories: cs.CV, cs.CL
arXiv: https://arxiv.org/abs/2510.08964v1

Recent advances in inference-time scaling, particularly those leveraging reinforcement learning with verifiable rewards, have substantially enhanced the reasoning capabilities of Large Vision-Language Models (LVLMs). Inspired by this success, similar strategies have been applied to multimodal reasoning, yet their impact on visual perception remains unclear. To investigate this gap, we introduce DisTANCE, a perception-centric benchmark for visual estimation tasks. Evaluation results show that LVLMs exhibit limited estimation precision, and inference-time scaling offers only marginal gains. We attribute this to the fast perception paradigm of current LVLMs, where visual understanding is treated as a one-shot output without modeling the underlying perceptual process. To address this, we propose Perception-Time Scaling (PTS), a novel paradigm that encourages token-rich perception and decomposes complex perception problems into intermediate tractable sub-problems, thereby enabling perception to align with and benefit from inference-time scaling. Combined with reinforcement learning techniques, PTS significantly improves perception accuracy, raising high-precision performance on DisTANCE from 8.0% to 64.7%, and generalizes well to out-of-domain tasks. Surprisingly, even though PTS data are purely synthetic, combining them with math reasoning data yields consistent gains in both reasoning and real-world perception benchmarks. Further analysis reveals that PTS introduces more perception-related tokens and increases the model's attention to image tokens. Our code and data will be publicly released.

---

### Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation

Authors: Xiaofeng Cao, Mingwei Xu, Xin Yu, Jiangchao Yao, Wei Ye, Shengjun Huang, Minling Zhang, Ivor W. Tsang, Yew Soon Ong, James T. Kwok, Heng Tao Shen
Published: 2025-10-10
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2510.08962v1

Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain significant. A fundamental objective of AI research is to achieve robust generalization with limited-resource data. This survey employs agnostic active sampling theory within the Probably Approximately Correct (PAC) framework to analyze the generalization error and label complexity associated with learning from low-resource data in both model-agnostic supervised and unsupervised settings. Based on this analysis, we investigate a suite of optimization strategies tailored for low-resource data learning, including gradient-informed optimization, meta-iteration optimization, geometry-aware optimization, and LLMs-powered optimization. Furthermore, we provide a comprehensive overview of multiple learning paradigms that can benefit from low-resource data, including domain transfer, reinforcement feedback, and hierarchical structure modeling. Finally, we conclude our analysis and investigation by summarizing the key findings and highlighting their implications for learning with low-resource data.

---

### DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction

Authors: Jinxin Shi, Zongsheng Cao, Runmin Ma, Yusong Hu, Jie Zhou, Xin Li, Lei Bai, Liang He, Bo Zhang
Published: 2025-10-10
Categories: cs.AI
arXiv: https://arxiv.org/abs/2510.08959v1

The deep-research framework orchestrates external tools to perform complex, multi-step scientific reasoning that exceeds the native limits of a single large language model. However, it still suffers from context pollution, weak evidentiary support, and brittle execution paths. To address these issues, we propose DualResearch, a retrieval and fusion framework that matches the epistemic structure of tool-intensive reasoning by jointly modeling two complementary graphs: a breadth semantic graph that encodes stable background knowledge, and a depth causal graph that captures execution provenance. Each graph has a layer-native relevance function, seed-anchored semantic diffusion for breadth, and causal-semantic path matching with reliability weighting for depth. To reconcile their heterogeneity and query-dependent uncertainty, DualResearch converts per-layer path evidence into answer distributions and fuses them in log space via an entropy-gated rule with global calibration. The fusion up-weights the more certain channel and amplifies agreement. As a complement to deep-research systems, DualResearch compresses lengthy multi-tool execution logs into a concise reasoning graph, and we show that it can reconstruct answers stably and effectively. On the scientific reasoning benchmarks HLE and GPQA, DualResearch achieves competitive performance. Using log files from the open-source system InternAgent, its accuracy improves by 7.7% on HLE and 6.06% on GPQA.

---

### EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory

Authors: Zirui Liao
Published: 2025-10-10
Categories: cs.AI, cs.IR
arXiv: https://arxiv.org/abs/2510.08958v1

Cognitive neuroscience research indicates that humans leverage cues to activate entity-centered memory traces (engrams) for complex, multi-hop recollection. Inspired by this mechanism, we introduce EcphoryRAG, an entity-centric knowledge graph RAG framework. During indexing, EcphoryRAG extracts and stores only core entities with corresponding metadata, a lightweight approach that reduces token consumption by up to 94\% compared to other structured RAG systems. For retrieval, the system first extracts cue entities from queries, then performs a scalable multi-hop associative search across the knowledge graph. Crucially, EcphoryRAG dynamically infers implicit relations between entities to populate context, enabling deep reasoning without exhaustive pre-enumeration of relationships. Extensive evaluations on the 2WikiMultiHop, HotpotQA, and MuSiQue benchmarks demonstrate that EcphoryRAG sets a new state-of-the-art, improving the average Exact Match (EM) score from 0.392 to 0.474 over strong KG-RAG methods like HippoRAG. These results validate the efficacy of the entity-cue-multi-hop retrieval paradigm for complex question answering.

---

### A Human Behavioral Baseline for Collective Governance in Software Projects

Authors: Mobina Noori, Mahasweta Chakraborti, Amy X Zhang, Seth Frey
Published: 2025-10-10
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2510.08956v2

We study how open source communities describe participation and control through version controlled governance documents. Using a corpus of 710 projects with paired snapshots, we parse text into actors, rules, actions, and objects, then group them and measure change with entropy for evenness, richness for diversity, and Jensen Shannon divergence for drift. Projects define more roles and more actions over time, and these are distributed more evenly, while the composition of rules remains stable. These findings indicate that governance grows by expanding and balancing categories of participation without major shifts in prescriptive force. The analysis provides a reproducible baseline for evaluating whether future AI mediated workflows concentrate or redistribute authority.

---

### Denoised Diffusion for Object-Focused Image Augmentation

Authors: Nisha Pillai
Published: 2025-10-10
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2510.08955v2

Modern agricultural operations increasingly rely on integrated monitoring systems that combine multiple data sources for farm optimization. Aerial drone-based animal health monitoring serves as a key component but faces limited data availability, compounded by scene-specific issues such as small, occluded, or partially visible animals. Transfer learning approaches often fail to address this limitation due to the unavailability of large datasets that reflect specific farm conditions, including variations in animal breeds, environments, and behaviors. Therefore, there is a need for developing a problem-specific, animal-focused data augmentation strategy tailored to these unique challenges. To address this gap, we propose an object-focused data augmentation framework designed explicitly for animal health monitoring in constrained data settings. Our approach segments animals from backgrounds and augments them through transformations and diffusion-based synthesis to create realistic, diverse scenes that enhance animal detection and monitoring performance. Our initial experiments demonstrate that our augmented dataset yields superior performance compared to our baseline models on the animal detection task. By generating domain-specific data, our method empowers real-time animal health monitoring solutions even in data-scarce scenarios, bridging the gap between limited data and practical applicability.

---

### When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach

Authors: Zhihan Zhang, Xunkai Li, Yilong Zuo, Henan Sun, Zhenjun Li, Bing Zhou, Rong-Hua Li, Guoren Wang
Published: 2025-10-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.08952v4

Text-attributed graphs (TAGs) have become a key form of graph-structured data in modern data management and analytics, combining structural relationships with rich textual semantics for diverse applications. However, the effectiveness of analytical models, particularly graph neural networks (GNNs), is highly sensitive to data quality. Our empirical analysis shows that both conventional and LLM-enhanced GNNs degrade notably under textual, structural, and label imperfections, underscoring TAG quality as a key bottleneck for reliable analytics. Existing studies have explored data-level optimization for TAGs, but most focus on specific degradation types and target a single aspect like structure or label, lacking a systematic and comprehensive perspective on data quality improvement. To address this gap, we propose LAGA (Large Language and Graph Agent), a unified multi-agent framework for comprehensive TAG quality optimization. LAGA formulates graph quality control as a data-centric process, integrating detection, planning, action, and evaluation agents into an automated loop. It holistically enhances textual, structural, and label aspects through coordinated multi-modal optimization. Extensive experiments on 5 datasets and 16 baselines across 9 scenarios demonstrate the effectiveness, robustness and scalability of LAGA, confirming the importance of data-centric quality optimization for reliable TAG analytics.

