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

Jun 16

Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation

arXiv:2606. 14945v1 Announce Type: new Abstract: The autoresearch pattern enables autonomous experimentation by having a large language model (LLM) iteratively modify code to optimize a target metric.

By Faramarz Jabbarvaziri

llms agents benchmarks

arXiv AI

Jun 16

Seeing Roads Through Words: A Language-Guided Framework for RGB-T Driving Scene Segmentation

arXiv:2602. 07343v2 Announce Type: replace-cross Abstract: Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications.

By Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy

llms rag agents computer-vision robotics multimodal benchmarks

arXiv Machine Learning

Jun 16

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.

By Maheed H. Ahmed, Mahsa Ghasemi

llms agents reinforcement-learning fine-tuning safety

arXiv AI

Jun 16

AgentFairBench: Do LLM Agents Discriminate When They Act?

arXiv:2606. 16723v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly take actions (screening applicants, recommending credit, triaging patients), yet fairness for LLMs is still measured by grading answers.

By Triveni Morla, Rohith Reddy Bellibaltu, Manpreet Singh, Manmeet Singh Kapoor

llms agents benchmarks safety

arXiv AI

Jun 16

FragFuse: Bypassing Access Control of Large Language Model Agents via Memory-Based Query Fragmentation and Fusion

arXiv:2606. 15609v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on long-term memory to support complex task execution, user personalization, and domain adaptation.

By Zixin Rao, Wentian Zhu, Chan Aristella Lu, Zhaorun Chen, Wei Niu, Le Guan, Bo Li, Zhen Xiang

llms agents fine-tuning benchmarks

arXiv AI

Jun 16

Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion

arXiv:2606. 14885v1 Announce Type: new Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.

By Yi Lu, Zhuofeng Li, Ping Nie, Haoxiang Zhang, Yuyu Zhang, Kai Zou, Wenhu Chen, Jimmy Lin, Dongfu Jiang, Yu Zhang

agents benchmarks

arXiv AI

Jun 16

From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents

arXiv:2606. 04990v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents are evolving from passive text generators into autonomous systems capable of planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.

By Yiqi Wang, Jiaqi Zhang, Taotao Cai, Zirui Liu, Qingqiang Sun, Zequn Sun, Zhangkai Wu, Manqing Dong, Mingkai Zhang, Xuefei Yin, Yanming Zhu

llms agents benchmarks safety

arXiv AI

Jun 16

Toward Vibe Medicine: A Self-Evolving Multi-Agent Framework for Clinical Decision Support

arXiv:2606. 15504v1 Announce Type: new Abstract: In recent years, the advances of large language models and autonomous agents have revolutionized the healthcare field, facilitating diagnosis and improving treatment results.

By Qianxue Zhang, Yiming Ren, Shihuan Qin, Xiao Zhang, Liao Zhang, Jinyang Huang, Zhengliang Liu, Chenbin Liu, Hongying Feng, Jingyuan Chen, Yuzhen Ding, Weihang You, Hanqi Jiang, Yi Pan, Yifan Zhou, Junhao Chen, Lifeng Chen, Wei Liu, Tianming Liu, Zengren Zhao, Lian Zhang

llms agents multimodal safety

arXiv AI

Jun 16

AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems

arXiv:2602. 11510v3 Announce Type: replace Abstract: Multi-agent Large Language Model (LLM) systems create privacy risks that current output-only benchmarks cannot measure.

By Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah

llms agents benchmarks safety

arXiv AI

Jun 16

A Formal Framework for Declarative Agentic AI in Business Process Analysis

arXiv:2606. 15291v1 Announce Type: new Abstract: Agentic AI opens new opportunities for automating Business Process (BP), enabling autonomous decision-making and dynamic adaptation.

By Mohammad Azarijafari, Luisa Mich, Michele Missikoff

rag agents

arXiv AI

Jun 16

State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs

arXiv:2606. 16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets.

By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Das, Jitesh Chandra, Yun-Shiuan Chuang, Chaitanya Kulkarni, Arun Menon, Linsey Pang, Avinash Karn, Mouli V, Prakhar Mehrotra

llms agents safety

arXiv AI

Jun 16

AutoDojo: Adaptive Attacks Expose Superficial Defenses and User-Underspecification Limits in LLM Agents

arXiv:2606. 15057v1 Announce Type: cross Abstract: Indirect prompt injection (IPI) is a major security threat to LLM-powered agents.

By Xinhang Ma, Taoran Li, Chaowei Xiao, Zhiyuan Yu, Ning Zhang, Yevgeniy Vorobeychik

llms agents multimodal benchmarks

arXiv AI

Jun 16

MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis

arXiv:2603. 01131v3 Announce Type: replace-cross Abstract: Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions.

By Yuqi Zhan, Xinyue Wu, Tianyu Lin, Yutong Bao, Xiaoyu Wang, Weihao Cheng, Huangwei Chen, Feiwei Qin, Zhu Zhu

llms agents

arXiv Machine Learning

Jun 16

MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems

arXiv:2604. 26963v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators.

By Yifei Wang, Hancheng Ye, Yechen Xu, Cong Guo, Chiyue Wei, Qinsi Wang, Dongting Li, Tingjun Chen, Hai "Helen" Li, Danyang Zhuo, Yiran Chen

llms agents efficiency

arXiv Machine Learning

Jun 16

Hierarchical Advantage Weighting for Online RL Fine-Tuning of VLAs from Sparse Episode Outcomes

arXiv:2606. 17043v1 Announce Type: cross Abstract: When pretrained VLA policies are fine-tuned through online RL, each rollout episode produces only a single binary outcome (success or failure), yet the actor update requires per-transition supervision.

By Tongyan Fang, Siyuan Huang, Naiyu Fang, Ganlong Zhao, Zhongjin Luo, Jianbo Liu, Xiaogang Wang, Ying Dong, Hongsheng Li

agents reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jun 16

An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers

arXiv:2606. 14739v1 Announce Type: cross Abstract: The deployment of modern machine learning (ML) solutions on resource-constrained edge devices highlights implementation challenges.

By Georgios Papandroulidakis, Shady Agwa, Themis Prodromakis

agents safety

arXiv Machine Learning

Jun 16

Multimodal Evaluator Preference Collapse: Cross-Modal Contagion in Self-Evolving Agents

arXiv:2606. 16682v1 Announce Type: new Abstract: When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge.

By Zewen Liu

llms agents multimodal

arXiv Machine Learning

Jun 16

BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models

arXiv:2606. 16489v1 Announce Type: new Abstract: Model-based Reinforcement Learning (MBRL) has achieved remarkable success in continuous control by leveraging latent world models.

By Shaowei Zhang, Jiahan Cao, Xunlan Zhou, Shenghua Wan, De-Chuan Zhan

agents computer-vision reinforcement-learning

arXiv Machine Learning

Jun 16

Best Arm Identification with Minimal Regret

arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.

By Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin

agents reinforcement-learning

arXiv AI

Jun 16

LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis

arXiv:2606. 16149v1 Announce Type: new Abstract: Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases.

By Minh-Ha Nguyen, Erica Gray, Chih-Ting Yang, Rizwan Hamid, Lingyao Li, Siyuan Ma, Thomas A. Cassini, Cathy Shyr

llms agents fine-tuning benchmarks

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