arXiv cs.AI 20251007 论文分析报告

📊 数据统计概览

📈基本统计

论文总数: 130
分析分类: cs.AI
时间范围: 20251007
独立作者数: 733

👥高产作者 Top 10

Rui Liu (3 篇)
James Zou (3 篇)
Tao Zhe (2 篇)
Dongjie Wang (2 篇)
Tian Xia (2 篇)
Shuo Yang (2 篇)
Aditya Desai (2 篇)
Matei Zaharia (2 篇)
Ion Stoica (2 篇)
Fabrizio Dimino (2 篇)

🔍热门关键词 Top 10

language (62 次)
data (49 次)
llms (46 次)
learning (43 次)
reasoning (38 次)
approaches (23 次)
agents (22 次)
detection (22 次)
diffusion (21 次)
series (20 次)

🤖 AI 深度分析

arXiv cs.AI 论文分析报告

期间: 2025年10月07日 | 论文总数: 108

1. 研究方向热度分析

LLM Applications & Reasoning

49 篇

核心技术: llm, large language model, reasoning

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

Multimodality (Vision, Speech, etc.)

28 篇

核心技术: multimodal, vision, speech, audio, image, vla, visual

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

Agentic AI & Multi-Agent Systems (MAS)

21 篇

核心技术: agent, agentic, multi-agent

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

AI for Science & Specific Domains

19 篇

核心技术: scientific, medical, health, radiography, cardiac, drug, financial, legal, biology, protein, astronomical, cyber-physical

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

Model Optimization & Efficiency

16 篇

核心技术: efficient, scaling, optimization, quantization, sparse, distillation, federated

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

Generative Models & Diffusion

15 篇

核心技术: generative, diffusion, gan, synthesis

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

AI Security & Privacy

10 篇

核心技术: security, privacy, vulnerability, adversarial, jailbreak, misinformation, spoofing, fingerprinting, auditing

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

Embodied AI & Robotics

7 篇

核心技术: embodied, robot, uav, navigation, mobility

简述: 该方向专注于利用AI解决特定领域问题，探索新范式和新应用。

总结与趋势

本次分析显示，LLM应用与推理 和 Agentic AI 依然是当前最热门的研究焦点，占据了论文总数的绝大部分。这表明研究界正致力于深化大型模型的推理能力、自主性及多智能体协作。同时，多模态学习 和 AI在特定科学领域的应用（如医疗、金融）也表现出强劲的增长势头。模型优化与效率 以及 AI安全 同样是不可或缺的研究方向，为AI技术的可持续发展和可靠部署提供了保障。未来，我们预测多模态、具备复杂推理能力的Agentic AI将在更多垂直领域产生颠覆性影响。

2. 作者关系图谱

高产作者

Rui Liu 3
Dongjie Wang 2
Ion Stoica 2
Tian Xia 2
Aditya Desai 2
Shuo Yang 2
Matei Zaharia 2
Tao Zhe 2
James Zou 2
Pan Lu 2
Yu Liu 2
Lorenzo Baraldi 2
Changyuan Zhao 1
Ruichen Zhang 1
Jiacheng Wang 1

合作网络图谱 (部分)

graph TD; subgraph Prominent Collaboration Clusters; direction LR; A_AdityaDesai["Aditya Desai (2)"]; A_JamesZou["James Zou (2)"]; A_ShuoYang["Shuo Yang (2)"]; A_JiachengWang["Jiacheng Wang (1)"]; A_PanLu["Pan Lu (2)"]; A_ChangyuanZhao["Changyuan Zhao (1)"]; A_LorenzoBaraldi["Lorenzo Baraldi (2)"]; A_RuichenZhang["Ruichen Zhang (1)"]; A_RuiLiu["Rui Liu (3)"]; A_TaoZhe["Tao Zhe (2)"]; A_DongjieWang["Dongjie Wang (2)"]; A_IonStoica["Ion Stoica (2)"]; A_MateiZaharia["Matei Zaharia (2)"]; A_TianXia["Tian Xia (2)"]; A_YuLiu["Yu Liu (2)"]; ShuoYang --- AdityaDesai; IonStoica --- AdityaDesai; MateiZaharia --- AdityaDesai; IonStoica --- ShuoYang; MateiZaharia --- ShuoYang; IonStoica --- MateiZaharia; DongjieWang --- TaoZhe; RuiLiu --- TaoZhe; DongjieWang --- RuiLiu; PanLu --- JamesZou; end

图表说明: 上图展示了本次统计中最高产的作者及其合作关系网络。节点代表作者，括号内数字为其发表论文数量。连线表示两位作者之间存在合作关系。这有助于我们识别核心的研究团队和合作枢纽。

3. 技术创新总结

From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions

创新点: This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques, .