---

### FS-RWKV: Leveraging Frequency Spatial-Aware RWKV for 3T-to-7T MRI Translation

Authors: Yingtie Lei, Zimeng Li, Chi-Man Pun, Yupeng Liu, Xuhang Chen
Published: 2025-10-10
Categories: eess.IV, cs.CV
arXiv: https://arxiv.org/abs/2510.08951v1

Ultra-high-field 7T MRI offers enhanced spatial resolution and tissue contrast that enables the detection of subtle pathological changes in neurological disorders. However, the limited availability of 7T scanners restricts widespread clinical adoption due to substantial infrastructure costs and technical demands. Computational approaches for synthesizing 7T-quality images from accessible 3T acquisitions present a viable solution to this accessibility challenge. Existing CNN approaches suffer from limited spatial coverage, while Transformer models demand excessive computational overhead. RWKV architectures offer an efficient alternative for global feature modeling in medical image synthesis, combining linear computational complexity with strong long-range dependency capture. Building on this foundation, we propose Frequency Spatial-RWKV (FS-RWKV), an RWKV-based framework for 3T-to-7T MRI translation. To better address the challenges of anatomical detail preservation and global tissue contrast recovery, FS-RWKV incorporates two key modules: (1) Frequency-Spatial Omnidirectional Shift (FSO-Shift), which performs discrete wavelet decomposition followed by omnidirectional spatial shifting on the low-frequency branch to enhance global contextual representation while preserving high-frequency anatomical details; and (2) Structural Fidelity Enhancement Block (SFEB), a module that adaptively reinforces anatomical structure through frequency-aware feature fusion. Comprehensive experiments on UNC and BNU datasets demonstrate that FS-RWKV consistently outperforms existing CNN-, Transformer-, GAN-, and RWKV-based baselines across both T1w and T2w modalities, achieving superior anatomical fidelity and perceptual quality.

---

### Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation

Authors: Zhen Yang, Yansong Ma, Lei Chen
Published: 2025-10-10
Categories: eess.IV, cs.CV
arXiv: https://arxiv.org/abs/2510.08949v1

Trustworthy medical image segmentation aims at deliver accurate and reliable results for clinical decision-making. Most existing methods adopt the evidence deep learning (EDL) paradigm due to its computational efficiency and theoretical robustness. However, the EDL-based methods often neglect leveraging uncertainty maps rich in attention cues to refine ambiguous boundary segmentation. To address this, we propose a progressive evidence uncertainty guided attention (PEUA) mechanism to guide the model to focus on the feature representation learning of hard regions. Unlike conventional approaches, PEUA progressively refines attention using uncertainty maps while employing low-rank learning to denoise attention weights, enhancing feature learning for challenging regions. Concurrently, standard EDL methods suppress evidence of incorrect class indiscriminately via Kullback-Leibler (KL) regularization, impairing the uncertainty assessment in ambiguous areas and consequently distorts the corresponding attention guidance. We thus introduce a semantic-preserving evidence learning (SAEL) strategy, integrating a semantic-smooth evidence generator and a fidelity-enhancing regularization term to retain critical semantics. Finally, by embedding PEUA and SAEL with the state-of-the-art U-KAN, we proposes Evidential U-KAN, a novel solution for trustworthy medical image segmentation. Extensive experiments on 4 datasets demonstrate superior accuracy and reliability over the competing methods. The code is available at \href{https://anonymous.4open.science/r/Evidence-U-KAN-BBE8}{github}.

---

### SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management

Authors: Nan Lu, Yurong Hu, Jiaquan Fang, Yan Liu, Rui Dong, Yiming Wang, Rui Lin, Shaoyi Xu
Published: 2025-10-10
Categories: cs.IR, cs.AI
arXiv: https://arxiv.org/abs/2510.08948v4

Effective e-commerce risk management requires in-depth case investigations to identify emerging fraud patterns in highly adversarial environments. However, manual investigation typically requires analyzing the associations and couplings among multi-source heterogeneous data, a labor-intensive process that limits efficiency. While Large Language Models (LLMs) show promise in automating these analyses, their deployment is hindered by the complexity of risk scenarios and the sparsity of long-tail domain knowledge. To address these challenges, we propose Sherlock, a framework that integrates structured domain knowledge with LLM-based reasoning through three core modules. First, we construct a domain Knowledge Base (KB) by distilling structured expertise from heterogeneous knowledge sources. Second, we design a two-stage retrieval-augmented generation strategy tailored for case investigation, which combines input contextual augmentation with a Reflect & Refine module to fully leverage the KB for improved analysis quality. Finally, we develop an integrated platform for operations and annotation to drive a self-evolving data flywheel. By combining real-time hotfixes through KB updates with periodic logic alignment via post-training, we facilitate continuous system evolution to counteract adversarial drifts. Online A/B tests at JD dot com demonstrate that Sherlock achieves an 82% Expert Acceptance Rate (EAR) and a 386.7% increase in daily investigation throughput. An additional 90-day evaluation shows that the flywheel successfully recovers from performance decay caused by changing tactics twice, raising the EAR ceiling by around 3.5% through autonomous model updates.

---

### Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection

Authors: Siyuan Chen, Minghao Guo, Caoliwen Wang, Anka He Chen, Yikun Zhang, Jingjing Chai, Yin Yang, Wojciech Matusik, Peter Yichen Chen
Published: 2025-10-10
Categories: q-bio.BM, cs.LG
arXiv: https://arxiv.org/abs/2510.08946v2

Biomolecular interaction modeling has been substantially advanced by foundation models, yet they often produce all-atom structures that violate basic steric feasibility. We address this limitation by enforcing physical validity as a strict constraint during both training and inference with a uniffed module. At its core is a differentiable projection that maps the provisional atom coordinates from the diffusion model to the nearest physically valid conffguration. This projection is achieved using a Gauss-Seidel scheme, which exploits the locality and sparsity of the constraints to ensure stable and fast convergence at scale. By implicit differentiation to obtain gradients, our module integrates seamlessly into existing frameworks for end-to-end ffnetuning. With our Gauss-Seidel projection module in place, two denoising steps are sufffcient to produce biomolecular complexes that are both physically valid and structurally accurate. Across six benchmarks, our 2-step model achieves the same structural accuracy as state-of-the-art 200-step diffusion baselines, delivering approximately 10 times faster wall-clock speed while guaranteeing physical validity. The code is available at https://github.com/chensiyuan030105/ProteinGS.git.

---

### FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

Authors: Samuel Hildebrand, Curtis Taylor, Sean Oesch, James M Ghawaly, Amir Sadovnik, Ryan Shivers, Brandon Schreiber, Kevin Kurian
Published: 2025-10-10
Categories: cs.AI
arXiv: https://arxiv.org/abs/2510.08945v3

Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipeline's ability to ingest, retrieve, and reason about several modalities of information, differentiating it from existing benchmarks that focus on particular aspects such as retrieval. We present (1) a small, human-created dataset of 93 questions designed to evaluate a pipeline's ability to ingest textual data, tables, images, and data spread across these modalities in one or more documents; (2) a phrase-level recall metric for correctness; (3) a nearest-neighbor embedding classifier to identify potential pipeline hallucinations; (4) a comparative evaluation of 2 pipelines built with open-source retrieval mechanisms and 4 closed-source foundation models; and (5) a third-party human evaluation of the alignment of our correctness and hallucination metrics. We find that closed-source pipelines significantly outperform open-source pipelines in both correctness and hallucination metrics, with wider performance gaps in questions relying on multimodal and cross-document information. Human evaluation of our metrics showed average agreement of 4.62 for correctness and 4.53 for hallucination detection on a 1-5 Likert scale (5 indicating "strongly agree").

