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

StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement

arXiv:2606. 00267v1 Announce Type: cross Abstract: Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot actions.

By Junwon Seo, Sushant Veer, Ran Tian, Wenhao Ding, Apoorva Sharma, Karen Leung, Edward Schmerling, Marco Pavone, Andrea Bajcsy

llms agents diffusion robotics multimodal benchmarks

arXiv AI

Jun 2

CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences

arXiv:2606. 00931v1 Announce Type: cross Abstract: Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edits and do not fully capture the diversity of real-image tasks in professional workflows.

By Fangzhou Lin, Peiran Li, Lingyu Xu, Wenjing Chen, Qianwen Ge, Shuo Xing, Mingyang Wu, Xiangbo Gao, Siyuan Yang, Kazunori Yamada, Ziming Zhang, Haichong Zhang, Zhen Dong, Ming-Hsuan Yang, Zhengzhong Tu

agents computer-vision multimodal 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

Task diversity produces systematic transfer but inhibits continual reinforcement learning

arXiv:2606. 00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions change.

By Purab Seth, Neil Shah, Kunal Jha, Samuel J. Gershman, Max Kleiman-Weiner, Wilka Carvalho

agents reinforcement-learning 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

Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic

arXiv:2601. 18783v2 Announce Type: replace-cross Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles.

By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

agents reinforcement-learning safety

arXiv AI

Jun 2

A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks

arXiv:2606. 01312v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous vehicle systems.

By Kiran Khurshid, Shumaila Javaid, Nasir Saeed

llms agents

arXiv AI

Jun 2

ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning

arXiv:2606. 02433v1 Announce Type: cross Abstract: The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical prediction.

By Zhensheng Wang, Xiaole Liu, Wenmian Yang, Kun Zhou, Yiquan Zhang, Weijia Jia

llms agents nlp data-engineering

arXiv AI

Jun 2

Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

arXiv:2606. 02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control.

By Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy

agents reinforcement-learning fine-tuning

arXiv AI

Jun 2

RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

arXiv:2604. 15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT).

By M\'elanie Roschewitz, Kenneth Styppa, Yitian Tao, Jiwoong Sohn, Jean-Benoit Delbrouck, Benjamin Gundersen, Nicolas Deperrois, Christian Bluethgen, Julia E. Vogt, Bjoern Menze, Farhad Nooralahzadeh, Michael Krauthammer, Michael Moor

llms agents multimodal safety

arXiv AI

Jun 2

AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents

arXiv:2603. 14465v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions.

By Shengda Fan, Xuyan Ye, Yupeng Huo, Zhi-Yuan Chen, Yiju Guo, Shenzhi Yang, Wenkai Yang, Shuqi Ye, Jingwen Chen, Haotian Chen, Xin Cong, Yankai Lin

llms agents benchmarks

arXiv AI

Jun 2

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

arXiv:2512. 16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks.

By Yuxuan Qiao, Dongqin Liu, Hongchang Yang, Wei Zhou, Songlin Hu

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 2

Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning

arXiv:2606. 02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning.

By Liuji Chen, Dianxing Tang, Xing Shi, Dingshuo Chen, Qiang Liu, Shu Wu, Liang Wang

agents reinforcement-learning benchmarks

arXiv AI

Jun 2

STaR-KV: Spatio-Temporal Adaptive Re-weighting for KV Cache Compression in GUI Vision-Language Models

arXiv:2606. 01790v1 Announce Type: cross Abstract: Vision-language-model-based graphical user interface (GUI) agents have shown broad automation capabilities, yet deployment is bottlenecked by a key-value (KV) cache that grows linearly with interaction steps.

By Yuhang Han, Wenzheng Yang, Yujie Chen, Xiangqi Jin, Yaojie Zhang, Siteng Huang, Linfeng Zhang

llms agents efficiency multimodal benchmarks

arXiv Machine Learning

Jun 2

Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks

arXiv:2605. 22305v2 Announce Type: replace Abstract: We analytically solve the Mountain Car problem, a canonical benchmark in RL, and derive an optimal control solution, closing a gap after 36 years.

By Stefan Huber, Hannes Unger, Georg Sch\"afer, Jakob Rehrl

agents reinforcement-learning benchmarks

arXiv AI

Jun 2

Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning

arXiv:2605. 26684v2 Announce Type: replace-cross Abstract: Group-based reinforcement learning (RL) methods have achieved remarkable success in improving the performance of large language models (LLMs) and have been rapidly extended to agentic tasks.

By Xin Cheng, Shuo He, Lang Feng, HaiYang Xu, Ming Yan, Lei Feng, Bo An

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 2

Herculean: An Agentic Benchmark for Financial Intelligence

arXiv:2605. 14355v3 Announce Type: replace Abstract: As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carry out financial professional work.

By Xueqing Peng, Zhuohan Xie, Yupeng Cao, Haohang Li, Lingfei Qian, Yan Wang, Vincent Jim Zhang, Huan He, Xuguang Ai, Linhai Ma, Ruoyu Xiang, Yueru He, Yi Han, Shuyao Wang, Yuqing Guo, Mingyang Jiang, Yilun Zhao, Youzhong Dong, Xiaoyu Wang, Yankai Chen, Ye Yuan, Qiyuan Zhang, Fuyuan Lyu, Haolun Wu, Yonghan Yang, Zichen Zhao, Yuyang Dai, Fan Zhang, Rania Elbadry, Ayesha Gull, Muhammad Usman Safder, Nuo Chen, Fengbin Zhu, Tianshi Cai, Zimu Wang, Polydoros Giannouris, Yuechen Jiang, Zhiwei Liu, Mohsinul Kabir, Yuyan Wang, Yixiang Zheng, Yangyang Yu, Weijin Liu, Wenbo Cao, Anke Xu, Peng Lu, Jerry Huang, Mingquan Lin, Prayag Tiwari, Yijia Zhao, V\'ictor Guti\'errez-Basulto, Xiao-Yang Liu, Kaleb E Smith, Jiahuan Pei, Arman Cohan, Jimin Huang, Yuehua Tang, Alejandro Lopez-Lira, Xi Chen, Xue Liu, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou

agents nlp benchmarks

arXiv AI

Jun 2

Implicit Regularization for Multi-label Feature Selection

arXiv:2411. 11436v2 Announce Type: replace-cross Abstract: In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding.

By Dou El Kefel Mansouri, Khalid Benabdeslem, Seif-Eddine Benkabou

rag agents benchmarks safety

arXiv AI

Jun 2

Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations

arXiv:2605. 26874v2 Announce Type: replace-cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores.

By Madhulatha Mandarapu, Sandeep Kunkunuru

llms agents benchmarks

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