4. 论文完整列表

标题	作者	摘要
From Agentification to Self-Evolving Agentic AI for Wireless Networks Concepts, Approaches, and Future Research Directions	Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Dusit Niyato, Geng Sun, Xianbin Wang, Shiwen Mao, Abbas Jamalipour	Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems by enabling autonomous agents to continually adapt and improve without human intervention. Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and workflows in response to environmental dynamics. This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques, ...
Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography	Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos, Christiane Kuhl, Daniel Truhn, Sven Nebelung	Objectives To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To a...
Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring An Attention-Based In-Context Learning Approach	Yousef Emami, Seyedsina Nabavirazavi, Jingjing Zheng, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida	Recently, Unmanned Aerial Vehicles (UAVs) are increasingly being investigated to collect sensory data in post-disaster monitoring scenarios, such as tsunamis, where early actions are critical to limit coastal damage. A major challenge is to design the data collection schedules and flight velocities, as unfavorable schedules and velocities can lead to transmission errors and buffer overflows of the ground sensors, ultimately resulting in significant packet loss. Meanwhile, online Deep Reinforceme...
Syn-Diag An LLM-based Synergistic Framework for Generalizable Few-shot Fault Diagnosis on the Edge	Zijun Jia, Shuang Liang, Jinsong Yu	Industrial fault diagnosis faces the dual challenges of data scarcity and the difficulty of deploying large AI models in resource-constrained environments. This paper introduces Syn-Diag, a novel cloud-edge synergistic framework that leverages Large Language Models to overcome these limitations in few-shot fault diagnosis. Syn-Diag is built on a three-tiered mechanism 1) Visual-Semantic Synergy, which aligns signal features with the LLM's semantic space through cross-modal pre-training; 2) Cont...
ConstraintLLM A Neuro-Symbolic Framework for Industrial-Level Constraint Programming	Weichun Shi, Minghao Liu, Wanting Zhang, Langchen Shi, Fuqi Jia, Feifei Ma, Jian Zhang	Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving efficiency. Using large language models (LLMs) to generate formal modeling automatically for COPs is becoming a promising approach, which aims to build trustworthy neuro-symbolic AI with the help of symbolic solvers. However, CP has received less attention compared to works based on operations research (OR) models. We ...
Optimizing for Persuasion Improves LLM Generalization Evidence from Quality-Diversity Evolution of Debate Strategies,	Aksel Joonas Reedi, Corentin Léger, Julien Pourcel, Loris Gaven, Perrine Charriau, Guillaume Pourcel	Large Language Models (LLMs) optimized to output truthful answers often overfit, producing brittle reasoning that fails to generalize. While persuasion-based optimization has shown promise in debate settings, it has not been systematically compared against mainstream truth-based approaches. We introduce DebateQD, a minimal Quality-Diversity (QD) evolutionary algorithm that evolves diverse debate strategies across different categories (rationality, authority, emotional appeal, etc.) through tourn...,
Training-Free Time Series Classification via In-Context Reasoning with LLM Agents,	Songyuan Sui, Zihang Xu, Yu-Neng Chuang, Kwei-Herng Lai, Xia Hu	Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and inflexible. Recent reasoning-oriented large language models (LLMs) show promise in understanding temporal patterns, but purely zero-shot usage remains suboptimal. We propose FETA, a multi-agent framework for training-free TSC via exemplar-based in-context reasoning. FETA decomposes a multivariate series into channel-wise subproblems, retrieves a few st...,
ARISE An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models,	Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng, Zhiyuan Yu, Qipeng Guo, Xuanjing Huang, Xipeng Qiu	Test-time scaling has emerged as a transformative paradigm for enhancing the performance of large reasoning models, enabling dynamic allocation of computational resources during inference. However, as the landscape of reasoning models rapidly expands, a critical question remains how can we systematically compare and evaluate the test-time scaling capabilities across different models? In this paper, we introduce ARISE (Adaptive Resolution-aware Scaling Evaluation), a novel metric specifically de...,
Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research,	Gang Liu, Yihan Zhu, Jie Chen, Meng Jiang	Large language models hold promise as scientific assistants, yet existing agents either rely solely on algorithm evolution or on deep research in isolation, both of which face critical limitations. Pure algorithm evolution, as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly plateaus in complex domains, while pure deep research proposes ideas without validation, resulting in unrealistic or unimplementable solutions. We present DeepEvolve, an agent that integrates deep r...,
Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents,	Tao Zhe, Rui Liu, Fateme Memar, Xiao Luo, Wei Fan, Xinyue Ye, Zhongren Peng, Dongjie Wang	Route recommendation aims to provide users with optimal travel plans that satisfy diverse and complex requirements. Classical routing algorithms (e.g., shortest-path and constraint-aware search) are efficient but assume structured inputs and fixed objectives, limiting adaptability to natural-language queries. Recent LLM-based approaches enhance flexibility but struggle with spatial reasoning and the joint modeling of route-level and POI-level preferences. To address these limitations, we propose...,
Classical AI vs. LLMs for Decision-Maker Alignment in Health Insurance Choices,	Mallika Mainali, Harsha Sureshbabu, Anik Sen, Christopher B. Rauch, Noah D. Reifsnyder, John Meyer, J. T. Turner, Michael W. Floyd, Matthew Molineaux, Rosina O. Weber	As algorithmic decision-makers are increasingly applied to high-stakes domains, AI alignment research has evolved from a focus on universal value alignment to context-specific approaches that account for decision-maker attributes. Prior work on Decision-Maker Alignment (DMA) has explored two primary strategies (1) classical AI methods integrating case-based reasoning, Bayesian reasoning, and naturalistic decision-making, and (2) large language model (LLM)-based methods leveraging prompt enginee...,
Pushing Test-Time Scaling Limits of Deep Search with Asymmetric Verification,	Weihao Zeng, Keqing He, Chuqiao Kuang, Xiaoguang Li, Junxian He	Test-time compute can be scaled both sequentially and in parallel. Sequential scaling involves lengthening the generation process, while parallel scaling involves verifying and selecting among multiple candidate outputs. Combining these two strategies has led to the most powerful AI systems, such as Grok 4 Heavy and GPT-5 Pro. In certain contexts (e.g., solving Sudoku puzzles), verifying responses can be substantially easier than generating them. This property, referred to as \emph{asymmetric ve...,
Barbarians at the Gate How AI is Upending Systems Research,	Audrey Cheng, Shu Liu, Melissa Pan, Zhifei Li, Bowen Wang, Alex Krentsel, Tian Xia, Mert Cemri, Jongseok Park, Shuo Yang, Jeff Chen, Lakshya Agrawal, Aditya Desai, Jiarong Xing, Koushik Sen, Matei Zaharia, Ion Stoica	Artificial Intelligence (AI) is starting to transform the research process as we know it by automating the discovery of new solutions. Given a task, the typical AI-driven approach is (i) to generate a set of diverse solutions, and then (ii) to verify these solutions and select one that solves the problem. Crucially, this approach assumes the existence of a reliable verifier, i.e., one that can accurately determine whether a solution solves the given problem. We argue that systems research, long ...,
Requirements for Game-Based Learning Design Framework for Information System Integration in the Context of Post-Merger Integration,	Ksenija Lace, Marite Kirikova	Post-merger integration states unique challenges for professionals responsible for information system integration aimed on alignment and combination diverse system architectures of merging organizations. Although the theoretical and practical guidance exists for post-merger integration on the business level, there is a significant gap in training for information system integration in this context. In prior research specific methods AMILI (Support method for informed decision identification) and ...,
Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks,	Wentao Deng, Jiahuan Pei, Zhiwei Xu, Zhaochun Ren, Zhumin Chen, Pengjie Ren	A multi-agent system (MAS) enhances its capacity to solve complex natural language processing (NLP) tasks through collaboration among multiple agents, where consensus-seeking serves as a fundamental mechanism. However, existing consensus-seeking approaches typically rely on voting mechanisms to judge consensus, overlooking contradictions in system-internal beliefs that destabilize the consensus. Moreover, these methods often involve agents updating their results through indiscriminate collaborat...,
Off-Trajectory Reasoning Can LLMs Collaborate on Reasoning Trajectory?,	Aochong Oliver Li, Tanya Goyal	Reasoning LLMs are trained to verbalize their reasoning process, yielding strong gains on complex tasks. This transparency also opens a promising direction multiple reasoners can directly collaborate on each other's thinking within a shared trajectory, yielding better inference efficiency and exploration. A key prerequisite, however, is the ability to assess the usefulness and build on another model's partial thinking -- we call this off-trajectory reasoning. Our paper investigates a critical q...,
Flavonoid Fusion Creating a Knowledge Graph to Unveil the Interplay Between Food and Health,	Aryan Singh Dalal, Yinglun Zhang, Duru Doğan, Atalay Mert İleri, Hande Küçük McGinty	The focus on "food as medicine" is gaining traction in the field of health and several studies conducted in the past few years discussed this aspect of food in the literature. However, very little research has been done on representing the relationship between food and health in a standardized, machine-readable format using a semantic web that can help us leverage this knowledge effectively. To address this gap, this study aims to create a knowledge graph to link food and health through the know...,
Vul-R2 A Reasoning LLM for Automated Vulnerability Repair	Xin-Cheng Wen, Zirui Lin, Yijun Yang, Cuiyun Gao, Deheng Ye	The exponential increase in software vulnerabilities has created an urgent need for automatic vulnerability repair (AVR) solutions. Recent research has formulated AVR as a sequence generation problem and has leveraged large language models (LLMs) to address this problem. Typically, these approaches prompt or fine-tune LLMs to generate repairs for vulnerabilities directly. Although these methods show state-of-the-art performance, they face the following challenges (1) Lack of high-quality, vulne...
LANTERN Scalable Distillation of Large Language Models for Job-Person Fit and Explanation	Zhoutong Fu, Yihan Cao, Yi-Lin Chen, Aman Lunia, Liming Dong, Neha Saraf, Ruijie Jiang, Yun Dai, Qingquan Song, Tan Wang, Guoyao Li, Derek Koh, Haichao Wei, Zhipeng Wang, Aman Gupta, Chengming Jiang, Jianqiang Shen, Liangjie Hong, Wenjing Zhang	Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applications, such as job-person fit and explanation in job seeking platforms, introduces distinct challenges. At LinkedIn, the job person fit task requires analyzing a candidate's public profile against job requirements to produce both a fit assessment and a detailed explanation. Directly applying open source or finetuned LL...
High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training	Zhuoyi Huang, Nutan Sahoo, Anamika Kumari, Girish Kumar, Kexuan Cai, Shixing Cao, Yue Kang, Tian Xia, Somya Chatterjee, Nicholas Hausman, Aidan Jay, Eric S. Rosenthal, Soundar Srinivasan, Sadid Hasan, Alex Fedorov, Sulaiman Vesal	The development of machine learning for cardiac care is severely hampered by privacy restrictions on sharing real patient electrocardiogram (ECG) data. Although generative AI offers a promising solution, the real-world use of existing model-synthesized ECGs is limited by persistent gaps in trustworthiness and clinical utility. In this work, we address two major shortcomings of current generative ECG methods insufficient morphological fidelity and the inability to generate personalized, patient-...
CAM A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension	Rui Li, Zeyu Zhang, Xiaohe Bo, Zihang Tian, Xu Chen, Quanyu Dai, Zhenhua Dong, Ruiming Tang	Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents. Despite the emergence of some heuristic approaches, a systematic design principle remains absent. To fill this void, we draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured s...