---

### Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling

Authors: Haroon Gharwi, Yue Dai, Kai Shu
Published: 2025-10-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.08944v2

Real-world time-series regression often involves non-stationarity, heteroscedasticity, and regime changes, under which recent prediction errors may contain structured information about local temporal mismatch between model predictions and observations. Learning how to represent and reuse these errors can therefore provide useful information for subsequent prediction. We introduce the Variability-Aware Recursive Neural Network (VARNN), a residual-aware architecture for supervised time-series regression that learns an explicit residual-memory state from recent prediction errors and uses it to condition subsequent predictions. Specifically, VARNN maps scalar prediction innovations into a learned nonlinear, vector-valued residual representation over a short context. Across nine datasets spanning energy, healthcare, and environmental domains, VARNN achieves lower test MSE than the compared static, lag-based, and sequence-model baselines. Targeted ablations further show that learned projected residual memory improves predictive accuracy over direct scalar residual feedback, supporting the benefit of a learned nonlinear representation of prediction deviations.

---

### SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures

Authors: Jiaming Wang, Zhe Tang, Zehao Jin, Hefei Chen, Yilin Jin, Peng Ding, Xiaoyu Li, Xuezhi Cao
Published: 2025-10-10
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.08942v2

As large language models (LLMs) are widely deployed as domain-specific agents, many benchmarks have been proposed to evaluate their ability to follow instructions and make decisions in real-world scenarios. However, business scenarios often involve complex standard operating procedures (SOPs), and the evaluation of LLM capabilities in such contexts has not been fully explored. To bridge this gap, we propose SOP-Maze, a benchmark constructed from real-world business data and adapted into a collection of 397 instances and 3422 subtasks from 23 complex SOP scenarios. We further categorize SOP tasks into two broad classes: Lateral Root System (LRS), representing wide-option tasks that demand precise selection; and Heart Root System (HRS), which emphasizes deep logical reasoning with complex branches. Extensive experiments reveal that nearly all state-of-the-art models struggle with SOP-Maze. We conduct a comprehensive analysis and identify three key error categories: (i) route blindness: difficulty following procedures; (ii) conversational fragility: inability to handle real dialogue nuances; and (iii) calculation errors: mistakes in time or arithmetic reasoning under complex contexts. The systematic study explores LLM performance across SOP tasks that challenge both breadth and depth, offering new insights for improving model capabilities. We have open-sourced our work on: https://github.com/meituan-longcat/SOP-Maze.

---

### Bi-level Meta-Policy Control for Dynamic Uncertainty Calibration in Evidential Deep Learning

Authors: Zhen Yang, Yansong Ma, Lei Chen
Published: 2025-10-10
Categories: cs.LG, cs.CV
arXiv: https://arxiv.org/abs/2510.08938v1

Traditional Evidence Deep Learning (EDL) methods rely on static hyperparameter for uncertainty calibration, limiting their adaptability in dynamic data distributions, which results in poor calibration and generalization in high-risk decision-making tasks. To address this limitation, we propose the Meta-Policy Controller (MPC), a dynamic meta-learning framework that adjusts the KL divergence coefficient and Dirichlet prior strengths for optimal uncertainty modeling. Specifically, MPC employs a bi-level optimization approach: in the inner loop, model parameters are updated through a dynamically configured loss function that adapts to the current training state; in the outer loop, a policy network optimizes the KL divergence coefficient and class-specific Dirichlet prior strengths based on multi-objective rewards balancing prediction accuracy and uncertainty quality. Unlike previous methods with fixed priors, our learnable Dirichlet prior enables flexible adaptation to class distributions and training dynamics. Extensive experimental results show that MPC significantly enhances the reliability and calibration of model predictions across various tasks, improving uncertainty calibration, prediction accuracy, and performance retention after confidence-based sample rejection.

---

### RO-Bench: Large-scale robustness evaluation of MLLMs with text-driven counterfactual videos

Authors: Zixi Yang, Jiapeng Li, Muxi Diao, Yinuo Jing, Kongming Liang
Published: 2025-10-10
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2510.08936v1

Recently, Multi-modal Large Language Models (MLLMs) have demonstrated significant performance across various video understanding tasks. However, their robustness, particularly when faced with manipulated video content, remains largely unexplored. In this paper, we introduce Ro-Bench, the first benchmark for evaluating MLLMs on dynamic out-of-distribution (OOD) counterfactual video test sets. Ro-Bench incorporates high-quality, diverse and temporally relevant video data, by editing Style, Object, Background and their compositions. We evaluated eight recent video MLLMs and found that current models exhibit substantial performance degradation on Ro-Bench when exposed to counterfactual video content. Furthermore, we demonstrate that fine-tuning MLLMs with counterfactual data enhances robustness, achieving a 21.73% performance increase on Ro-Bench and a 12.78% improvement across 20 tasks in the MVBench dataset. These findings underscore the effectiveness of counterfactual data in enhancing the video understanding ability of MLLMs. The code and data will be released shortly.

---

### MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths

Authors: Moyu Zhang, Yun Chen, Yujun Jin, Jinxin Hu, Yu Zhang, Xiaoyi Zeng
Published: 2025-10-10
Categories: cs.LG, cs.IR
arXiv: https://arxiv.org/abs/2510.08932v2

Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining training objectives to aid parameter learning, thereby achieving better predictive performance. However, previous efforts have primarily focused on the training phase, largely neglecting opportunities for optimization during the inference phase. Infrequently occurring feature combinations, in particular, can degrade prediction performance, leading to unreliable or low-confidence outputs. To unlock the predictive potential of trained CTR models, we propose a Model-Agnostic Test-Time paradigm (MATT), which leverages the confidence scores of feature combinations to guide the generation of multiple inference paths, thereby mitigating the influence of low-confidence features on the final prediction. Specifically, to quantify the confidence of feature combinations, we introduce a hierarchical probabilistic hashing method to estimate the occurrence frequencies of feature combinations at various orders, which serve as their corresponding confidence scores. Then, using the confidence scores as sampling probabilities, we generate multiple instance-specific inference paths through iterative sampling and subsequently aggregate the prediction scores from multiple paths to conduct robust predictions. Finally, extensive offline experiments and online A/B tests strongly validate the compatibility and effectiveness of MATT across existing CTR models.

---

### RADAR: Mechanistic Pathways for Detecting Data Contamination in LLM Evaluation

Authors: Ashish Kattamuri, Harshwardhan Fartale, Arpita Vats, Rahul Raja, Ishita Prasad
Published: 2025-10-10
Categories: cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2510.08931v1

Data contamination poses a significant challenge to reliable LLM evaluation, where models may achieve high performance by memorizing training data rather than demonstrating genuine reasoning capabilities. We introduce RADAR (Recall vs. Reasoning Detection through Activation Representation), a novel framework that leverages mechanistic interpretability to detect contamination by distinguishing recall-based from reasoning-based model responses. RADAR extracts 37 features spanning surface-level confidence trajectories and deep mechanistic properties including attention specialization, circuit dynamics, and activation flow patterns. Using an ensemble of classifiers trained on these features, RADAR achieves 93\% accuracy on a diverse evaluation set, with perfect performance on clear cases and 76.7\% accuracy on challenging ambiguous examples. This work demonstrates the potential of mechanistic interpretability for advancing LLM evaluation beyond traditional surface-level metrics.

---

### Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders

Authors: Ruixuan Sun, Junyuan Wang, Sanjali Roy, Joseph A. Konstan
Published: 2025-10-10
Categories: cs.HC, cs.AI
arXiv: https://arxiv.org/abs/2510.08930v1

Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby improving recommendation quality. Building on this foundation, we introduce a human-AI collaborative profile for a movie recommender system that presents editable personalized interest summaries of a user's movie history. Unlike static profiles, this design invites users to directly inspect, modify, and reflect on the system's inferences. In an eight-week online field deployment with 1775 active movie recommender users, we find persistent gaps between user-perceived and system-inferred interests, show how the profile encourages engagement and reflection, and identify design directions for leveraging imperfect AI-powered user profiles to stimulate more user intervention and build more transparent and trustworthy recommender experiences.

