AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

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arXiv Machine Learning

Jul 30

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

arXiv:2607. 26643v1 Announce Type: cross Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications.

By Hongqiang Lin, Chao Liu, Xiaofan Bai, Xuan Jin, Yuhong Li, Nenggan Zheng, Xipeng Cao

llms agents benchmarks

arXiv Machine Learning

Jul 30

Minimal Markovization via Stable Quotients in Holonomy-Cover Decision Processes

arXiv:2607. 27132v1 Announce Type: new Abstract: An agent acting under partial observability must retain a recursively updateable statistic of history that restores the Markov property, but the smallest such statistic is generally unknown.

By Zuyuan Zhang, Yongshan Chen, Mahdi Imani, Tian Lan

agents reinforcement-learning

arXiv Machine Learning

Jul 30

RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment

arXiv:2607. 26631v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments.

By Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha, Anura Jayasumana, Kanchana Thilakarathna

llms rag agents efficiency benchmarks

arXiv Machine Learning

Jul 30

Top-$k$ Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection

arXiv:2607. 26273v1 Announce Type: new Abstract: We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of $k$ arms and observes their $d$-dimensional reward vectors under semi-bandit feedback.

By Nicolas Gutowski, Fabien Chhel, Alexandre Letard, Sylvain Lamprier

agents reinforcement-learning

arXiv Machine Learning

Jul 30

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

arXiv:2607. 26998v1 Announce Type: cross Abstract: Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools.

By Ruoyu Wang, Heng Zhao, Renjie Wu, Mengnan Zhao, Zhixuan Chu, Wanyu Lin, Tianhang Zheng

llms agents

arXiv Machine Learning

Jul 30

Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement

arXiv:2607. 26473v1 Announce Type: new Abstract: Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings.

By Haifeng Wu

llms agents robotics

arXiv Machine Learning

Jul 30

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning

arXiv:2607. 26059v1 Announce Type: new Abstract: We report a striking phenomenon: deep reinforcement learning agents trained with frozen, randomly initialized CNN feature extractors spontaneously develop extremely sparse fully-connected representations, without any sparsity-inducing objective.

By Scott M. Norton

agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 30

Two Calls Beat Five Agents: Evaluating Multi-Agent Pipelines Against Self-Refinement for Local Language Models

arXiv:2607. 26922v1 Announce Type: new Abstract: Multi-agent LLM pipeline systems break down the task among multiple roles for better reasoning, but are benchmarked mainly with large-scale commercial models.

By Ashish Prajapati, Om Mohite

llms agents benchmarks

arXiv Machine Learning

Jul 30

DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution

arXiv:2607. 26722v1 Announce Type: cross Abstract: Harness plays a critical role in large language model agent performance, and building a high-performing harness requires substantial expert effort.

By Hanghui Guo, Weijie Shi, Zhangze Chen, Shengxiang Xu, Yishu Wang, Yimei Zhang, Wangze Ni, Jia Zhu, Shimin Di

llms agents efficiency benchmarks

arXiv Machine Learning

Jul 30

Parameterized Fair Resource Allocation under Diversity Constraints

arXiv:2607. 26485v1 Announce Type: cross Abstract: Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem with diversity constraints to ensure group fairness.

By Keke Huang, Yik Yu Ng, Laks V. S. Lakshmanan, Xiaokui Xiao

agents safety

arXiv Machine Learning

Jul 30

MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts

arXiv:2607. 26345v1 Announce Type: new Abstract: Modeling and forecasting nonlinear dynamics under distribution shifts is essential for robust decision-making in real-world systems.

By Mahmoud Selim, Sriharsha Bhat, Karl H. Johansson

agents

arXiv Machine Learning

Jul 30

Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations

arXiv:2607. 26481v1 Announce Type: new Abstract: Detecting when the statistical behavior of an engineered system changes, and identifying which component is responsible, are core problems in the monitoring of telecommunication networks, robotic platforms, security infrastructure, and multi-agent systems.

By Seunghun Yu, Meiyi Zhu, Petar Popovski, Joonhyuk Kang, Osvaldo Simeone

agents robotics benchmarks safety

arXiv Machine Learning

Jul 30

Mitigating Compounding Error via Video Representation Regularization

arXiv:2607. 27036v1 Announce Type: cross Abstract: Video diffusion-based world models enable long autoregressive video generation for robotics, autonomous driving and simulation tasks, yet sliding-window autoregressive inference suffers from severe error accumulation that degrades frame quality over time.

By Taiye Chen, Qi Zhang, Yisen Wang

agents diffusion robotics

arXiv Machine Learning

Jul 30

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.

By Zhiyuan Yao, Yuxin Chen, Zhengxi Lu, Zishan Xu, Yueqing Sun, Yifu Guo, Yuquan Lu, Zhengzhou Cai, Kangning Zhang, Zhuowen Han, Zi-Han Wang, Ziang Ye, Qi Gu, Xunliang Cai, Weiwen Liu, Yongliang Shen

llms agents reinforcement-learning

arXiv Machine Learning

Jul 30

MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis

arXiv:2607. 27146v1 Announce Type: cross Abstract: Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation.

By Yihao Chen, Shi Chang, Khaled Chawa, Feng Lin, Boyuan Chen, Shaowei Wang, Ahmed E. Hassan

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jul 30

GPT-Red: Automated Red Teaming via Self-Play at Scale

arXiv:2607. 26115v1 Announce Type: cross Abstract: We introduce \textbf{GPT-Red}, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs.

By Eric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal, Sam Toyer, Dylan Hunn, Stephanie Lin, Yuxin Wen, Xiangyu Qi, Christopher Wolff, Zizhao Wang, Milad Nasr, Sicheng Zhu, Chuan Guo, Juan Felipe Cer\'on Uribe, Kaiwen Wang, Aiden Low, Kai Xiao, Kai Chen

llms agents reinforcement-learning safety

arXiv Machine Learning

Jul 30

AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control

arXiv:2607. 26533v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios.

By Jingbo Cui, Jitao Zhao, Di Jin, Dongxiao He

agents

arXiv Machine Learning

Jul 30

From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations

arXiv:2607. 26492v1 Announce Type: new Abstract: The Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained, conceptually interpretable neural networks.

By Yuan-Heng Wang, Hoshin V. Gupta

agents

arXiv Machine Learning

Jul 30

Metis: Memory Foundation Model

arXiv:2607. 26760v1 Announce Type: cross Abstract: Recent advances in AI agents have increasingly internalized native capabilities into their underlying foundation models, giving rise to multimodal foundation models and large reasoning models.

By Zeyu Zhang, Ziliang Guo, Yihang Sun, Xichong Zhang, Xixuan Hao, Zehao Lin, Yang Zhang, Xiaoyan Zhao, Tong Shen, Bo Tang, Zhi-Qin John Xu, Junchi Yan, Haofen Wang, Xu Chen, Feiyu Xiong, Zhiyu Li, Tat-Seng Chua

agents multimodal

arXiv Machine Learning

Jul 30

Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning

arXiv:2607. 26358v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference policy.

By Keegan Harris, Brian W. Lee, Ian Waudby-Smith, Philip Amortila, Nika Haghtalab, Michael I. Jordan

llms agents reinforcement-learning fine-tuning

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