Provably Mitigating Corruption, Overoptimization, and Verbosity Simultaneously in Offline and Online RLHF/DPO Alignment	Ziyi Chen, Junyi Li, Peiran Yu, Heng Huang	Reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) are important techniques to align large language models (LLM) with human preference. However, the quality of RLHF and DPO training is seriously compromised by \textit{\textbf{C}orrupted} preference, reward \textit{\textbf{O}veroptimization}, and bias towards \textit{\textbf{V}erbosity}. To our knowledge, most existing works tackle only one of these important issues, and the few other works require much com...
Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection	Rui Liu, Tao Zhe, Yanjie Fu, Feng Xia, Ted Senator, Dongjie Wang	Feature selection eliminates redundancy among features to improve downstream task performance while reducing computational overhead. Existing methods often struggle to capture intricate feature interactions and adapt across diverse application scenarios. Recent advances employ generative intelligence to alleviate these drawbacks. However, these methods remain constrained by permutation sensitivity in embedding and reliance on convexity assumptions in gradient-based search. To address these limit...
Seeing the Big Picture Evaluating Multimodal LLMs' Ability to Interpret and Grade Handwritten Student Work	Owen Henkel, Bill Roberts, Doug Jaffe, Laurence Holt	Recent advances in multimodal large language models (MLLMs) raise the question of their potential for grading, analyzing, and offering feedback on handwritten student classwork. This capability would be particularly beneficial in elementary and middle-school mathematics education, where most work remains handwritten, because seeing students' full working of a problem provides valuable insights into their learning processes, but is extremely time-consuming to grade. We present two experiments inv...
Decade-long Emission Forecasting with an Ensemble Model in Taiwan	Gordon Hung, Salinna Abdullah	Taiwan's high population and heavy dependence on fossil fuels have led to severe air pollution, with the most prevalent greenhouse gas being carbon dioxide (CO2). There-fore, this study presents a reproducible and comprehensive case study comparing 21 of the most commonly employed time series models in forecasting emissions, analyzing both univariate and multivariate approaches. Among these, Feedforward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest Regressor (RFR) achiev...
Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection	Sheng Xiang, Yidong Jiang, Yunting Chen, Dawei Cheng, Guoping Zhao, Changjun Jiang	Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focus on isolated node features, often overlooking the broader context of interconnected nodes. Graph-based techniques, particularly Graph Neural Networks (GNNs), have advanced the field by leveraging relational information effectively. However, in real-world spoofing detection datasets, trading behaviors exhibit dynamic, ...
Mission Impossible Feedback-Guided Dynamic Interactive Planning for Improving Reasoning on LLMs	Dong Yan, Gaochen Wu, Bowen Zhou	Recent advancements in language agents have led to significant improvements in multi-hop reasoning tasks. However, existing approaches often struggle with handling open-domain problems, which require massive information retrieval due to their reliance on a fixed sequence of actions. To address this, we propose Feedback-Guided Dynamic Interactive Planning (FGDIP), a novel framework tailored to enhance reasoning in LLMs by utilizing dynamic and adaptive strategies for information exploration in op...
MetaVLA Unified Meta Co-training For Efficient Embodied Adaption	Chen Li, Zhantao Yang, Han Zhang, Fangyi Chen, Chenchen Zhu, Anudeepsekhar Bolimera, Marios Savvides	Vision-Language-Action (VLA) models show promise in embodied reasoning, yet remain far from true generalists-they often require task-specific fine-tuning, and generalize poorly to unseen tasks. We propose MetaVLA, a unified, backbone-agnostic post-training framework for efficient and scalable alignment. MetaVLA introduces Context-Aware Meta Co-Training, which consolidates diverse target tasks into a single fine-tuning stage while leveraging structurally diverse auxiliary tasks to improve in-doma...
Deciphering Invariant Feature Decoupling in Source-free Time Series Forecasting with Proxy Denoising	Kangjia Yan, Chenxi Liu, Hao Miao, Xinle Wu, Yan Zhao, Chenjuan Guo, Bin Yang	The proliferation of mobile devices generates a massive volume of time series across various domains, where effective time series forecasting enables a variety of real-world applications. This study focuses on a new problem of source-free domain adaptation for time series forecasting. It aims to adapt a pretrained model from sufficient source time series to the sparse target time series domain without access to the source data, embracing data protection regulations. To achieve this, we propose T...
AgentDR Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents	Mingdai Yang, Nurendra Choudhary, Jiangshu Du, Edward W. Huang, Philip S. Yu, Karthik Subbian, Danai Kourta	Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-existent items and full-catalog ranking. Besides, a largely underexplored opportunity lies in leveraging LLMs'commonsense reasoning to capture user intent through substitute and complement relationships between items, which are usually implicit in datasets and difficult for traditional ID-based recommenders to capture. I...
AutoPentester An LLM Agent-based Framework for Automated Pentesting	Yasod Ginige, Akila Niroshan, Sajal Jain, Suranga Seneviratne	Penetration testing and vulnerability assessment are essential industry practices for safeguarding computer systems. As cyber threats grow in scale and complexity, the demand for pentesting has surged, surpassing the capacity of human professionals to meet it effectively. With advances in AI, particularly Large Language Models (LLMs), there have been attempts to automate the pentesting process. However, existing tools such as PentestGPT are still semi-manual, requiring significant professional h...
HOI-R1 Exploring the Potential of Multimodal Large Language Models for Human-Object Interaction Detection	Junwen Chen, Peilin Xiong, Keiji Yanai	Recent Human-object interaction detection (HOID) methods highly require prior knowledge from VLMs to enhance the interaction recognition capabilities. The training strategies and model architectures for connecting the knowledge from VLMs to the HOI instance representations from the object detector are challenging, and the whole framework is complex for further development or application. On the other hand, the inherent reasoning abilities of MLLMs on human-object interaction detection are under-...
MADIAVE Multi-Agent Debate for Implicit Attribute Value Extraction	Wei-Chieh Huang, Cornelia Caragea	Implicit Attribute Value Extraction (AVE) is essential for accurately representing products in e-commerce, as it infers lantent attributes from multimodal data. Despite advances in multimodal large language models (MLLMs), implicit AVE remains challenging due to the complexity of multidimensional data and gaps in vision-text understanding. In this work, we introduce \textsc{\modelname}, a multi-agent debate framework that employs multiple MLLM agents to iteratively refine inferences. Through a s...
The African Languages Lab A Collaborative Approach to Advancing Low-Resource African NLP	Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim, Lieqi Liu, Erick Rosas Gonzalez, Sylvester Kpei, Jemimah Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, Saadia Gabriel	Despite representing nearly one-third of the world's languages, African languages remain critically underserved by modern NLP technologies, with 88\% classified as severely underrepresented or completely ignored in computational linguistics. We present the African Languages Lab (All Lab), a comprehensive research initiative that addresses this technological gap through systematic data collection, model development, and capacity building. Our contributions include (1) a quality-controlled data c...
Ocular-Induced Abnormal Head Posture Diagnosis and Missing Data Imputation	Saja Al-Dabet, Sherzod Turaev, Nazar Zaki, Arif O. Khan, Luai Eldweik	Ocular-induced abnormal head posture (AHP) is a compensatory mechanism that arises from ocular misalignment conditions, such as strabismus, enabling patients to reduce diplopia and preserve binocular vision. Early diagnosis minimizes morbidity and secondary complications such as facial asymmetry; however, current clinical assessments remain largely subjective and are further complicated by incomplete medical records. This study addresses both challenges through two complementary deep learning fr...
Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models	David Debot, Giuseppe Marra	Concept Bottleneck Models (CBNMs) are deep learning models that provide interpretability by enforcing a bottleneck layer where predictions are based exclusively on human-understandable concepts. However, this constraint also restricts information flow and often results in reduced predictive accuracy. Concept Sidechannel Models (CSMs) address this limitation by introducing a sidechannel that bypasses the bottleneck and carry additional task-relevant information. While this improves accuracy, it s...
Code-Switching In-Context Learning for Cross-Lingual Transfer of Large Language Models	Haneul Yoo, Jiho Jin, Kyunghyun Cho, Alice Oh	While large language models (LLMs) exhibit strong multilingual abilities, their reliance on English as latent representations creates a translation barrier, where reasoning implicitly depends on internal translation into English. When this process fails, performance in non-English languages deteriorates sharply, limiting the inclusiveness of LLM-based applications. Existing cross-lingual in-context learning (X-ICL) methods primarily leverage monolingual demonstrations, often failing to mitigate ...
QGraphLIME - Explaining Quantum Graph Neural Networks	Haribandhu Jena, Jyotirmaya Shivottam, Subhankar Mishra	Quantum graph neural networks offer a powerful paradigm for learning on graph-structured data, yet their explainability is complicated by measurement-induced stochasticity and the combinatorial nature of graph structure. In this paper, we introduce QuantumGraphLIME (QGraphLIME), a model-agnostic, post-hoc framework that treats model explanations as distributions over local surrogates fit on structure-preserving perturbations of a graph. By aggregating surrogate attributions together with their d...
vAttention Verified Sparse Attention	Aditya Desai, Kumar Krishna Agrawal, Shuo Yang, Alejandro Cuadron, Luis Gaspar Schroeder, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica	State-of-the-art sparse attention methods for reducing decoding latency fall into two main categories approximate top-$k$ (and its extension, top-$p$) and recently introduced sampling-based estimation. However, these approaches are fundamentally limited in their ability to approximate full attention they fail to provide consistent approximations across heads and query vectors and, most critically, lack guarantees on approximation quality, limiting their practical deployment. We observe that to...
Membership Inference Attacks on Tokenizers of Large Language Models	Meng Tong, Yuntao Du, Kejiang Chen, Weiming Zhang, Ninghui Li	Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these attacks are applied to pre-trained large language models (LLMs), they encounter significant challenges, including mislabeled samples, distribution shifts, and discrepancies in model size between experimental and real-world settings. To address these limitations, we introduce tokenizers as a new attack vector for membership inference. Specifically, a tokeniz...
Uncovering Representation Bias for Investment Decisions in Open-Source Large Language Models	Fabrizio Dimino, Krati Saxena, Bhaskarjit Sarmah, Stefano Pasquali	Large Language Models are increasingly adopted in financial applications to support investment workflows. However, prior studies have seldom examined how these models reflect biases related to firm size, sector, or financial characteristics, which can significantly impact decision-making. This paper addresses this gap by focusing on representation bias in open-source Qwen models. We propose a balanced round-robin prompting method over approximately 150 U.S. equities, applying constrained decodin...
FinReflectKG - EvalBench Benchmarking Financial KG with Multi-Dimensional Evaluation	Fabrizio Dimino, Abhinav Arun, Bhaskarjit Sarmah, Stefano Pasquali	Large language models (LLMs) are increasingly being used to extract structured knowledge from unstructured financial text. Although prior studies have explored various extraction methods, there is no universal benchmark or unified evaluation framework for the construction of financial knowledge graphs (KG). We introduce FinReflectKG - EvalBench, a benchmark and evaluation framework for KG extraction from SEC 10-K filings. Building on the agentic and holistic evaluation principles of FinReflectKG...