---

### Mirror Flow Matching with Heavy-Tailed Priors for Generative Modeling on Convex Domains

Authors: Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma
Published: 2025-10-10
Categories: stat.ML, cs.LG
arXiv: https://arxiv.org/abs/2510.08929v1

We study generative modeling on convex domains using flow matching and mirror maps, and identify two fundamental challenges. First, standard log-barrier mirror maps induce heavy-tailed dual distributions, leading to ill-posed dynamics. Second, coupling with Gaussian priors performs poorly when matching heavy-tailed targets. To address these issues, we propose Mirror Flow Matching based on a \emph{regularized mirror map} that controls dual tail behavior and guarantees finite moments, together with coupling to a Student-$t$ prior that aligns with heavy-tailed targets and stabilizes training. We provide theoretical guarantees, including spatial Lipschitzness and temporal regularity of the velocity field, Wasserstein convergence rates for flow matching with Student-$t$ priors and primal-space guarantees for constrained generation, under $\varepsilon$-accurate learned velocity fields. Empirically, our method outperforms baselines in synthetic convex-domain simulations and achieves competitive sample quality on real-world constrained generative tasks.

---

### LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition

Authors: Yushuo Zheng, Tongrui Ye, Zicheng Zhang, Xiongkuo Min, Huiyu Duan, Guangtao Zhai
Published: 2025-10-10
Categories: cs.AI
arXiv: https://arxiv.org/abs/2510.08928v2

Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce LM Fight Arena (Large Model Fight Arena), a novel framework that evaluates LMMs by pitting them against each other in the classic fighting game Mortal Kombat II, a task requiring rapid visual understanding and tactical, sequential decision-making. In a controlled tournament, we test six leading open- and closed-source models, where each agent operates controlling the same character to ensure a fair comparison. The models are prompted to interpret game frames and state data to select their next actions. Unlike static evaluations, LM Fight Arena provides a fully automated, reproducible, and objective assessment of an LMM's strategic reasoning capabilities in a dynamic setting. This work introduces a challenging and engaging benchmark that bridges the gap between AI evaluation and interactive entertainment.

---

### Defense against Unauthorized Distillation in Image Restoration via Feature Space Perturbation

Authors: Han Hu, Zhuoran Zheng, Chen Lyu
Published: 2025-10-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2510.08925v1

Knowledge distillation (KD) attacks pose a significant threat to deep model intellectual property by enabling adversaries to train student networks using a teacher model's outputs. While recent defenses in image classification have successfully disrupted KD by perturbing output probabilities, extending these methods to image restoration is difficult. Unlike classification, restoration is a generative task with continuous, high-dimensional outputs that depend on spatial coherence and fine details. Minor perturbations are often insufficient, as students can still learn the underlying mapping.To address this, we propose Adaptive Singular Value Perturbation (ASVP), a runtime defense tailored for image restoration models. ASVP operates on internal feature maps of the teacher using singular value decomposition (SVD). It amplifies the topk singular values to inject structured, high-frequency perturbations, disrupting the alignment needed for distillation. This hinders student learning while preserving the teacher's output quality.We evaluate ASVP across five image restoration tasks: super-resolution, low-light enhancement, underwater enhancement, dehazing, and deraining. Experiments show ASVP reduces student PSNR by up to 4 dB and SSIM by 60-75%, with negligible impact on the teacher's performance. Compared to prior methods, ASVP offers a stronger and more consistent defense.Our approach provides a practical solution to protect open-source restoration models from unauthorized knowledge distillation.

---

### AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition

Authors: Jonah Botvinick-Greenhouse, Wael H. Ali, Mouhacine Benosman, Saviz Mowlavi
Published: 2025-10-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.08924v1

We introduce adaptive-basis physics-informed neural networks (AB-PINNs), a novel approach to domain decomposition for training PINNs in which existing subdomains dynamically adapt to the intrinsic features of the unknown solution. Drawing inspiration from classical mesh refinement techniques, we also modify the domain decomposition on-the-fly throughout training by introducing new subdomains in regions of high residual loss, thereby providing additional expressive power where the solution of the differential equation is challenging to represent. Our flexible approach to domain decomposition is well-suited for multiscale problems, as different subdomains can learn to capture different scales of the underlying solution. Moreover, the ability to introduce new subdomains during training helps prevent convergence to unwanted local minima and can reduce the need for extensive hyperparameter tuning compared to static domain decomposition approaches. Throughout, we present comprehensive numerical results which demonstrate the effectiveness of AB-PINNs at solving a variety of complex multiscale partial differential equations.

---

### SegTrans: Transferable Adversarial Examples for Segmentation Models

Authors: Yufei Song, Ziqi Zhou, Qi Lu, Hangtao Zhang, Yifan Hu, Lulu Xue, Shengshan Hu, Minghui Li, Leo Yu Zhang
Published: 2025-10-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2510.08922v1

Segmentation models exhibit significant vulnerability to adversarial examples in white-box settings, but existing adversarial attack methods often show poor transferability across different segmentation models. While some researchers have explored transfer-based adversarial attack (i.e., transfer attack) methods for segmentation models, the complex contextual dependencies within these models and the feature distribution gaps between surrogate and target models result in unsatisfactory transfer success rates. To address these issues, we propose SegTrans, a novel transfer attack framework that divides the input sample into multiple local regions and remaps their semantic information to generate diverse enhanced samples. These enhanced samples replace the original ones for perturbation optimization, thereby improving the transferability of adversarial examples across different segmentation models. Unlike existing methods, SegTrans only retains local semantic information from the original input, rather than using global semantic information to optimize perturbations. Extensive experiments on two benchmark datasets, PASCAL VOC and Cityscapes, four different segmentation models, and three backbone networks show that SegTrans significantly improves adversarial transfer success rates without introducing additional computational overhead. Compared to the current state-of-the-art methods, SegTrans achieves an average increase of 8.55% in transfer attack success rate and improves computational efficiency by more than 100%.

---

### Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model

Authors: Yuting Yang, Gang Mei, Zhengjing Ma, Nengxiong Xu, Jianbing Peng
Published: 2025-10-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.08920v1

Small Earth data are geoscience observations with limited short-term monitoring variability, providing sparse but meaningful measurements, typically exhibiting spatiotemporal correlations. Spatiotemporal forecasting on such data is crucial for understanding geoscientific processes despite their small scale. However, conventional deep learning models for spatiotemporal forecasting requires task-specific training for different scenarios. Foundation models do not need task-specific training, but they often exhibit forecasting bias toward the global mean of the pretraining distribution. Here we propose a simple and robust approach for spatiotemporally correlated small Earth data forecasting. The essential idea is to characterize and quantify spatiotemporal patterns of small Earth data and then utilize tabular foundation models for accurate forecasting across different scenarios. Comparative results across three typical scenarios demonstrate that our forecasting approach achieves superior accuracy compared to the graph deep learning model (T-GCN) and tabular foundation model (TabPFN) in the majority of instances, exhibiting stronger robustness.

---

### PHyCLIP: $\ell_1$-Product of Hyperbolic Factors Unifies Hierarchy and Compositionality in Vision-Language Representation Learning

Authors: Daiki Yoshikawa, Takashi Matsubara
Published: 2025-10-10
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2510.08919v2

Vision-language models have achieved remarkable success in multi-modal representation learning from large-scale pairs of visual scenes and linguistic descriptions. However, they still struggle to simultaneously express two distinct types of semantic structures: the hierarchy within a concept family (e.g., dog $\preceq$ mammal $\preceq$ animal) and the compositionality across different concept families (e.g., "a dog in a car" $\preceq$ dog, car). Recent works have addressed this challenge by employing hyperbolic space, which efficiently captures tree-like hierarchy, yet its suitability for representing compositionality remains unclear. To resolve this dilemma, we propose PHyCLIP, which employs an $\ell_1$-Product metric on a Cartesian product of Hyperbolic factors. With our design, intra-family hierarchies emerge within individual hyperbolic factors, and cross-family composition is captured by the $\ell_1$-product metric, analogous to a Boolean algebra. Experiments on zero-shot classification, retrieval, hierarchical classification, and compositional understanding tasks demonstrate that PHyCLIP outperforms existing single-space approaches and offers more interpretable structures in the embedding space.

