AI Agent

Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network

Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, Khaled B. Letaief

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments This paper has been submitted to IEEE TNSM. The source code has been released at: https://github.com/qiongwu86/Edge-Caching-Based-on-Multi-Agent-Deep-Reinforcement-Learning-and-Federated-Learning

Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem

Tanmay Vilas Samak, Chinmay Vilas Samak, Venkat Krovi

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted as Multi-Agent Dynamic Games (MAD-Games) Workshop Paper at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2023

A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents

Qin Yang, Ramviyas Parasuraman

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments This paper is accepted by the ACM Symposium on Applied Computing (SAC) 2023 Technical Track on Intelligent Robotics and Multi-Agent Systems (IRMAS)

Multi-Agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP

Yiwei Fu, Dheeraj S. K. Kapilavai, Elliot Way

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Submitted to 20th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2022)

How to solve a classification problem using a cooperative tiling Multi-Agent System?

Thibault Fourez, Nicolas Verstaevel, Frédéric Migeon, Frédéric Schettini, Frédéric Amblard

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, Jul 2022, L'Aquila, Italy. arXiv admin note: substantial text overlap with arXiv:2209.06824

Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning

Yuchen Xiao, Xueguang Lyu, Christopher Amato

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Journal ref IEEE The 3rd International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2021

A Game-Theoretic Approach to Multi-Agent Trust Region Optimization

Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments A Multi-Agent Trust Region Learning (MATRL) algorithm that augments the single-agent trust region policy optimization with a weak stable fixed point approximated by the policy-space meta-game

Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges

Clément Moulin-Frier, Pierre-Yves Oudeyer

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.CL、cs.LG

Journal ref Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL AAAI 2020-2021), AAAI Spring Symposium Series, Stanford University, Palo Alto, California, USA

Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG

Hangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments Attention-based Multi-agent DDPG. Experimental results show that it not only outperforms the state-of-the-art RL-based methods and rule-based methods by a large margin, but also achieves better performance in terms of scalability and robustness

ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning

Hangyu Mao, Zhibo Gong, Yan Ni, Zhen Xiao

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

Comments V3 of original submission. Actor-Critic Method for Multi-agent Learning-to-Communicate based on Deep Reinforcement Learning, It is suitable for both continuous and discrete action space environments

Verification & Validation of Agent Based Simulations using the VOMAS (Virtual Overlay Multi-agent System) approach

Muaz A. Niazi, Amir Hussain, Mario Kolberg

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.SE

Comments 7 pages, 5 figures, cite as Muaz Niazi, Amir Hussain and Mario Kolberg , Verification and Validation of Agent-Based Simulation using the VOMAS approach, Proceedings of the Third Workshop on Multi-Agent Systems and Simulation'09 (MASS '09), as part of MALLOW 09, Sep 7-11, 2009, Torino, Italy

GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning Systems

Ashish Rana, Michael Oesterle, Jannik Brinkmann

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI；autonomous agent(comments)

Comments Extended Abstract accepted in the 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2024)

Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning

Jennifer She, Jayesh K. Gupta, Mykel J. Kochenderfer

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.LG；autonomous agent(comments)

Comments Full version of the Extended Abstract accepted at the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022

Quantifying the effects of environment and population diversity in multi-agent reinforcement learning

Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI；autonomous agent(comments)

Comments Accepted at Autonomous Agents and Multi-Agent Systems

Unravelling multi-agent ranked delegations

Rachael Colley, Umberto Grandi, Arianna Novaro

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI；autonomous agent(comments)

Comments 48 pages, 5 Tables, 3 Figures, to be published in the Journal of Autonomous Agents and Multi-Agent Systems

Planner-Conditioned Diffusion for Coordinated Multi-Agent Exploration

用于协调多智能体探索的规划器条件扩散模型

Marcus Yu Siong Teo, Jeric Lew, Tanishq Duhan, Guillaume Sartoretti

机构 * National University of Singapore（新加坡国立大学） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；planning(abstract)

AI总结 提出规划器条件扩散策略PCDP，通过规划器身份作为条件输入训练多模态单智能体策略，结合局部重排序实现协调，在多智能体探索中提升性能并验证了方法有效性。

Comments Code and models are available at https://github.com/marmotlab/PCDP

One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL

单个冻结模拟器不够：多智能体强化学习中的模拟器崩溃问题

Simon Yu, Nicholas Tomlin, Marwa Abdulhai, Ximing Lu, Derek Chong, Abe Hou, Dilara Soylu, Sergey Levine, Christopher D. Manning, Weiyan Shi