Redefining Generalization in Visual Domains A Two-Axis Framework for Fake Image Detection with FusionDetect	Amirtaha Amanzadi, Zahra Dehghanian, Hamid Beigy, Hamid R. Rabiee	The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images. Although most of the work has now focused on cross-generator generalization, we argue that this viewpoint is too limited. Detecting synthetic images involves another equally important challenge generalization across visual domains. To bridge this gap,we present the OmniGen Benchmark. This comprehensive evaluation dataset incorporates 12 state-of-the-art gen...
Artificially intelligent agents in the social and behavioral sciences A history and outlook	Petter Holme, Milena Tsvetkova	We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific...
Are Heterogeneous Graph Neural Networks Truly Effective? A Causal Perspective	Xiao Yang, Xuejiao Zhao, Zhiqi Shen	Graph neural networks (GNNs) have achieved remarkable success in node classification. Building on this progress, heterogeneous graph neural networks (HGNNs) integrate relation types and node and edge semantics to leverage heterogeneous information. Causal analysis for HGNNs is advancing rapidly, aiming to separate genuine causal effects from spurious correlations. However, whether HGNNs are intrinsically effective remains underexamined, and most studies implicitly assume rather than establish th...
Uncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport	Jeffrey N. Clark, Elena Fillola, Nawid Keshtmand, Raul Santos-Rodriguez, Matthew Rigby	Monitoring greenhouse gas emissions and evaluating national inventories require efficient, scalable, and reliable inference methods. Top-down approaches, combined with recent advances in satellite observations, provide new opportunities to evaluate emissions at continental and global scales. However, transport models used in these methods remain a key source of uncertainty they are computationally expensive to run at scale, and their uncertainty is difficult to characterise. Artificial intellig...
Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit A Time-Window Analysis	Sedat Dogan, Nina Dethlefs, Debarati Chakraborty	Predicting the virality of online content remains challenging, especially for culturally complex, fast-evolving memes. This study investigates the feasibility of early prediction of meme virality using a large-scale, cross-lingual dataset from 25 diverse Reddit communities. We propose a robust, data-driven method to define virality based on a hybrid engagement score, learning a percentile-based threshold from a chronologically held-out training set to prevent data leakage. We evaluated a suite o...
RareAgent Self-Evolving Reasoning for Drug Repurposing in Rare Diseases	Lang Qin, Zijian Gan, Xu Cao, Pengcheng Jiang, Yankai Jiang, Jiawei Han, Kaishun Wu, Jintai Chen	Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in wh...
InforME Improving Informativeness of Abstractive Text Summarization With Informative Attention Guided by Named Entity Salience	Jianbin Shen, Christy Jie Liang, Junyu Xuan	Abstractive text summarization is integral to the Big Data era, which demands advanced methods to turn voluminous and often long text data into concise but coherent and informative summaries for efficient human consumption. Despite significant progress, there is still room for improvement in various aspects. One such aspect is to improve informativeness. Hence, this paper proposes a novel learning approach consisting of two methods an optimal transport-based informative attention method to impr...
Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images	Sven Koehler, Sarah Kaye Mueller, Jonathan Kiekenap, Gerald Greil, Tarique Hussain, Samir Sarikouch, Florian André, Norbert Frey, Sandy Engelhardt	Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac function, but individual cardiac cycles complicate automatic temporal comparison or sub-phase analysis. Accurate cardiac keyframe detection can eliminate this problem. However, automatic methods solely derive end-systole (ES) and end-diastole (ED) frames from left ventricular volume curves, which do not provide a deeper insight into myocardial motion. We propose a self-supervised deep learning method detecting fiv...
Centering Emotion Hotspots Multimodal Local-Global Fusion and Cross-Modal Alignment for Emotion Recognition in Conversations	Yu Liu, Hanlei Shi, Haoxun Li, Yuqing Sun, Yuxuan Ding, Linlin Gong, Leyuan Qu, Taihao Li	Emotion Recognition in Conversations (ERC) is hard because discriminative evidence is sparse, localized, and often asynchronous across modalities. We center ERC on emotion hotspots and present a unified model that detects per-utterance hotspots in text, audio, and video, fuses them with global features via Hotspot-Gated Fusion, and aligns modalities using a routed Mixture-of-Aligners; a cross-modal graph encodes conversational structure. This design focuses modeling on salient spans, mitigates m...
MMA-ASIA A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation	Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen	Large language models (LLMs) are now used worldwide, yet their multimodal understanding and reasoning often degrade outside Western, high-resource settings. We propose MMA-ASIA, a comprehensive framework to evaluate LLMs' cultural awareness with a focus on Asian contexts. MMA-ASIA centers on a human-curated, multilingual, and multimodally aligned multiple-choice benchmark covering 8 Asian countries and 10 languages, comprising 27,000 questions; over 79 percent require multi-step reasoning ground...
Impact of LLMs on Team Collaboration in Software Development	Devang Dhanuka	Large Language Models (LLMs) are increasingly being integrated into software development processes, with the potential to transform team workflows and productivity. This paper investigates how LLMs affect team collaboration throughout the Software Development Life Cycle (SDLC). We reframe and update a prior study with recent developments as of 2025, incorporating new literature and case studies. We outline the problem of collaboration hurdles in SDLC and explore how LLMs can enhance productivity...
Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique	Yanming Li, Seifeddine Ghozzi, Cédric Eichler, Nicolas Anciaux, Alexandra Bensamoun, Lorena Gonzalez Manzano	We address the problem of auditing whether sensitive or copyrighted texts were used to fine-tune large language models (LLMs) under black-box access. Prior signals-verbatim regurgitation and membership inference-are unreliable at the level of individual documents or require altering the visible text. We introduce a text-preserving watermarking framework that embeds sequences of invisible Unicode characters into documents. Each watermark is split into a cue (embedded in odd chunks) and a reply (e...
Adversarial-Resilient RF Fingerprinting A CNN-GAN Framework for Rogue Transmitter Detection	Raju Dhakal, Prashant Shekhar, Laxima Niure Kandel	Radio Frequency Fingerprinting (RFF) has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network (CNN) based framework for detecting rogue devices and identifying genuine ones using softmax probability thresholding. We emulate an attack scenario in which adversaries attempt to mimic the RF characteristics of genuine devices by traini...
The Role of Federated Learning in Improving Financial Security A Survey	Cade Houston Kennedy, Amr Hilal, Morteza Momeni	With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional machine learning models have shown to be effective in fraud detections, they often compromise user data by requiring centralized access to sensitive information. In IoT-enabled financial endpoints such as ATMs and POS Systems that regularly produce sensitive data that is sent over the network. Federated Learning (FL) offers a privacy-preserving, dece...
GAZE Governance-Aware pre-annotation for Zero-shot World Model Environments	Leela Krishna, Mengyang Zhao, Saicharithreddy Pasula, Harshit Rajgarhia, Abhishek Mukherji	Training robust world models requires large-scale, precisely labeled multimodal datasets, a process historically bottlenecked by slow and expensive manual annotation. We present a production-tested GAZE pipeline that automates the conversion of raw, long-form video into rich, task-ready supervision for world-model training. Our system (i) normalizes proprietary 360-degree formats into standard views and shards them for parallel processing; (ii) applies a suite of AI models (scene understanding, ...
AMAQ Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning	Yurun Song, Zhuoyi Yang, Ian G. Harris, Sangeetha Abdu Jyothi	Large Language Models (LLMs) are scaling rapidly, creating significant challenges for collaborative server client distributed training, particularly in terms of communication efficiency and computational overheads. To address these challenges, we implement Parameter-efficient Split Learning, which effectively balances efficiency and performance for collaborative training on low-resource devices. To reduce communication overhead in collaborative training, we introduce Adaptive Mixed bit Activat...
Orders in Chaos Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting	Zhongkai Yu, Yue Guan, Zihao Yu, Chenyang Zhou, Shuyi Pei, Yangwook Kang, Yufei Ding, Po-An Tsai	Large Language Models (LLMs) with Mixture of Experts (MoE) architectures achieve remarkable performance improvements, but their random expert selection mechanism introduces significant data movement overhead that becomes the dominant bottleneck in multi-unit serving systems. To forecast the patterns underlying this data movement, we conduct comprehensive data-movement-centric profiling across three state-of-the-art large-scale MoE models (200B- 671B) using over 24,000 requests spanning diverse w...
Critical attention scaling in long-context transformers	Shi Chen, Zhengjiang Lin, Yury Polyanskiy, Philippe Rigollet	As large language models scale to longer contexts, attention layers suffer from a fundamental pathology attention scores collapse toward uniformity as context length $n$ increases, causing tokens to cluster excessively, a phenomenon known as rank-collapse. While $\textit{attention scaling}$ effectively addresses this deficiency by rescaling attention scores with a polylogarithmic factor $\beta_n$, theoretical justification for this approach remains lacking. We analyze a simplified yet tractab...
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use	Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu	Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios. Agentic systems offer a promising alternative by decomposing work across specialized modules, yet most remain training-free or rely on offline training decoupled from the li...
Improving Chain-of-Thought Efficiency for Autoregressive Image Generation	Zeqi Gu, Markos Georgopoulos, Xiaoliang Dai, Marjan Ghazvininejad, Chu Wang, Felix Juefei-Xu, Kunpeng Li, Yujun Shi, Zecheng He, Zijian He, Jiawei Zhou, Abe Davis, Jialiang Wang	Autoregressive multimodal large language models have recently gained popularity for image generation, driven by advances in foundation models. To enhance alignment and detail, newer approaches employ chain-of-thought (CoT) reasoning, expanding user inputs into elaborated prompts prior to image synthesis. However, this strategy can introduce unnecessary redundancy -- a phenomenon we call visual overthinking -- which increases computational costs and can introduce details that contradict the origi...
PointNSP Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction	Ziqiao Meng, Qichao Wang, Zhiyang Dou, Zixing Song, Zhipeng Zhou, Irwin King, Peilin Zhao	Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an artificial ordering on inherently unordered point sets, forcing shape generation to proceed as a sequence of local predictions. This sequential bias emphasizes short-range continuity but undermines the model's capacity to capture long-range dependencies, hindering its ability to enforce global structural properties such a...
Beyond Spectral Peaks Interpreting the Cues Behind Synthetic Image Detection	Sara Mandelli, Diego Vila-Portela, David Vázquez-Padín, Paolo Bestagini, Fernando Pérez-González	Over the years, the forensics community has proposed several deep learning-based detectors to mitigate the risks of generative AI. Recently, frequency-domain artifacts (particularly periodic peaks in the magnitude spectrum), have received significant attention, as they have been often considered a strong indicator of synthetic image generation. However, state-of-the-art detectors are typically used as black-boxes, and it still remains unclear whether they truly rely on these peaks. This limits t...
From Neural Activity to Computation Biological Reservoirs for Pattern Recognition in Digit Classification	Ludovico Iannello, Luca Ciampi, Fabrizio Tonelli, Gabriele Lagani, Lucio Maria Calcagnile, Federico Cremisi, Angelo Di Garbo, Giuseppe Amato	In this paper, we present a biologically grounded approach to reservoir computing (RC), in which a network of cultured biological neurons serves as the reservoir substrate. This system, referred to as biological reservoir computing (BRC), replaces artificial recurrent units with the spontaneous and evoked activity of living neurons. A multi-electrode array (MEA) enables simultaneous stimulation and readout across multiple sites inputs are delivered through a subset of electrodes, while the rema...