---

### A Representer Theorem for Hawkes Processes via Penalized Least Squares Minimization

Authors: Hideaki Kim, Tomoharu Iwata
Published: 2025-10-10
Categories: stat.ML, cs.LG
arXiv: https://arxiv.org/abs/2510.08916v2

The representer theorem is a cornerstone of kernel methods, which aim to estimate latent functions in reproducing kernel Hilbert spaces (RKHSs) in a nonparametric manner. Its significance lies in converting inherently infinite-dimensional optimization problems into finite-dimensional ones over dual coefficients, thereby enabling practical and computationally tractable algorithms. In this paper, we address the problem of estimating the latent triggering kernels--functions that encode the interaction structure between events--for linear multivariate Hawkes processes based on observed event sequences within an RKHS framework. We show that, under the principle of penalized least squares minimization, a novel form of representer theorem emerges: a family of transformed kernels can be defined via a system of simultaneous integral equations, and the optimal estimator of each triggering kernel is expressed as a linear combination of these transformed kernels evaluated at the data points. Remarkably, the dual coefficients are all analytically fixed to unity, obviating the need to solve a costly optimization problem to obtain the dual coefficients. This leads to a highly efficient estimator capable of handling large-scale data more effectively than conventional nonparametric approaches. Empirical evaluations on synthetic datasets reveal that the proposed method attains competitive predictive accuracy while substantially improving computational efficiency over existing state-of-the-art kernel method-based estimators.

---

### Artificial Impressions: Evaluating Large Language Model Behavior Through the Lens of Trait Impressions

Authors: Nicholas Deas, Kathleen McKeown
Published: 2025-10-10
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.08915v1

We introduce and study artificial impressions--patterns in LLMs' internal representations of prompts that resemble human impressions and stereotypes based on language. We fit linear probes on generated prompts to predict impressions according to the two-dimensional Stereotype Content Model (SCM). Using these probes, we study the relationship between impressions and downstream model behavior as well as prompt features that may inform such impressions. We find that LLMs inconsistently report impressions when prompted, but also that impressions are more consistently linearly decodable from their hidden representations. Additionally, we show that artificial impressions of prompts are predictive of the quality and use of hedging in model responses. We also investigate how particular content, stylistic, and dialectal features in prompts impact LLM impressions.

---

### Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS

Authors: Maoxin Ji, Tong Wang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen
Published: 2025-10-10
Categories: cs.LG, cs.NI
arXiv: https://arxiv.org/abs/2510.08911v1

Addressing the problem of Age of Information (AoI) deterioration caused by packet collisions and vehicle speed-related channel uncertainties in Semi-Persistent Scheduling (SPS) for the Internet of Vehicles (IoV), this letter proposes an optimization approach based on Large Language Models (LLM) and Deep Deterministic Policy Gradient (DDPG). First, an AoI calculation model influenced by vehicle speed, vehicle density, and Resource Reservation Interval (RRI) is established, followed by the design of a dual-path optimization scheme. The DDPG is guided by the state space and reward function, while the LLM leverages contextual learning to generate optimal parameter configurations. Experimental results demonstrate that LLM can significantly reduce AoI after accumulating a small number of exemplars without requiring model training, whereas the DDPG method achieves more stable performance after training.

---

### A Frequency-Domain Analysis of the Multi-Armed Bandit Problem: A New Perspective on the Exploration-Exploitation Trade-off

Authors: Di Zhang
Published: 2025-10-10
Categories: cs.LG, cs.AI, cs.IT, math.OC, stat.ML
arXiv: https://arxiv.org/abs/2510.08908v1

The stochastic multi-armed bandit (MAB) problem is one of the most fundamental models in sequential decision-making, with the core challenge being the trade-off between exploration and exploitation. Although algorithms such as Upper Confidence Bound (UCB) and Thompson Sampling, along with their regret theories, are well-established, existing analyses primarily operate from a time-domain and cumulative regret perspective, struggling to characterize the dynamic nature of the learning process. This paper proposes a novel frequency-domain analysis framework, reformulating the bandit process as a signal processing problem. Within this framework, the reward estimate of each arm is viewed as a spectral component, with its uncertainty corresponding to the component's frequency, and the bandit algorithm is interpreted as an adaptive filter. We construct a formal Frequency-Domain Bandit Model and prove the main theorem: the confidence bound term in the UCB algorithm is equivalent in the frequency domain to a time-varying gain applied to uncertain spectral components, a gain inversely proportional to the square root of the visit count. Based on this, we further derive finite-time dynamic bounds concerning the exploration rate decay. This theory not only provides a novel and intuitive physical interpretation for classical algorithms but also lays a rigorous theoretical foundation for designing next-generation algorithms with adaptive parameter adjustment.

---

### Autoencoding-Free Context Compression for LLMs via Contextual Semantic Anchors

Authors: Xin Liu, Runsong Zhao, Pengcheng Huang, Xinyu Liu, Junyi Xiao, Chunyang Xiao, Tong Xiao, Shengxiang Gao, Zhengtao Yu, Jingbo Zhu
Published: 2025-10-10
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.08907v4

Context compression is an advanced technique that accelerates large language model (LLM) inference by converting long inputs into compact representations. Existing methods primarily rely on autoencoding tasks to train special compression tokens to represent contextual semantics. While autoencoding tasks enable compression tokens to acquire compression capabilities, we remark that such capabilities potentially conflict with actual downstream task requirements, prevent the models from learning the features more beneficial for real-world usage. Based on this observation, we propose Semantic-Anchor Compression (SAC), a novel method that shifts from autoencoding task based compression to an architecture that is equipped with this compression capability \textit{a priori}. Instead of training models to compress contexts through autoencoding tasks, SAC directly selects so-called anchor tokens from the original context and aggregates contextual information into their key-value (KV) representations. To ensure that anchors can effectively collect information, SAC introduces two key designs: (1) anchor embedding, a learnable embedding vector attached to the selected anchor tokens to mark compression carriers and (2) bidirectional attention modification, which enables anchor tokens to integrate information from the entire context. Experimental results show that SAC consistently outperforms existing context compression methods across different compression ratios and model sizes on question-answering and long-context summarization tasks. Our data, model and code have been released at \href{https://github.com/lx-Meteors/SAC}{https://github.com/lx-Meteors/SAC}.

---

### Gradient-Guided Furthest Point Sampling for Robust Training Set Selection

Authors: Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld
Published: 2025-10-10
Categories: stat.ML, cs.LG, physics.chem-ph
arXiv: https://arxiv.org/abs/2510.08906v2

Training set sampling methods are used to improve model performance and lower data costs in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular force norms to guide efficient sampling of configurational spaces of molecules. Numerical evidence is presented for a toy system (the Styblinski-Tang function) as well as for molecular dynamics trajectories from the MD17 dataset. Our numerical results indicate superior data efficiency and model robustness when using GGFPS compared to FPS and uniform random sampling (URS), as well as established supervised FPS-style selectors, PCov-FPS and PCov-CUR. Distribution analysis of the MD17 data suggests that FPS systematically under-samples equilibrium geometries, resulting in large test errors for relaxed structures. GGFPS cures this artifact and (i) enables up to twofold reductions in training cost without sacrificing predictive accuracy compared to FPS in the 2-dimensional Styblinski-Tang system, (ii) systematically lowers prediction errors for equilibrium as well as strained structures in MD17, and (iii) systematically decreases prediction error variances across all of the MD17 configuration spaces. These results suggest that gradient-aware sampling methods hold great promise as effective training set selection tools, and that naive use of FPS may result in imbalanced training and inconsistent prediction outcomes.

