2025 "multi-agent systems" Papers
19 papers found
AgentBreeder: Mitigating the AI Safety Risks of Multi-Agent Scaffolds via Self-Improvement
J Rosser, Jakob Foerster
Agent-Oriented Planning in Multi-Agent Systems
Ao LI, Yuexiang Xie, Songze Li et al.
Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection
Michelle Yuan, Khushbu Pahwa, Shuaichen Chang et al.
Do as We Do, Not as You Think: the Conformity of Large Language Models
Zhiyuan Weng, Guikun Chen, Wenguan Wang
DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity
Zhen Qin, Zhuqing Liu, Songtao Lu et al.
Graph Neural Networks Gone Hogwild
Olga Solodova, Nick Richardson, Deniz Oktay et al.
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models
Logan Cross, Violet Xiang, Agam Bhatia et al.
Knowledge Starts with Practice: Knowledge-Aware Exercise Generative Recommendation with Adaptive Multi-Agent Cooperation
Yangtao Zhou, Hua Chu, chen et al.
KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems
Hancheng Ye, Zhengqi Gao, Mingyuan Ma et al.
Learning to Communicate Through Implicit Communication Channels
Han Wang, Binbin Chen, zhang et al.
Lessons Learned: A Multi-Agent Framework for Code LLMs to Learn and Improve
Yuanzhe Liu, Ryan Deng, Tim Kaler et al.
Many LLMs Are More Utilitarian Than One
Anita Keshmirian, Razan Baltaji, Babak Hemmatian et al.
MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
Xuanming Zhang, Yuxuan Chen, Samuel (Min-Hsuan) Yeh et al.
MLZero: A Multi-Agent System for End-to-end Machine Learning Automation
Haoyang Fang, Boran Han, Nick Erickson et al.
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Observation Delays
Songchen Fu, Siang Chen, Shaojing Zhao et al.
SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning
Wanjia Zhao, Mert Yuksekgonul, Shirley Wu et al.
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
Lorenzo Magnino, Kai Shao, Zida Wu et al.
Towards Doctor-Like Reasoning: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients
Yuxing Lu, Gecheng Fu, Wei Wu et al.
Towards Principled Unsupervised Multi-Agent Reinforcement Learning
Riccardo Zamboni, Mirco Mutti, Marcello Restelli