Verifier-free Test-Time Sampling for Vision Language Action Models	Suhyeok Jang, Dongyoung Kim, Changyeon Kim, Youngsuk Kim, Jinwoo Shin	Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require additional training and fail to generalize to unseen conditions. We propose Masking Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the ...
D2E Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI	Suwhan Choi, Jaeyoon Jung, Haebin Seong, Minchan Kim, Minyeong Kim, Yongjun Cho, Yoonshik Kim, Yubeen Park, Youngjae Yu, Yunsung Lee	Large language models leverage internet-scale text data, yet embodied AI remains constrained by the prohibitive costs of physical trajectory collection. Desktop environments -- particularly gaming -- offer a compelling alternative they provide rich sensorimotor interactions at scale while maintaining the structured observation-action coupling essential for embodied learning. We present D2E (Desktop to Embodied AI), a framework that demonstrates desktop interactions can serve as an effective pre...
Sparse deepfake detection promotes better disentanglement	Antoine Teissier, Marie Tahon, Nicolas Dugué, Aghilas Sini	Due to the rapid progress of speech synthesis, deepfake detection has become a major concern in the speech processing community. Because it is a critical task, systems must not only be efficient and robust, but also provide interpretable explanations. Among the different approaches for explainability, we focus on the interpretation of latent representations. In such paper, we focus on the last layer of embeddings of AASIST, a deepfake detection architecture. We use a TopK activation inspired by ...
Towards Reliable and Practical LLM Security Evaluations via Bayesian Modelling	Mary Llewellyn, Annie Gray, Josh Collyer, Michael Harries	Before adopting a new large language model (LLM) architecture, it is critical to understand vulnerabilities accurately. Existing evaluations can be difficult to trust, often drawing conclusions from LLMs that are not meaningfully comparable, relying on heuristic inputs or employing metrics that fail to capture the inherent uncertainty. In this paper, we propose a principled and practical end-to-end framework for evaluating LLM vulnerabilities to prompt injection attacks. First, we propose practi...
Improving Discrete Diffusion Unmasking Policies Beyond Explicit Reference Policies	Chunsan Hong, Seonho An, Min-Soo Kim, Jong Chul Ye	Masked diffusion models (MDMs) have recently emerged as a novel framework for language modeling. MDMs generate sentences by iteratively denoising masked sequences, filling in [MASK] tokens step by step. Although MDMs support any-order sampling, performance is highly sensitive to the choice of which position to unmask next. Prior work typically relies on rule-based schedules (e.g., max-confidence, max-margin), which provide ad hoc improvements. In contrast, we replace these heuristics with a lear...
ARM Discovering Agentic Reasoning Modules for Generalizable Multi-Agent Systems	Bohan Yao, Shiva Krishna Reddy Malay, Vikas Yadav	Large Language Model (LLM)-powered Multi-agent systems (MAS) have achieved state-of-the-art results on various complex reasoning tasks. Recent works have proposed techniques to automate the design of MASes, eliminating the need for manual engineering. However, these techniques perform poorly, often achieving similar or inferior performance to simple baselines. Furthermore, they require computationally expensive re-discovery of architectures for each new task domain and expensive data annotation ...
Mellum Production-Grade in-IDE Contextual Code Completion with Multi-File Project Understanding	Nikita Pavlichenko, Iurii Nazarov, Ivan Dolgov, Ekaterina Garanina, Dmitry Ustalov, Ivan Bondyrev, Kseniia Lysaniuk, Evgeniia Vu, Kirill Chekmenev, Joseph Shtok, Yaroslav Golubev, Anton Semenkin, Uladzislau Sazanovich	We present the Mellum models family, open-weight code completion models designed for interactive use in JetBrains IDEs. Mellums have 4B parameters, adopt a Llama-style architecture, and are pre-trained on ~4T tokens of permissively licensed, multi-language code. Our studies show that (i) careful data curation and staged training significantly improve the model's quality, (ii) editor-critical capabilities such as context packing are necessary for high-quality suggestions, and (iii) a compact, tas...
Data-efficient Targeted Token-level Preference Optimization for LLM-based Text-to-Speech	Rikuto Kotoge, Yuichi Sasaki	Aligning text-to-speech (TTS) system outputs with human feedback through preference optimization has been shown to effectively improve the robustness and naturalness of language model-based TTS models. Current approaches primarily require paired desirable and undesirable samples at the utterance level. However, such pairs are often limited in TTS output data, and utterance-level formulation prevents fine-grained token-level optimization needed for accurate pronunciation alignment. In this study,...
Risk level dependent Minimax Quantile lower bounds for Interactive Statistical Decision Making	Raghav Bongole, Amirreza Zamani, Tobias J. Oechtering, Mikael Skoglund	Minimax risk and regret focus on expectation, missing rare failures critical in safety-critical bandits and reinforcement learning. Minimax quantiles capture these tails. Three strands of prior work motivate this study minimax-quantile bounds restricted to non-interactive estimation; unified interactive analyses that focus on expected risk rather than risk level specific quantile bounds; and high-probability bandit bounds that still lack a quantile-specific toolkit for general interactive proto...
Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling	Giorgio Giannone, Guangxuan Xu, Nikhil Shivakumar Nayak, Rohan Mahesh Awhad, Shivchander Sudalairaj, Kai Xu, Akash Srivastava	Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time. Particle Filtering (PF) has emerged as a strong ITS method for complex mathematical reasoning tasks, but it is vulnerable when guided by process reward models, which often assign overconfident scores early in the reasoning process. This causes PF to suffer from premature exploitation it myopically commits to locally promising trajectories, prunes potentially correct hypotheses, and converges...
DACP Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarization	Xue-Yong Fu, Elena Khasanova, Md Tahmid Rahman Laskar, Harsh Saini, Shashi Bhushan TN	Large language models (LLMs) have achieved impressive performance in text summarization, yet their performance often falls short when applied to specialized domains that differ from their original pre-training distribution. While fine-tuning can improve summarization quality, it typically relies on costly and scarce high-quality labeled data. In this work, we explore continual pre-training as a scalable, self-supervised approach to adapt LLMs for downstream summarization tasks, particularly in t...
The Safety Challenge of World Models for Embodied AI Agents A Review	Lorenzo Baraldi, Zifan Zeng, Chongzhe Zhang, Aradhana Nayak, Hongbo Zhu, Feng Liu, Qunli Zhang, Peng Wang, Shiming Liu, Zheng Hu, Angelo Cangelosi, Lorenzo Baraldi	The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental dynamics. In this context, World Models (WMs) have been introduced to provide embodied agents with the abilities to anticipate future environmental states and fill in knowledge gaps, thereby enhancing agents' ability to plan and execute actions. However, when dealing with embodied agents it is fundamental to ensure th...
Untangling Component Imbalance in Hybrid Linear Attention Conversion Methods	Martin Benfeghoul, Teresa Delgado, Adnan Oomerjee, Haitham Bou Ammar, Jun Wang, Zafeirios Fountas	Transformers' quadratic computational complexity limits their scalability despite remarkable performance. While linear attention reduces this to linear complexity, pre-training such models from scratch remains, in most cases, prohibitively expensive. Recent post-training linearisation methods convert pre-trained Transformers to linear models efficiently, often using hybrid approaches that combine linear attention with sliding-window softmax. We identify a critical flaw existing hybrid methods i...
Kaputt A Large-Scale Dataset for Visual Defect Detection	Sebastian Höfer, Dorian Henning, Artemij Amiranashvili, Douglas Morrison, Mariliza Tzes, Ingmar Posner, Marc Matvienko, Alessandro Rennola, Anton Milan	We present a novel large-scale dataset for defect detection in a logistics setting. Recent work on industrial anomaly detection has primarily focused on manufacturing scenarios with highly controlled poses and a limited number of object categories. Existing benchmarks like MVTec-AD [6] and VisA [33] have reached saturation, with state-of-the-art methods achieving up to 99.9% AUROC scores. In contrast to manufacturing, anomaly detection in retail logistics faces new challenges, particularly in th...
Carré du champ flow matching better quality-generalisation tradeoff in generative models	Jacob Bamberger, Iolo Jones, Dennis Duncan, Michael M. Bronstein, Pierre Vandergheynst, Adam Gosztolai	Deep generative models often face a fundamental tradeoff high sample quality can come at the cost of memorisation, where the model reproduces training data rather than generalising across the underlying data geometry. We introduce Carr\'e du champ flow matching (CDC-FM), a generalisation of flow matching (FM), that improves the quality-generalisation tradeoff by regularising the probability path with a geometry-aware noise. Our method replaces the homogeneous, isotropic noise in FM with a spati...
Gaussian Embeddings How JEPAs Secretly Learn Your Data Density	Randall Balestriero, Nicolas Ballas, Mike Rabbat, Yann LeCun	Joint Embedding Predictive Architectures (JEPAs) learn representations able to solve numerous downstream tasks out-of-the-box. JEPAs combine two objectives (i) a latent-space prediction term, i.e., the representation of a slightly perturbed sample must be predictable from the original sample's representation, and (ii) an anti-collapse term, i.e., not all samples should have the same representation. While (ii) is often considered as an obvious remedy to representation collapse, we uncover that J...
Diffusion Models for Low-Light Image Enhancement A Multi-Perspective Taxonomy and Performance Analysis	Eashan Adhikarla, Yixin Liu, Brian D. Davison	Low-light image enhancement (LLIE) is vital for safety-critical applications such as surveillance, autonomous navigation, and medical imaging, where visibility degradation can impair downstream task performance. Recently, diffusion models have emerged as a promising generative paradigm for LLIE due to their capacity to model complex image distributions via iterative denoising. This survey provides an up-to-date critical analysis of diffusion models for LLIE, distinctively featuring an in-depth c...
ECTSpeech Enhancing Efficient Speech Synthesis via Easy Consistency Tuning	Tao Zhu, Yinfeng Yu, Liejun Wang, Fuchun Sun, Wendong Zheng	Diffusion models have demonstrated remarkable performance in speech synthesis, but typically require multi-step sampling, resulting in low inference efficiency. Recent studies address this issue by distilling diffusion models into consistency models, enabling efficient one-step generation. However, these approaches introduce additional training costs and rely heavily on the performance of pre-trained teacher models. In this paper, we propose ECTSpeech, a simple and effective one-step speech synt...
Deterministic Legal Retrieval An Action API for Querying the SAT-Graph RAG	Hudson de Martim	The Structure-Aware Temporal Graph RAG (SAT-Graph RAG) addresses core limitations of standard Retrieval-Augmented Generation in the legal domain by providing a verifiable knowledge graph that models hierarchical structure, temporal evolution, and causal events of legal norms. However, a critical gap remains how to reliably query this structured knowledge without sacrificing its deterministic properties. This paper introduces the SAT-Graph API, a formal query execution layer centered on canonica...
Emergent AI Surveillance Overlearned Person Re-Identification and Its Mitigation in Law Enforcement Context	An Thi Nguyen, Radina Stoykova, Eric Arazo	Generic instance search models can dramatically reduce the manual effort required to analyze vast surveillance footage during criminal investigations by retrieving specific objects of interest to law enforcement. However, our research reveals an unintended emergent capability through overlearning, these models can single out specific individuals even when trained on datasets without human subjects. This capability raises concerns regarding identification and profiling of individuals based on th...
Controllable Audio-Visual Viewpoint Generation from 360° Spatial Information	Christian Marinoni, Riccardo Fosco Gramaccioni, Eleonora Grassucci, Danilo Comminiello	The generation of sounding videos has seen significant advancements with the advent of diffusion models. However, existing methods often lack the fine-grained control needed to generate viewpoint-specific content from larger, immersive 360-degree environments. This limitation restricts the creation of audio-visual experiences that are aware of off-camera events. To the best of our knowledge, this is the first work to introduce a framework for controllable audio-visual generation, addressing this...