机构 * Northeastern University（东北大学） ； New York University（纽约大学） ； UC Berkeley（加州大学伯克利分校） ； University of Washington（华盛顿大学） ； Stanford University（斯坦福大学） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.CL、cs.LG

AI总结 针对人机交互多智能体强化学习中单个LLM模拟器导致的策略泛化缺陷，提出Verbalized Sampling和Co-Training两种方案，在多轮基准测试和真实用户研究中显著提升了性能，发布了开源框架SCOPE。

Comments 42 pages, 29 figures

Multi-Agent Embodied Autonomous Driving (MAEAD): From V2X Information Exchange to Shared World Models

多智能体具身自动驾驶：从V2X信息交换到共享世界模型

Senkang Hu, Zhengru Fang, Yihang Tao, Zihan Fang, Yiqin Deng, Yuguang Fang

机构 * Lingnan University, Hong Kong（岭南大学（香港）） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；planning(abstract)

AI总结 本文综述了从单车智能向多智能体具身系统转变的自动驾驶技术，通过共享世界模型实现感知共享、意图推断和协同规划，并指出了在仿真评估、实时安全保证等方面的研究空白。

VLM- and LLM-Driven Multi-Agent System for PET Image Denoising

基于视觉语言模型（VLM）与大语言模型（LLM）驱动的多智能体正电子发射断层扫描（PET）图像去噪系统

Boxiao Yu, Savas Ozdemir, Yang Xing, Fumio Hashimoto, Jiong Wu, Yizhou Chen, Axel Rominger, Ruogu Fang, Kuangyu Shi, Tinsu Pan, Kuang Gong

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；agentic(abstract)

AI总结 针对PET图像分辨率低、信噪比差及深度学习去噪部署需多模型与专家干预的问题，本文提出VLM与LLM驱动的多智能体闭环PET去噪框架，自主选最优模型参数，在低剂量数据上优于UNet等基线方法。

Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages

错误但有用：多智能体消息中超越答案正确性的轨迹价值

Chih-Hsuan Yang, Anjir Ahmed Chowdhury, Cheng-Hau Yang, Weijian Zheng, Fernando Llorente, Xiaolong Ma, Xinyang Li, Eliu A. Huerta, Ian T. Foster, Rajeev Thakur

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.CL、cs.LG

AI总结 该研究提出DHD协议，发现多智能体错误消息含有用信息，错误但有帮助的消息普遍存在，其轨迹价值可辅助决策，答案正确性不决定轨迹价值。

Comments 24 pages, 9 figures. Includes an appendix and an ancillary reproducibility artifact

Who Are You Explaining To? A Multi-Agent System for Audience-Aware XAI Narratives

你在向谁解释？面向受众感知的XAI叙事多智能体系统

Francesco Musicco, Danilo Danese, Giuseppe Fasano, Angela Lombardi, Alberto Carlo Maria Mancino, Tommaso Di Noia

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；planning(abstract)

AI总结 针对现有XAI叙事无法适配不同受众的问题，提出XstrAI多智能体框架，结合三类LLM智能体与修正循环，在糖尿病、中风风险预测任务中，其叙事适配性与保真度优于多数基线。

Beyond Cash Flows: A Multi-Agent AI Framework for Valuing Clinical-Stage, Cross-Border Biotechnology

超越现金流：用于临床阶段跨境生物技术估值的多智能体AI框架

Yuhan Fang

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；agentic(abstract)

AI总结 本文针对临床阶段生物技术估值的现金流假设失效问题，提出多智能体AI框架，其含估值层、跨市场协调层与冲突融合机制，基于作者过往实践确立智能体投资系统设计原则。

MoRSE: Task-Oriented Multi-Agent System with Mixture of Role-Subtask Experts

MoRSE：面向任务的混合角色-子任务专家多智能体系统

Peiwen Li, Shiyang Zhang, Yangtian Zhang, Sizhuang He, David van Dijk, Rex Ying

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.CL、cs.LG

AI总结 MoRSE是一种面向任务的多智能体系统，通过角色-子任务专家混合机制与分层组相对策略优化，在代码生成基准上提升了任务及逐步性能，且专业化增益可跨任务类别与领域泛化。

