71 citations · 90 across the 22 of their papers we have counts for
6 papers · 1 filter
MASBench: Benchmarking LLM-based Multi-Agent Collaboration under Partial Observability
Qizhi Chu, Zekai Yu, Sijie Wen +5
Large language models (LLMs) have progressively evolved into the core of autonomous agents. Building on this progress, LLM-based multi-agent systems (MAS) coordinate multiple agent…
HiSkill: Empowering LLM Agents with Hierarchical Skill Graphs
Yu Hao, Jinxuan Cai, Qi Zhang +4
Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks. However, existing trajectory-…
Self-Aware Recursively Self-Improving Agents for Personal Singularity: A Goal-, Scope-, Tool-, and Benchmark-Driven Multi-Agent Architecture
Chengshuai Yang
Large language model (LLM) agents can plan, use tools, maintain memory, and execute long-horizon tasks. This paper proposes Self-Aware Recursively Self-Improving (SARSI) agents: go…
Unlocking Strong Supervision: A Data-Centric Study of General-Purpose Audio Pre-Training Methods
Xuanru Zhou, Yiwen Shao, Wei-Cheng Tseng +1
Current audio pre-training seeks to learn unified representations for broad audio understanding tasks, but it remains fragmented and is fundamentally bottlenecked by its reliance o…
MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing
Yang Liu, Jinxuan Cai, Yishen Li +6
Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows ca…
Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science
Jingru Fan, Dewen Liu, Yufan Dang +15
Recent advancements in Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS), demonstrating significant effectiveness across a wide range…