TelecomTS A Multi-Modal Observability Dataset for Time Series and Language Analysis	Austin Feng, Andreas Varvarigos, Ioannis Panitsas, Daniela Fernandez, Jinbiao Wei, Yuwei Guo, Jialin Chen, Ali Maatouk, Leandros Tassiulas, Rex Ying	Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as weather, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets are underrepresented in public benchmarks due to proprietary restrictions. Existing datasets are often anonymized and normalized, removing scale information and limit...
Benchmark It Yourself (BIY) Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks	João Palmeiro, Diogo Duarte, Rita Costa, Pedro Bizarro	AI models are increasingly used for data analysis and visualization, yet benchmarks rarely address scatterplot-specific tasks, limiting insight into performance. To address this gap for one of the most common chart types, we introduce a synthetic, annotated dataset of over 18,000 scatterplots from six data generators and 17 chart designs, and a benchmark based on it. We evaluate proprietary models from OpenAI and Google using N-shot prompting on five distinct tasks derived from annotations of cl...
Moloch's Bargain Emergent Misalignment When LLMs Compete for Audiences	Batu El, James Zou	Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive advertisements, to election campaigns optimizing messaging to gain votes, to social media influencers boosting engagement. These settings are inherently competitive, with sellers, candidates, and influencers vying for audience approval, yet it remains poorly understood how competitive feedback loops influence LLM behavior. We show that optimizing LLMs f...
Distributional Semantics Tracing A Framework for Explaining Hallucinations in Large Language Models	Gagan Bhatia, Somayajulu G Sripada, Kevin Allan, Jacobo Azcona	Large Language Models (LLMs) are prone to hallucination, the generation of plausible yet factually incorrect statements. This work investigates the intrinsic, architectural origins of this failure mode through three primary contributions. First, to enable the reliable tracing of internal semantic failures, we propose Distributional Semantics Tracing (DST), a unified framework that integrates established interpretability techniques to produce a causal map of a model's reasoning, treating meaning ...
Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images	Aditya Prakash, David Forsyth, Saurabh Gupta	We tackle the problem of forecasting bimanual 3D hand motion & articulation from a single image in everyday settings. To address the lack of 3D hand annotations in diverse settings, we design an annotation pipeline consisting of a diffusion model to lift 2D hand keypoint sequences to 4D hand motion. For the forecasting model, we adopt a diffusion loss to account for the multimodality in hand motion distribution. Extensive experiments across 6 datasets show the benefits of training on diverse dat...
LLMs as Policy-Agnostic Teammates A Case Study in Human Proxy Design for Heterogeneous Agent Teams	Aju Ani Justus, Chris Baber	A critical challenge in modelling Heterogeneous-Agent Teams is training agents to collaborate with teammates whose policies are inaccessible or non-stationary, such as humans. Traditional approaches rely on expensive human-in-the-loop data, which limits scalability. We propose using Large Language Models (LLMs) as policy-agnostic human proxies to generate synthetic data that mimics human decision-making. To evaluate this, we conduct three experiments in a grid-world capture game inspired by Stag...
Smartphone-based iris recognition through high-quality visible-spectrum iris image capture.V2	Naveenkumar G Venkataswamy, Yu Liu, Soumyabrata Dey, Stephanie Schuckers, Masudul H Imtiaz	Smartphone-based iris recognition in the visible spectrum (VIS) remains difficult due to illumination variability, pigmentation differences, and the absence of standardized capture controls. This work presents a compact end-to-end pipeline that enforces ISO/IEC 29794-6 quality compliance at acquisition and demonstrates that accurate VIS iris recognition is feasible on commodity devices. Using a custom Android application performing real-time framing, sharpness evaluation, and feedback, we introd...
Automated Program Repair of Uncompilable Student Code	Griffin Pitts, Aum Pandya, Darsh Rank, Tirth Bhatt, Muntasir Hoq, Bita Akram	A significant portion of student programming submissions in CS1 learning environments are uncompilable, limiting their use in student modeling and downstream knowledge tracing. Traditional modeling pipelines often exclude these cases, discarding observations of student learning. This study investigates automated program repair as a strategy to recover uncompilable code while preserving students' structural intent for use in student modeling. Within this framework, we assess large language models...
BanglaTalk Towards Real-Time Speech Assistance for Bengali Regional Dialects	Jakir Hasan, Shubhashis Roy Dipta	Real-time speech assistants are becoming increasingly popular for ensuring improved accessibility to information. Bengali, being a low-resource language with a high regional dialectal diversity, has seen limited progress in developing such systems. Existing systems are not optimized for real-time use and focus only on standard Bengali. In this work, we present BanglaTalk, the first real-time speech assistance system for Bengali regional dialects. BanglaTalk follows the client-server architecture...
Latent Speech-Text Transformer	Yen-Ju Lu, Yashesh Gaur, Wei Zhou, Benjamin Muller, Jesus Villalba, Najim Dehak, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Srinivasan Iyer, Duc Le	Auto-regressive speech-text models are typically pre-trained on a large number of interleaved sequences of text tokens and raw speech encoded as speech tokens using vector quantization. These models have demonstrated state-of-the-art performance in speech-to-speech understanding and generation benchmarks, together with promising scaling laws, primarily enabled by the representational alignment between text and speech. Nevertheless, they suffer from shortcomings, partly owing to the disproportion...
StarEmbed Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars	Weijian Li, Hong-Yu Chen, Qinjie Lin, Nabeel Rehemtulla, Ved G. Shah, Dennis Wu, Adam A. Miller, Han Liu	Time series foundation models (TSFMs) are increasingly being adopted as highly-capable general-purpose time series representation learners. Although their training corpora are vast, they exclude astronomical time series data. Observations of stars produce peta-scale time series with unique challenges including irregular sampling and heteroskedasticity. We introduce StarEmbed, the first public benchmark for rigorous and standardized evaluation of state-of-the-art TSFMs on stellar time series obse...
TokenChain A Discrete Speech Chain via Semantic Token Modeling	Mingxuan Wang, Satoshi Nakamura	Machine Speech Chain, simulating the human perception-production loop, proves effective in jointly improving ASR and TTS. We propose TokenChain, a fully discrete speech chain coupling semantic-token ASR with a two-stage TTS an autoregressive text-to-semantic model co-trained with ASR and a masked-generative semantic-to-acoustic model for synthesis only. End-to-end feedback across the text interface is enabled with straight-through argmax/Gumbel-Softmax and balanced with supervised ASR via dynam...
Stratified GRPO Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents	Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao, Jiaya Jia	Large language model (LLM) agents increasingly rely on external tools such as search engines to solve complex, multi-step problems, and reinforcement learning (RL) has become a key paradigm for training them. However, the trajectories of search agents are structurally heterogeneous, where variations in the number, placement, and outcomes of search calls lead to fundamentally different answer directions and reward distributions. Standard policy gradient methods, which use a single global baseline...
TaTToo Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning	Jiaru Zou, Soumya Roy, Vinay Kumar Verma, Ziyi Wang, David Wipf, Pan Lu, Sumit Negi, James Zou, Jingrui He	Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. Through detailed empirical analyses, we identify that existing PRMs, though widely adopted for supervising text-only reasoning steps, struggle with table-specific operations such as sub-table retr...
Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs	Bang Chen, Lijun Guo, Houli Fan, Wentao He, Rong Zhang	Identifying cancer driver genes (CDGs) is essential for understanding cancer mechanisms and developing targeted therapies. Graph neural networks (GNNs) have recently been employed to identify CDGs by capturing patterns in biological interaction networks. However, most GNN-based approaches rely on a single protein-protein interaction (PPI) network, ignoring complementary information from other biological networks. Some studies integrate multiple networks by aligning features with consistency cons...
Efficient High-Resolution Image Editing with Hallucination-Aware Loss and Adaptive Tiling	Young D. Kwon, Abhinav Mehrotra, Malcolm Chadwick, Alberto Gil Ramos, Sourav Bhattacharya	High-resolution (4K) image-to-image synthesis has become increasingly important for mobile applications. Existing diffusion models for image editing face significant challenges, in terms of memory and image quality, when deployed on resource-constrained devices. In this paper, we present MobilePicasso, a novel system that enables efficient image editing at high resolutions, while minimising computational cost and memory usage. MobilePicasso comprises three stages (i) performing image editing at...
Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systems	Fikret Mert Gultekin, Oscar Lilja, Ranim Khojah, Rebekka Wohlrab, Marvin Damschen, Mazen Mohamad	In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In many software teams, cybersecurity experts are either entirely absent or represented by only a small number of specialists. As a result, the workload for these experts becomes high, and software engineers would need to conduct cybersecurity activities themselves. This creates a need for a tool to support cybersecurity experts and engineers in evaluating vulnerab...
Flexible Swarm Learning May Outpace Foundation Models in Essential Tasks	Moein E. Samadi, Andreas Schuppert	Foundation models have rapidly advanced AI, raising the question of whether their decisions will ultimately surpass human strategies in real-world domains. The exponential, and possibly super-exponential, pace of AI development makes such analysis elusive. Nevertheless, many application areas that matter for daily life and society show only modest gains so far; a prominent case is diagnosing and treating dynamically evolving disease in intensive care. The common challenge is adapting complex s...
TransFIRA Transfer Learning for Face Image Recognizability Assessment	Allen Tu, Kartik Narayan, Joshua Gleason, Jennifer Xu, Matthew Meyn, Tom Goldstein, Vishal M. Patel	Face recognition in unconstrained environments such as surveillance, video, and web imagery must contend with extreme variation in pose, blur, illumination, and occlusion, where conventional visual quality metrics fail to predict whether inputs are truly recognizable to the deployed encoder. Existing FIQA methods typically rely on visual heuristics, curated annotations, or computationally intensive generative pipelines, leaving their predictions detached from the encoder's decision geometry. We ...
Relational Transformer Toward Zero-Shot Foundation Models for Relational Data	Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos Kanatsoulis, Roshan Upendra, Mahmoud Mohammadi, Joe Meyer, Tom Palczewski, Carlos Guestrin, Jure Leskovec	Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and tasks. The core challenge is the diversity of relational data, with varying heterogeneous schemas, graph structures and functional dependencies. In this paper, we present the Relational Transformer (RT) architecture, which can be pretrained on diverse relational databases and directly applied to unseen datasets and tasks w...
Adaptive Protein Design Protocols and Middleware	Aymen Alsaadi, Jonathan Ash, Mikhail Titov, Matteo Turilli, Andre Merzky, Shantenu Jha, Sagar Khare	Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-learning for Protein Structures at Scale (IMPRESS) offers methods and advanced computing systems for coupling AI to high-performance computing ...
Context-Aware Inference via Performance Forecasting in Decentralized Learning Networks	Joel Pfeffer, J. M. Diederik Kruijssen, Clément Gossart, Mélanie Chevance, Diego Campo Millan, Florian Stecker, Steven N. Longmore	In decentralized learning networks, predictions from many participants are combined to generate a network inference. While many studies have demonstrated performance benefits of combining multiple model predictions, existing strategies using linear pooling methods (ranging from simple averaging to dynamic weight updates) face a key limitation. Dynamic prediction combinations that rely on historical performance to update weights are necessarily reactive. Due to the need to average over a reasonab...
