AI agents

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

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arXiv AI

Jun 2

MMG2Skill: Can Agents Distill In-the-Wild Guides into Self-Evolving Skills?

arXiv:2606. 01993v1 Announce Type: cross Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks.

By Xinyu Che, Junqi Xiong, Yunfei Ge, Xinping Lei, Shihao Li, Hang Yan, Han Li, Yuanxing Zhang, Zhiqi Bai, Jinhua Hao, Ming Sun, Han Li, Jiaheng Liu

llms agents multimodal benchmarks

arXiv AI

Jun 2

Evaluating Deep Research Agents on Expert Consulting Work: A Benchmark with Verifiers, Rubrics, and Cognitive Traps

arXiv:2605. 17554v2 Announce Type: replace Abstract: Frontier deep research agents (DRAs) plan a research task, synthesize across documents, and return a structured deliverable on demand.

By Tanmay Asthana, Aman Saksena, Divyansh Sahu

llms agents benchmarks

arXiv AI

Jun 2

Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation

arXiv:2310. 15676v2 Announce Type: replace-cross Abstract: Multi-modal 3D Intelligence has gained considerable attention due to its wide applications in autonomous driving and world simulation, etc.

By Yinjie Lei, Zixuan Wang, Feng Chen, Guoqing Wang, Peng Wang, Yang Yang

agents robotics multimodal benchmarks

arXiv AI

Jun 2

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

arXiv:2512. 05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.

By Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain, Ahmad Rezaei, Mohsen Gholami, Alireza Heidarikhazaei, Zhou Weimin, Yong Zhang, Mohammad Akbari

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 2

Adversarial Feeds Steer LLM Agent Decisions Against Their Defaults

arXiv:2606. 00914v1 Announce Type: new Abstract: LLM agents increasingly act after consuming ranked external information streams such as social feeds, search results, retrieval contexts, and email queues, yet safety evaluations almost always test the model or the user prompt in isolation, never the upstream ranker that decides what the agent reads just before it acts.

By Rana Muhammad Usman

llms agents safety

arXiv AI

Jun 2

SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

arXiv:2606. 00822v1 Announce Type: cross Abstract: Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to execution.

By Zicai Cui, Zihan Guo, Weiwen Liu, Weinan Zhang

llms agents benchmarks

arXiv AI

Jun 2

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.

By Chuanjie Wu, Zhishang Xiang, Yunbo Tang, Zerui Chen, Qinggang Zhang, Jinsong Su

llms rag agents benchmarks safety

arXiv AI

Jun 2

Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback

arXiv:2606. 00590v1 Announce Type: cross Abstract: Agentic search systems iteratively interact with retrieval models to answer complex queries.

By Md Zarif Ul Alam, Alireza Salemi, Hamed Zamani

agents

arXiv AI

Jun 2

Interpretable Policy Distillation for Power Grid Topology Control

arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.

By Aleksandra Dmitruka, Karlis Freivalds

agents reinforcement-learning efficiency

arXiv AI

Jun 2

Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation

arXiv:2606. 01020v1 Announce Type: new Abstract: Identifying logical fallacies in everyday discourse is challenging for many people.

By Minjing Shi, Junling Wang, Jingwei Ni, Sankalan Pal Chowdhury, Mrinmaya Sachan

llms agents

arXiv AI

Jun 2

ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment

arXiv:2606. 00644v1 Announce Type: new Abstract: AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned.

By Qiuyu Tian, Zequn Liu, Yingce Xia, Haojie Yin, Youyong Kong

llms rag agents benchmarks

arXiv Machine Learning

Jun 2

Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning

arXiv:2603. 11653v2 Announce Type: replace Abstract: Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments.

By Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, Roberto Martin-Martin

agents reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

KISS: Keeping it Simple and Slotted when Learning to Communicate over Wireless

arXiv:2606. 00266v1 Announce Type: cross Abstract: A long-standing challenge in distributed wireless systems is ensuring efficient and fair random channel access.

By Kamil Szczech, Maksymilian Wojnar, Krzysztof Rusek, Katarzyna Kosek-Szott, Szymon Szott

agents benchmarks safety

arXiv Machine Learning

Jun 2

World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications

arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.

By Arif Hassan Zidan, Yi Pan, Hanqi Jiang, Ruiyu Yan, Wei Ruan, Zihao Wu, Lifeng Chen, Weihang You, Xinliang Li, Bowen Chen, Huawen Hu, Peilong Wang, Sizhuang Liu, Jing Zhang, Siyuan Li, Zhengliang Liu, Yu Bao, Lin Zhao, Lichao Sun, Dajiang Zhu, Xiang Li, Jinglei Lv, Quanzheng Li, Wei Liu, Tianming Liu, Wei Zhang

llms agents diffusion reinforcement-learning robotics multimodal benchmarks safety

arXiv AI

Jun 2

Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning

arXiv:2606. 00780v1 Announce Type: cross Abstract: Offline meta-reinforcement learning leverages static datasets to enable agents to generalize to unseen environments by combining offline efficiency with meta-learning adaptability, yet it faces key challenges from context and policy distribution shifts.

By Fuyuan Qian, Menglong Zhang, Song Wang, Quanying Liu

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 2

Do Joint Audio-Video Generation Models Understand Physics?

arXiv:2605. 07061v2 Announce Type: replace-cross Abstract: Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visual physics, or merely generate plausible sounds and frames that violate real-world consistency?

By Zijun Cui, Xiulong Liu, Hao Fang, Mingwei Xu, Jiageng Liu, Zexin Xu, Weiguo Pian, Shijian Deng, Feiyu Du, Chenming Ge, Yapeng Tian

llms agents diffusion multimodal benchmarks

arXiv AI

Jun 2

HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization

arXiv:2603. 03741v2 Announce Type: replace-cross Abstract: To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent reinforcement learning (MARL).

By Hao Zhang, Yaru Niu, Yikai Wang, Ding Zhao, H. Eric Tseng

agents reinforcement-learning robotics

arXiv AI

Jun 2

Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation

arXiv:2606. 00827v1 Announce Type: cross Abstract: Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models.

By Xinpeng Lv, Chunyuan Zheng, Yunxin Mao, Renzhe Xu, Jinxuan Yang, Yuanlong Chen, Wangrong Huang, Shaowu Yang, Wenjing Yang, Xinwang Liu, Peng Cui, Haotian Wang

agents robotics safety

arXiv AI

Jun 2

CodeCytos: AI-assisted spatial molecular imaging analysis via code-augmented agent action space

arXiv:2606. 00472v1 Announce Type: cross Abstract: Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feature extraction, and spatial organization analysis.

By Hung Q. Vo, Huy Q. Vo, Son T. Ly, Zhihao Wan, Anh-Vu Nguyen, Hong Zhao, Jianting Sheng, Stephen T. C. Wong, Hien V. Nguyen

llms agents computer-vision benchmarks

arXiv AI

Jun 2

AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science

arXiv:2603. 19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains.

By An Luo, Jin Du, Xun Xian, Robert Specht, Fangqiao Tian, Ganghua Wang, Xuan Bi, Charles Fleming, Ashish Kundu, Jayanth Srinivasa, Mingyi Hong, Rui Zhang, Tianxi Li, Galin Jones, Jie Ding

llms agents benchmarks

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