Comments 25 pages, 8 figures, 9 tables

Plan-and-Avoid: Real-Time Aircraft Trajectory Coordination in a Multi-Agent Environment

规划与避让：多智能体环境下的实时飞行器轨迹协调

Huseyin Emre Tekaslan, Ella M. Atkins, Natasha Neogi

机构 * Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学） ； NASA Langley Research Center（NASA兰利研究中心） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；planning(abstract)

AI总结 该研究提出Plan-and-Avoid框架，针对多智能体空域环境，通过实时生成协作建议，在900余起强制着陆案例测试中，以5.7秒最坏响应时间实现优先级轨迹的低延迟安全间隔协调。

Offline Nash Solvers Meet Online Tree Search in Multi-Agent Games on Graphs

离线纳什求解器与在线树搜索在图上多智能体博弈中的结合

Mukesh Kumar, Yue Guan, Panagiotis Tsiotras

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；planning(abstract)

AI总结 多智能体追逃博弈计算纳什均衡策略有挑战，本文提出原始引导树搜索框架PGTS，结合离线精确纳什均衡计算与在线树搜索，实验表明其显著优于现有基线，在多种图拓扑上对对手保持稳健性能。

Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings for CLEF JOKER 2025 Task 2

并非双关：结合对比学习与音义嵌入的多智能体双关语翻译方法，用于CLEF JOKER 2025任务2

Russell Taylor, Benjamin Herbert, Michael Sana

机构 * Georgia Institute of Technology（佐治亚理工学院） ； Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学） ； Joint Institute for Nuclear Research（联合核子研究中心） ； Vrije Universiteit Amsterdam（阿姆斯特丹自由大学） ； University of Skövde（斯德哥尔摩大学） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.CL、cs.LG

AI总结 本研究针对跨语言双关语翻译难题，提出结合对比学习、音义嵌入与多智能体生成器-判别器框架的三阶段方法，在CLEF JOKER 2025任务2竞赛中获第一、第二名，推动了双关语翻译研究。

Journal ref CEUR Workshop Proceedings, Vol-4038, pp. 2870-2888, 2025

History Matters: Meta-policy Delegation with Heterogeneous Multi-agent Reinforcement Learning

历史至关重要：异构多智能体强化学习的元策略委托

Ziqing Lu, Avinash Reddy Mudireddy, Sarra Alqahtani, Weiyu Xu

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；AI agent(abstract)

AI总结 本文针对异构多智能体系统的资源受限任务委托问题，提出依赖交互历史的元策略委托框架与多维货币机制，基于多智能体强化学习实现高效协作与低成本任务完成。

AgenticVAU: Multi-Agent Explore-Verify Reasoning for Video Anomaly Understanding

AgenticVAU：用于视频异常理解的多智能体探索-验证推理框架

Yuxiang Duan, Huining Li, Ao Li, Shuai Feng, Lanju Kong, Ning Liu, Jian Zhang, Xingdong Sheng, Yuntao Du

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；planning(abstract)

AI总结 本文提出无需训练的多智能体框架AgenticVAU，通过四个专门智能体协作完成视频异常理解的探索-验证过程，在VAU-Bench数据集上优于零样本推理及强化学习基线方法。

Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity

通过连通性对多智能体协同过滤系统发起攻击与防御

Anjun Hu, Hanting Xie, Saranya Govindan, Jas Kandola, Kurt Cutajar

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；agentic(abstract)

AI总结 本研究将MAS的攻击与防御方法适配到AgentCF框架下的智能体CF场景，表征连通性对攻防结果的影响，还探索了流行病启发指标用于CF配置的健壮性评估。

Comments 10 pages, 10 figures, 20th ACM Conference on Recommender Systems (RecSys '26)

MDGAM-Based Cooperative Task Scheduling for Communication-Constrained Distributed Multi-Agent Systems

基于MDGAM的通信受限分布式多智能体系统协同任务调度

Licheng Wang, Mingtao Huang, Yuan Shen

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；planning(abstract)

AI总结 针对通信受限分布式多智能体系统的任务调度问题，本文提出基于MDGAM的神经调度框架，结合GRMAPG算法，经实验验证其可提升任务完成性能。