A Survey on Agentic Security Applications, Threats and Defenses	Asif Shahriar, Md Nafiu Rahman, Sadif Ahmed, Farig Sadeque, Md Rizwan Parvez	The rapid shift from passive LLMs to autonomous LLM-agents marks a new paradigm in cybersecurity. While these agents can act as powerful tools for both offensive and defensive operations, the very agentic context introduces a new class of inherent security risks. In this work we present the first holistic survey of the agentic security landscape, structuring the field around three interdependent pillars Applications, Threats, and Defenses. We provide a comprehensive taxonomy of over 150 papers,...
Deep Generative Model for Human Mobility Behavior	Ye Hong, Yatao Zhang, Konrad Schindler, Martin Raubal	Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite decades of effort, simulating individual mobility remains challenging because of its complex, context-dependent, and exploratory nature. Here, we present MobilityGen, a deep generative model that produces realistic mobility trajectories spanning days to weeks at large spatial scales. By linking behavioral attributes with environmental context, MobilityGen...
A Median Perspective on Unlabeled Data for Out-of-Distribution Detection	Momin Abbas, Ali Falahati, Hossein Goli, Mohammad Mohammadi Amiri	Out-of-distribution (OOD) detection plays a crucial role in ensuring the robustness and reliability of machine learning systems deployed in real-world applications. Recent approaches have explored the use of unlabeled data, showing potential for enhancing OOD detection capabilities. However, effectively utilizing unlabeled in-the-wild data remains challenging due to the mixed nature of both in-distribution (InD) and OOD samples. The lack of a distinct set of OOD samples complicates the task of t...
Visualizing Multimodality in Combinatorial Search Landscapes	Xavier F. C. Sánchez-Díaz, Ole Jakob Mengshoel	This work walks through different visualization techniques for combinatorial search landscapes, focusing on multimodality. We discuss different techniques from the landscape analysis literature, and how they can be combined to provide a more comprehensive view of the search landscape. We also include examples and discuss relevant work to show how others have used these techniques in practice, based on the geometric and aesthetic elements of the Grammar of Graphics. We conclude that there is no f...
Local MAP Sampling for Diffusion Models	Shaorong Zhang, Rob Brekelmans, Greg Ver Steeg	Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from $p(x_0 \mid y)$. However, in practice, the goal of inverse problem solving is not to cover the posterior but to recover the most accurate reconstruction, where optimization-based diffusion solvers often excel despite lacking a clear probabilistic foundation. We introduce Local MAP Sampling (LMAPS), a new inference framework that iteratively solving local MAP subproblems along the diffu...
Mitigating Surgical Data Imbalance with Dual-Prediction Video Diffusion Model	Danush Kumar Venkatesh, Adam Schmidt, Muhammad Abdullah Jamal, Omid Mohareri	Surgical video datasets are essential for scene understanding, enabling procedural modeling and intra-operative support. However, these datasets are often heavily imbalanced, with rare actions and tools under-represented, which limits the robustness of downstream models. We address this challenge with $SurgiFlowVid$, a sparse and controllable video diffusion framework for generating surgical videos of under-represented classes. Our approach introduces a dual-prediction diffusion module that join...
Mnemosyne An Unsupervised, Human-Inspired Long-Term Memory Architecture for Edge-Based LLMs	Aneesh Jonelagadda, Christina Hahn, Haoze Zheng, Salvatore Penachio	Long-term memory is essential for natural, realistic dialogue. However, current large language model (LLM) memory systems rely on either brute-force context expansion or static retrieval pipelines that fail on edge-constrained devices. We introduce Mnemosyne, an unsupervised, human-inspired long-term memory architecture designed for edge-based LLMs. Our approach uses graph-structured storage, modular substance and redundancy filters, memory committing and pruning mechanisms, and probabilistic re...
LatentBreak Jailbreaking Large Language Models through Latent Space Feedback	Raffaele Mura, Giorgio Piras, Kamilė Lukošiūtė, Maura Pintor, Amin Karbasi, Battista Biggio	Jailbreaks are adversarial attacks designed to bypass the built-in safety mechanisms of large language models. Automated jailbreaks typically optimize an adversarial suffix or adapt long prompt templates by forcing the model to generate the initial part of a restricted or harmful response. In this work, we show that existing jailbreak attacks that leverage such mechanisms to unlock the model response can be detected by a straightforward perplexity-based filtering on the input prompt. To overcome...
Toward a Safer Web Multilingual Multi-Agent LLMs for Mitigating Adversarial Misinformation Attacks	Nouar Aldahoul, Yasir Zaki	The rapid spread of misinformation on digital platforms threatens public discourse, emotional stability, and decision-making. While prior work has explored various adversarial attacks in misinformation detection, the specific transformations examined in this paper have not been systematically studied. In particular, we investigate language-switching across English, French, Spanish, Arabic, Hindi, and Chinese, followed by translation. We also study query length inflation preceding summarization a...
Relative Positioning Based Code Chunking Method For Rich Context Retrieval In Repository Level Code Completion Task With Code Language Model	Imranur Rahman, Md Rayhanur Rahman	Code completion can help developers improve efficiency and ease the development lifecycle. Although code completion is available in modern integrated development environments (IDEs), research lacks in determining what makes a good context for code completion based on the information available to the IDEs for the large language models (LLMs) to perform better. In this paper, we describe an effective context collection strategy to assist the LLMs in performing better at code completion tasks. The ...
Gradient-Sign Masking for Task Vector Transport Across Pre-Trained Models	Filippo Rinaldi, Aniello Panariello, Giacomo Salici, Fengyuan Liu, Marco Ciccone, Angelo Porrello, Simone Calderara	When a new release of a foundation model is published, practitioners typically need to repeat full fine-tuning, even if the same task has already been solved in the previous version. A promising alternative is to reuse the parameter changes (i.e., task vectors) that capture how a model adapts to a specific task. However, they often fail to transfer across different pre-trained models due to their misaligned parameter space. In this work, we show that the key to successful transfer lies in the si...
Learning What Matters Steering Diffusion via Spectrally Anisotropic Forward Noise	Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw, Jason Hartford, Yoshua Bengio	Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biases into the training and sampling of diffusion models to better accommodate the target distribution of the data to model. We introduce an anisotropic noise operator that shapes these biases by replacing the isotropic forward covariance with a structured, frequency-diagonal covariance. This operator unifies band-pass mas...
SSL-SE-EEG A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks	Meghna Roy Chowdhury, Yi Ding, Shreyas Sen	Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise artifacts, missing data, and high annotation costs. We introduce SSL-SE-EEG, a framework that integrates Self-Supervised Learning (SSL) with Squeeze-and-Excitation Networks (SE-Nets) to enhance feature extraction, improve noise robustness, and reduce reliance on labeled data. Unlike conventional EEG processing techniques, ...
Domain-Shift-Aware Conformal Prediction for Large Language Models	Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz	Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under domain shift, often leading to under-coverage and unreliable prediction sets. We propose a new framework called Domain-Shift-Aware Confo...
Monte Carlo-Type Neural Operator for Differential Equations	Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar, Saif Eddin Jabari	The Monte Carlo-type Neural Operator (MCNO) introduces a framework for learning solution operators of one-dimensional partial differential equations (PDEs) by directly learning the kernel function and approximating the associated integral operator using a Monte Carlo-type approach. Unlike Fourier Neural Operators (FNOs), which rely on spectral representations and assume translation-invariant kernels, MCNO makes no such assumptions. The kernel is represented as a learnable tensor over sampled inp...
Federated Split Learning for Resource-Constrained Robots in Industrial IoT Framework Comparison, Optimization Strategies, and Future Directions	Wanli Ni, Hui Tian, Shuai Wang, Chengyang Li, Lei Sun, Zhaohui Yang	Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios. We compare synchronous, asynchronous, hierarchical, and heterogeneous FedSL frame...
Segment-Factorized Full-Song Generation on Symbolic Piano Music	Ping-Yi Chen, Chih-Pin Tan, Yi-Hsuan Yang	We propose the Segmented Full-Song Model (SFS) for symbolic full-song generation. The model accepts a user-provided song structure and an optional short seed segment that anchors the main idea around which the song is developed. By factorizing a song into segments and generating each one through selective attention to related segments, the model achieves higher quality and efficiency compared to prior work. To demonstrate its suitability for human-AI interaction, we further wrap SFS into a web a...
Information-Theoretic Policy Pre-Training with Empowerment	Moritz Schneider, Robert Krug, Narunas Vaskevicius, Luigi Palmieri, Michael Volpp, Joschka Boedecker	Empowerment, an information-theoretic measure of an agent's potential influence on its environment, has emerged as a powerful intrinsic motivation and exploration framework for reinforcement learning (RL). Besides for unsupervised RL and skill learning algorithms, the specific use of empowerment as a pre-training signal has received limited attention in the literature. We show that empowerment can be used as a pre-training signal for data-efficient downstream task adaptation. For this we extend ...
Asking For It Question-Answering for Predicting Rule Infractions in Online Content Moderation	Mattia Samory, Diana Pamfile, Andrew To, Shruti Phadke	Online communities rely on a mix of platform policies and community-authored rules to define acceptable behavior and maintain order. However, these rules vary widely across communities, evolve over time, and are enforced inconsistently, posing challenges for transparency, governance, and automation. In this paper, we model the relationship between rules and their enforcement at scale, introducing ModQ, a novel question-answering framework for rule-sensitive content moderation. Unlike prior class...
Generative Models for Helmholtz Equation Solutions A Dataset of Acoustic Materials	Riccardo Fosco Gramaccioni, Christian Marinoni, Fabrizio Frezza, Aurelio Uncini, Danilo Comminiello	Accurate simulation of wave propagation in complex acoustic materials is crucial for applications in sound design, noise control, and material engineering. Traditional numerical solvers, such as finite element methods, are computationally expensive, especially when dealing with large-scale or real-time scenarios. In this work, we introduce a dataset of 31,000 acoustic materials, named HA30K, designed and simulated solving the Helmholtz equations. For each material, we provide the geometric confi...
Generative AI-Driven Hierarchical Multi-Agent Framework for Zero-Touch Optical Networks	Yao Zhang, Yuchen Song, Shengnan Li, Yan Shi, Shikui Shen, Xiongyan Tang, Min Zhang, Danshi Wang	The rapid development of Generative Artificial Intelligence (GenAI) has catalyzed a transformative technological revolution across all walks of life. As the backbone of wideband communication, optical networks are expecting high-level autonomous operation and zero-touch management to accommodate their expanding network scales and escalating transmission bandwidth. The integration of GenAI is deemed as the pivotal solution for realizing zero-touch optical networks. However, the lifecycle manageme...
Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems A Comparative Study of VQC vs. MLP	Aueaphum Aueawatthanaphisut, Nyi Wunna Tun	The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environment. The study employed a multilayer perceptron (MLP) agent as a classical baseline and a parameterized variational quantum circuit (VQC) as a quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results demo...