---

### A Unified Biomedical Named Entity Recognition Framework with Large Language Models

Authors: Tengxiao Lv, Ling Luo, Juntao Li, Yanhua Wang, Yuchen Pan, Chao Liu, Yanan Wang, Yan Jiang, Huiyi Lv, Yuanyuan Sun, Jian Wang, Hongfei Lin
Published: 2025-10-10
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2510.08902v1

Accurate recognition of biomedical named entities is critical for medical information extraction and knowledge discovery. However, existing methods often struggle with nested entities, entity boundary ambiguity, and cross-lingual generalization. In this paper, we propose a unified Biomedical Named Entity Recognition (BioNER) framework based on Large Language Models (LLMs). We first reformulate BioNER as a text generation task and design a symbolic tagging strategy to jointly handle both flat and nested entities with explicit boundary annotation. To enhance multilingual and multi-task generalization, we perform bilingual joint fine-tuning across multiple Chinese and English datasets. Additionally, we introduce a contrastive learning-based entity selector that filters incorrect or spurious predictions by leveraging boundary-sensitive positive and negative samples. Experimental results on four benchmark datasets and two unseen corpora show that our method achieves state-of-the-art performance and robust zero-shot generalization across languages. The source codes are freely available at https://github.com/dreamer-tx/LLMNER.

---

### Modeling Time-Lapse Trajectories to Characterize Cranberry Growth

Authors: Ronan John, Anis Chihoub, Ryan Meegan, Gina Sidelli, Jeffery Neyhart, Peter Oudemans, Kristin Dana
Published: 2025-10-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2510.08901v1

Change monitoring is an essential task for cranberry farming as it provides both breeders and growers with the ability to analyze growth, predict yield, and make treatment decisions. However, this task is often done manually, requiring significant time on the part of a cranberry grower or breeder. Deep learning based change monitoring holds promise, despite the caveat of hard-to-interpret high dimensional features and hand-annotations for fine-tuning. To address this gap, we introduce a method for modeling crop growth based on fine-tuning vision transformers (ViTs) using a self-supervised approach that avoids tedious image annotations. We use a two-fold pretext task (time regression and class prediction) to learn a latent space for the time-lapse evolution of plant and fruit appearance. The resulting 2D temporal tracks provide an interpretable time-series model of crop growth that can be used to: 1) predict growth over time and 2) distinguish temporal differences of cranberry varieties. We also provide a novel time-lapse dataset of cranberry fruit featuring eight distinct varieties, observed 52 times over the growing season (span of around four months), annotated with information about fungicide application, yield, and rot. Our approach is general and can be applied to other crops and applications (code and dataset can be found at https://github. com/ronan-39/tlt/).

---

### Pinpointing crucial steps: Attribution-based Credit Assignment for Verifiable Reinforcement Learning

Authors: Junxi Yin, Haisen Luo, Zhenyu Li, Yihua Liu, Dan Liu, Zequn Li, Xiaohang Xu
Published: 2025-10-10
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2510.08899v1

While Reinforcement Learning with Verifiable Rewards (RLVR) enhances complex reasoning in LLMs, current methods struggle to balance exploration and exploitation. This leads to critical issues like inaccurate credit assignment for intermediate steps and premature entropy collapse, limiting model performance. To address this, we introduce Attribution-based Contribution to Policy Optimization (ACPO), a phased framework that incorporates a difficulty-aware curriculum. ACPO improves exploration by using trajectory semantic segmentation and an attribution-based representation to dynamically regulate policy entropy, thus mitigating its collapse. Concurrently, it enhances exploitation with a factorized reward system that precisely quantifies the hierarchical contribution of each reasoning step, ensuring accurate credit assignment. Extensive experiments on challenging benchmarks, including AIME, MATH, and AMC, demonstrate that ACPO significantly outperforms existing state-of-the-art approaches.

---

### HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance

Authors: Suming Qiu, Jing Li, Zhicheng Zhou, Junjie Huang, Linyuan Qiu, Zhijie Sun
Published: 2025-10-10
Categories: cs.DB, cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2510.08896v1

We present HES-SQL, a novel hybrid training framework that advances Text-to-SQL generation through the integration of thinking-mode-fused supervised fine-tuning (SFT) with Group Relative Policy Optimization (GRPO). Our approach introduces three key innovations: (1) a skeleton-completeness scoring mechanism that enhances preference alignment between generated queries and optimal SQL structures; (2) a query-latency-aware reward system that incentivizes the generation of computationally efficient SQL queries; (3) a self-distillation process for thinking-mode completion that prevents degradation of the model's reasoning capabilities. This framework enables hybrid thinking models to switch between reasoning and non-reasoning modes while improving SQL query accuracy and execution efficiency. Experimental evaluation, conducted on MySQL 8.0 and SQLite 3.42 under controlled single-user conditions, demonstrates that HES-SQL achieves competitive performance with execution accuracies of 79.14\% and 54.9\% on the BIRD and KaggleDBQA benchmarks, respectively. Query latency is measured as the end-to-end execution time of generated queries on the DBMS, averaged over multiple runs to mitigate variance. Efficiency gains range from 11\% to 20\% relative to supervised baselines. Our results establish a new paradigm for Text-to-SQL systems that effectively balances semantic accuracy with computational efficiency through execution-informed reinforcement learning (RL). The proposed methodology has significant implications for developing robust natural language interfaces to databases and can be extended to broader structured generation tasks requiring both correctness and efficiency optimization.

---

### Exploring Multi-Temperature Strategies for Token- and Rollout-Level Control in RLVR

Authors: Haomin Zhuang, Yujun Zhou, Taicheng Guo, Yue Huang, Fangxu Liu, Kai Song, Xiangliang Zhang
Published: 2025-10-10
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2510.08892v1

Reinforcement Learning has demonstrated substantial improvements in the reasoning abilities of Large Language Models (LLMs), exhibiting significant applicability across various domains. Recent research has identified that tokens within LLMs play distinct roles during reasoning tasks, categorizing them into high-entropy reasoning tokens and low-entropy knowledge tokens. Prior approaches have typically focused on restricting updates to indirectly encourage exploration, yet they do not explicitly facilitate exploratory behavior during the token generation stage itself. In this work, we introduce a complementary approach that explicitly promotes exploration during sampling by applying distinct temperature settings for different token types. Specifically, our method employs higher temperatures for reasoning tokens to actively encourage exploration, while retaining lower temperatures for knowledge tokens to maintain factual correctness. Furthermore, we systematically investigate various multi-temperature scheduling strategies and their impacts within reinforcement learning contexts. Empirical evaluations on several reasoning benchmarks demonstrate that our approach significantly enhances the reasoning performance of LLMs. The code is available at https://github.com/zhmzm/Multi_Temperature_Verl.git.

---

### Designing and Evaluating an AI-enhanced Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education

Authors: Ruijie Wang, Jie Lu, Bo Pei, Evonne Jones, Jamey Brinson, Timothy Brown
Published: 2025-10-10
Categories: cs.ET, cs.AI, cs.HC
arXiv: https://arxiv.org/abs/2510.08891v2

Interprofessional education has long relied on case studies and the use of standardized patients to support teamwork, communication, and related collaborative competencies among healthcare professionals. However, traditional approaches are often limited by cost, scalability, and inability to mimic the dynamic complexity of real-world clinical scenarios. To address these challenges, we designed and developed AIMS (AI-enhanced Immersive Multidisciplinary Simulations), a virtual simulation that integrates a large language model (Gemini-2.5-Flash), a Unity-based virtual environment engine, and a character creation pipeline to support synchronized, multimodal interactions between the user and the virtual patient. AIMS was designed to enhance collaborative clinical reasoning and health promotion competencies among students from pharmacy, medicine, nursing, and social work. A formal usability testing session was conducted in which participants assumed professional roles on a healthcare team and engaged in a mix of scripted and unscripted conversations. Participants explored the patient's symptoms, social context, and care needs. Usability issues were identified (e.g., audio routing, response latency) and used to guide subsequent refinements. Findings suggest that AIMS supports realistic, profession-specific, and contextually appropriate conversations. We discuss technical innovations of AIMS and conclude with future directions.

