6 papers
EvolveMAS: A Unified Self-Evolution Framework for Decentralized Multi-Agent Systems
Yalun Wu, Qiwei Ma, Bingbing Xu +10
Decentralized multi-agent systems (MAS) enable large language model agents to interact without centralized control. Agents need to adapt to other agents at test time and reuse accu…
When Extremists Enter LLM Societies: Investigating Extreme Behavior in Multi-Agent Systems
Guo Chen, Huaijin Xie, Ziwen Li +5
Large language model (LLM) agents increasingly serve as computational actors in multi-agent social simulations. While existing work emphasizes representative personas and average-c…
From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations
Ping Wang, Xiangguo Sun, Bingbing Xu +2
Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon we attribute to fact perturba…
ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration
Guo Chen, Ziwen Li, Reed Li +4
Multi-agent systems (MAS) built on Large Language Models (LLMs) are proliferating rapidly, but their heterogeneous execution traces provide no common basis for evaluation across me…
Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation
Jiani Luo, Xiaoyan Zhao, Yang Zhang +4
Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interaction and shape appropriate resp…
Towards Knowledgeable Deep Research: Framework and Benchmark
Wenxuan Liu, Zixuan Li, Long Bai +13
Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive reports. In contrast to existing…