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ARXIV CS AI 20251007 SUMMARY

arXiv cs.AI 20251007 论文分析报告

📊 数据统计概览

📈基本统计

👥高产作者 Top 10

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🤖 AI 深度分析

arXiv cs.AI 论文分析报告

1. 研究方向热度分析

LLM Applications & Reasoning

Multimodality (Vision, Speech, etc.)

Agentic AI & Multi-Agent Systems (MAS)

AI for Science & Specific Domains

Model Optimization & Efficiency

Generative Models & Diffusion

AI Security & Privacy

Embodied AI & Robotics

总结与趋势

2. 作者关系图谱

高产作者

合作网络图谱 (部分)

3. 技术创新总结

From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions

Syn-Diag: An LLM-based Synergistic Framework for Generalizable Few-shot Fault Diagnosis on the Edge

Optimizing for Persuasion Improves LLM Generalization: Evidence from Quality-Diversity Evolution of Debate Strategies,

Training-Free Time Series Classification via In-Context Reasoning with LLM Agents,

ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models,

Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research,

Barbarians at the Gate: How AI is Upending Systems Research,

Mission Impossible: Feedback-Guided Dynamic Interactive Planning for Improving Reasoning on LLMs

MetaVLA: Unified Meta Co-training For Efficient Embodied Adaption

Deciphering Invariant Feature Decoupling in Source-free Time Series Forecasting with Proxy Denoising

4. 论文完整列表

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