---

### FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs

Authors: Yan Wang, Keyi Wang, Shanshan Yang, Jaisal Patel, Jeff Zhao, Fengran Mo, Xueqing Peng, Lingfei Qian, Yankai Chen, Víctor Gutiérrez-Basulto, Jimin Huang, Guojun Xiong et al.
Published: 2025-10-10
Categories: cs.CL, cs.CE, cs.IR
arXiv: https://arxiv.org/abs/2510.08886v3

Going beyond simple text processing, financial auditing requires detecting semantic, structural, and numerical inconsistencies across large-scale disclosures. As financial reports are filed in XBRL, a structured XML format governed by accounting standards, auditing becomes a structured information extraction and reasoning problem involving concept alignment, taxonomy-defined relations, and cross-document consistency. Although large language models (LLMs) show promise on isolated financial tasks, their capability in professional-grade auditing remains unclear. We introduce FinAuditing, a taxonomy-aligned, structure-aware benchmark built from real XBRL filings. It contains 1,102 annotated instances averaging over 33k tokens and defines three tasks: Financial Semantic Matching (FinSM), Financial Relationship Extraction (FinRE), and Financial Mathematical Reasoning (FinMR). Evaluations of 13 state-of-the-art LLMs reveal substantial gaps in concept retrieval, taxonomy-aware relation modeling, and consistent cross-document reasoning. These findings highlight the need for realistic, structure-aware benchmarks. We release the evaluation code at https://github.com/The-FinAI/FinAuditing and the dataset at https://huggingface.co/collections/TheFinAI/finauditing. The task currently serves as the official benchmark of an ongoing public evaluation contest at https://open-finance-lab.github.io/SecureFinAI_Contest_2026/.

---

### An Improved Model-Free Decision-Estimation Coefficient with Applications in Adversarial MDPs

Authors: Haolin Liu, Chen-Yu Wei, Julian Zimmert
Published: 2025-10-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2510.08882v2

We study decision making with structured observation (DMSO). Previous work (Foster et al., 2021b, 2023a) has characterized the complexity of DMSO via the decision-estimation coefficient (DEC), but left a gap between the regret upper and lower bounds that scales with the size of the model class. To tighten this gap, Foster et al. (2023b) introduced optimistic DEC, achieving a bound that scales only with the size of the value-function class. However, their optimism-based exploration is only known to handle the stochastic setting, and it remains unclear whether it extends to the adversarial setting. We introduce Dig-DEC, a model-free DEC that removes optimism and drives exploration purely by information gain. Dig-DEC is always no larger than optimistic DEC and can be much smaller in special cases. Importantly, the removal of optimism allows it to handle adversarial environments without explicit reward estimators. By applying Dig-DEC to hybrid MDPs with stochastic transitions and adversarial rewards, we obtain the first model-free regret bounds for hybrid MDPs with bandit feedback under several general transition structures, resolving the main open problem left by Liu et al. (2025). We also improve the online function-estimation procedure in model-free learning: For average estimation error minimization, we refine the estimator in Foster et al. (2023b) to achieve sharper concentration, improving their regret bounds from $T^{3/4}$ to $T^{2/3}$ (on-policy) and from $T^{5/6}$ to $T^{7/9}$ (off-policy). For squared error minimization in Bellman-complete MDPs, we redesign their two-timescale procedure, improving the regret bound from $T^{2/3}$ to $\sqrt{T}$. This is the first time a DEC-based method achieves performance matching that of optimism-based approaches (Jin et al., 2021; Xie et al., 2023) in Bellman-complete MDPs.

---

### ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling

Authors: Yuxuan Jiang, Zehua Chen, Zeqian Ju, Yusheng Dai, Weibei Dou, Jun Zhu
Published: 2025-10-10
Categories: cs.SD, cs.AI, cs.CL, eess.AS
arXiv: https://arxiv.org/abs/2510.08878v3

Text-to-audio (TTA) generation with fine-grained control signals, e.g., precise timing control or intelligible speech content, has been explored in recent works. However, constrained by data scarcity, their generation performance at scale is still compromised. In this study, we recast controllable TTA generation as a multi-task learning problem and introduce a progressive diffusion modeling approach, ControlAudio. Our method adeptly fits distributions conditioned on more fine-grained information, including text, timing, and phoneme features, through a step-by-step strategy. First, we propose a data construction method spanning both annotation and simulation, augmenting condition information in the sequence of text, timing, and phoneme. Second, at the model training stage, we pretrain a diffusion transformer (DiT) on large-scale text-audio pairs, achieving scalable TTA generation, and then incrementally integrate the timing and phoneme features with unified semantic representations, expanding controllability. Finally, at the inference stage, we propose progressively guided generation, which sequentially emphasizes more fine-grained information, aligning inherently with the coarse-to-fine sampling nature of DiT. Extensive experiments show that ControlAudio achieves state-of-the-art performance in terms of temporal accuracy and speech clarity, significantly outperforming existing methods on both objective and subjective evaluations. Demo samples are available at: https://control-audio.github.io/Control-Audio.

---

### Vector Graph-Based Repository Understanding for Issue-Driven File Retrieval

Authors: Kostiantyn Bevziuk, Andrii Fatula, Svetozar Lashin Yaroslav Opanasenko, Anna Tukhtarova, Ashok Jallepalli Pradeepkumar Sharma, Hritvik Shrivastava
Published: 2025-10-10
Categories: cs.SE, cs.AI
arXiv: https://arxiv.org/abs/2510.08876v1

We present a repository decomposition system that converts large software repositories into a vectorized knowledge graph which mirrors project architectural and semantic structure, capturing semantic relationships and allowing a significant level of automatization of further repository development. The graph encodes syntactic relations such as containment, implementation, references, calls, and inheritance, and augments nodes with LLM-derived summaries and vector embeddings. A hybrid retrieval pipeline combines semantic retrieval with graph-aware expansion, and an LLM-based assistant formulates constrained, read-only graph requests and produces human-oriented explanations.

---

### Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication

Authors: Benjamin Brock, Renato Golin
Published: 2025-10-10
Categories: cs.DC, cs.AI
arXiv: https://arxiv.org/abs/2510.08874v1

Many important applications across science, data analytics, and AI workloads depend on distributed matrix multiplication. Prior work has developed a large array of algorithms suitable for different problem sizes and partitionings including 1D, 2D, 1.5D, and 2.5D algorithms. A limitation of current work is that existing algorithms are limited to a subset of partitionings. Multiple algorithm implementations are required to support the full space of possible partitionings. If no algorithm implementation is available for a particular set of partitionings, one or more operands must be redistributed, increasing communication costs. This paper presents a universal one-sided algorithm for distributed matrix multiplication that supports all combinations of partitionings and replication factors. Our algorithm uses slicing (index arithmetic) to compute the sets of overlapping tiles that must be multiplied together. This list of local matrix multiplies can then either be executed directly, or reordered and lowered to an optimized IR to maximize overlap. We implement our algorithm using a high-level C++-based PGAS programming framework that performs direct GPU-to-GPU communication using intra-node interconnects. We evaluate performance for a wide variety of partitionings and replication factors, finding that our work is competitive with PyTorch DTensor, a highly optimized distributed tensor library targeting AI models.

---

### GTAlign: Game-Theoretic Alignment of LLM Assistants for Social Welfare

Authors: Siqi Zhu, David Zhang, Pedro Cisneros-Velarde, Jiaxuan You
Published: 2025-10-10
Categories: cs.AI, cs.GT, cs.HC, cs.LG, cs.MA
arXiv: https://arxiv.org/abs/2510.08872v3

Large Language Models (LLMs) have achieved remarkable progress in reasoning, yet sometimes produce responses that are suboptimal for users in tasks such as writing, information seeking, or providing practical guidance. Conventional alignment practices typically assume that maximizing model reward also maximizes user welfare, but this assumption frequently fails in practice: models may over-clarify or generate overly verbose reasoning when users prefer concise answers. Such behaviors resemble the prisoner's dilemma, where individually rational choices lead to socially suboptimal outcomes. The fundamental challenge is the lack of a principled decision making mechanism that mutually benefits both the LLM and the user. We propose Game-Theoretic Alignment (GTAlign), an alignment framework that integrates game-theoretic decision making into both reasoning and training. During reasoning, the model explicitly treats user-LLM interaction as a strategic game: it constructs payoff matrices within its reasoning chain to estimate welfare for both itself and the user, and then selects actions that are mutually beneficial. During training, we introduce a social welfare reward that reinforces cooperative responses, aligning model behavior with socially efficient outcomes. In addition, we introduce an inference technique that leverages game-theoretic reasoning to dynamically adapt LLM's response when pricing policies of LLM service change. Extensive experiments demonstrate that GTAlign substantially improves reasoning efficiency, answer quality, and social welfare compared to baselines across diverse tasks. The code is available at https://github.com/ulab-uiuc/GTAlign .

---

### Quality Estimation Reranking for Document-Level Translation

Authors: Krzysztof Mrozinski, Minji Kang, Ahmed Khota, Vincent Michael Sutanto, Giovanni Gatti De Giacomo
Published: 2025-10-10
Categories: cs.CL
arXiv: https://arxiv.org/abs/2510.08870v1

Quality estimation (QE) reranking is a form of quality-aware decoding which aims to improve machine translation (MT) by scoring and selecting the best candidate from a pool of generated translations. While known to be effective at the sentence level, its application to the increasingly prominent domain of document-level translation remains underexplored. In this work, we evaluate QE reranking performance on document-level (rather than the typical sentence-level) translation, using various learned and large language model (LLM)-based QE metrics. We find that with our best learned metric, SLIDE, BLEURT-20 scores improve by +2.00 with only two candidates, and by +5.09 with 32, across both decoder-only LLM models and encoder-decoder neural machine translation (NMT) models. Using the best LLM-based metric, GEMBA-DA, gains of +1.63 and +4.30 are achieved under the same conditions. Although gains shrink with longer inputs, reranking with 32 candidates yields improvements of +2.34 (SLIDE) and +1.40 (GEMBA-DA) on our longest documents (512-1024 source tokens). These findings demonstrate the practical value of document-level QE, with minimal runtime overhead given suitable translation models and hardware.

---

### VeriPy -- A New Python-Based Approach for SDR Pipelined/Unrolled Hardware Accelerator Generation

Authors: Yuqin Zhao, Linghui Ye, Haihang Xia, Luke Seed, Tiantai Deng
Published: 2025-10-09
Categories: cs.AR, cs.CL
arXiv: https://arxiv.org/abs/2512.00006v1

Software-defined radio (SDR) plays an important role in the communication field by providing a flexible and customized communication system for different purposes according to the needs. To enhance the performance of SDR applications, hardware accelerators have been widely deployed in recent years. In facing this obstacle, a necessity arises for a high-level synthesis (HLS) tool specifically designed for communication engineers without detailed hardware knowledge. To lower the barrier between SDR engineers and hardware development, this work proposed a Python-based HLS tool, VeriPy, which can generate both mainstream architecture for hardware accelerators in Verilog specifically for SDR designs including unrolled design and pipelined design, requiring no detailed digital hardware knowledge or Hardware Description Languages (HDL). Furthermore, VeriPy supports automatic testbench generation with random input stimulus, an extensible hardware library, performance and resource estimation, and offers strong optimisation potential at both the algorithmic and digital hardware levels. The generated hardware design by VeriPy can achieve up to 70% faster operating frequency compared to pragma-optimised Vivado HLS designs with a reasonably higher resource con-sumption while delivering comparable performance and resource consumption to hand-coded implementations. Regarding code complexity, VeriPy requires no pragmas, completely eliminating the need for low-level hardware knowledge. For straightforward algorithms, the input code length remains comparable to that of Vivado HLS.

---

### Production-Grade Local LLM Inference on Apple Silicon: A Comparative Study of MLX, MLC-LLM, Ollama, llama.cpp, and PyTorch MPS

Authors: Varun Rajesh, Om Jodhpurkar, Pooja Anbuselvan, Mantinder Singh, Ashok Jallepali, Shantanu Godbole, Pradeep Kumar Sharma, Hritvik Shrivastava
Published: 2025-10-09
Categories: cs.AR, cs.AI
arXiv: https://arxiv.org/abs/2511.05502v1

We present a systematic, empirical evaluation of five local large language model (LLM) runtimes on Apple Silicon: MLX, MLC-LLM, llama.cpp, Ollama, and PyTorch MPS. Experiments were conducted on a Mac Studio equipped with an M2 Ultra processor and 192 GB of unified memory. Using the Qwen-2.5 model family across prompts ranging from a few hundred to 100,000 tokens, we measure time-to-first-token (TTFT), steady-state throughput, latency percentiles, long-context behavior (key-value and prompt caching), quantization support, streaming performance, batching and concurrency behavior, and deployment complexity. Under our settings, MLX achieves the highest sustained generation throughput, while MLC-LLM delivers consistently lower TTFT for moderate prompt sizes and offers stronger out-of-the-box inference features. llama.cpp is highly efficient for lightweight single-stream use, Ollama emphasizes developer ergonomics but lags in throughput and TTFT, and PyTorch MPS remains limited by memory constraints on large models and long contexts. All frameworks execute fully on-device with no telemetry, ensuring strong privacy guarantees. We release scripts, logs, and plots to reproduce all results. Our analysis clarifies the design trade-offs in Apple-centric LLM deployments and provides evidence-based recommendations for interactive and long-context processing. Although Apple Silicon inference frameworks still trail NVIDIA GPU-based systems such as vLLM in absolute performance, they are rapidly maturing into viable, production-grade solutions for private, on-device LLM inference.

---

### Efficient Deployment of CNN Models on Multiple In-Memory Computing Units

Authors: Eleni Bougioukou, Theodore Antonakopoulos
Published: 2025-10-09
Categories: cs.AR, cs.AI
arXiv: https://arxiv.org/abs/2511.04682v1

In-Memory Computing (IMC) represents a paradigm shift in deep learning acceleration by mitigating data movement bottlenecks and leveraging the inherent parallelism of memory-based computations. The efficient deployment of Convolutional Neural Networks (CNNs) on IMC-based hardware necessitates the use of advanced task allocation strategies for achieving maximum computational efficiency. In this work, we exploit an IMC Emulator (IMCE) with multiple Processing Units (PUs) for investigating how the deployment of a CNN model in a multi-processing system affects its performance, in terms of processing rate and latency. For that purpose, we introduce the Load-Balance-Longest-Path (LBLP) algorithm, that dynamically assigns all CNN nodes to the available IMCE PUs, for maximizing the processing rate and minimizing latency due to efficient resources utilization. We are benchmarking LBLP against other alternative scheduling strategies for a number of CNN models and experimental results demonstrate the effectiveness of the proposed algorithm.

---

### A Multiscale Approach for Enhancing Weak Signal Detection

Authors: Dixon Vimalajeewa, Ursula U. Muller, Brani Vidakovic
Published: 2025-10-09
Categories: eess.SP, cs.LG, stat.CO, stat.ME
arXiv: https://arxiv.org/abs/2510.20828v1

Stochastic resonance (SR), a phenomenon originally introduced in climate modeling, enhances signal detection by leveraging optimal noise levels within non-linear systems. Traditional SR techniques, mainly based on single-threshold detectors, are limited to signals whose behavior does not depend on time. Often large amounts of noise are needed to detect weak signals, which can distort complex signal characteristics. To address these limitations, this study explores multi-threshold systems and the application of SR in multiscale applications using wavelet transforms. In the multiscale domain signals can be analyzed at different levels of resolution to better understand the underlying dynamics. We propose a double-threshold detection system that integrates two single-threshold detectors to enhance weak signal detection. We evaluate it both in the original data domain and in the multiscale domain using simulated and real-world signals and compare its performance with existing methods. Experimental results demonstrate that, in the original data domain, the proposed double-threshold detector significantly improves weak signal detection compared to conventional single-threshold approaches. Its performance is further improved in the frequency domain, requiring lower noise levels while outperforming existing detection systems. This study advances SR-based detection methodologies by introducing a robust approach to weak signal identification, with potential applications in various disciplines.

---