71 citations · 90 across the 22 of their papers we have counts for
25 papers
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…
BurstEngine: an Efficient Distributed Framework for Training Transformers on Extremely Long Sequences of over 1M Tokens
Ao Sun, Weilin Zhao, Xu Han +4
Existing methods for training LLMs on long-sequence data, such as Tensor Parallelism and Context Parallelism, exhibit low Model FLOPs Utilization as sequence lengths and number of…
Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration
Yilong Li, Chen Qian, Yu Xia +12
Large Language Model-based multi-agent systems (MAS) have shown remarkable progress in solving complex tasks through collaborative reasoning and inter-agent critique. However, exis…
Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development
Rennai Qiu, Chen Qian, Ran Li +9
Recent advancements in Large Language Models (LLMs) and autonomous agents have demonstrated remarkable capabilities across various domains. However, standalone agents frequently en…
Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection
Zhuoyang Wu, Xinze Li, Zhenghao Liu +7
Large Language Models (LLMs) have exhibited strong reasoning capabilities and achieved remarkable performance in mathematical problem-solving tasks. Recently, distilling reasoning…
Multi-Agent Collaboration via Evolving Orchestration
Yufan Dang, Chen Qian, Xueheng Luo +11
Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-s…
Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models
Shuliang Liu, Xinze Li, Zhenghao Liu +6
Retrieval-Augmented Generation (RAG) has proven its effectiveness in alleviating hallucinations for Large Language Models (LLMs). However, existing automated evaluation metrics can…
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
Xin Li, Qizhi Chu, Yubin Chen +7
Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing. Existing LLM-based graph analysis approaches either integr…
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System
Weize Chen, Jiarui Yuan, Chen Qian +3
Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving, yet they still face critical challenges: low communication ef…
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
Xin Li, Weize Chen, Qizhi Chu +9
The need to analyze graphs is ubiquitous across various fields, from social networks to biological research and recommendation systems. Therefore, enabling the ability of large lan…
Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence
Weize Chen, Ziming You, Ran Li +7
The rapid advancement of large language models (LLMs) has paved the way for the development of highly capable autonomous agents. However, existing multi-agent frameworks often stru…
Autonomous Agents for Collaborative Task under Information Asymmetry
Wei Liu, Chenxi Wang, Yifei Wang +7
Large Language Model Multi-Agent Systems (LLM-MAS) have achieved great progress in solving complex tasks. It performs communication among agents within the system to collaborativel…
Multi-Agent Collaboration via Cross-Team Orchestration
Zhuoyun Du, Chen Qian, Wei Liu +9
Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. A representative scenario is in software develo…
Scaling Large Language Model-based Multi-Agent Collaboration
Chen Qian, Zihao Xie, YiFei Wang +9
Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. Ins…
Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training
Ao Sun, Weilin Zhao, Xu Han +5
The emergence of large language models (LLMs) relies heavily on distributed training strategies, among which pipeline parallelism plays a crucial role. As LLMs' training sequence l…
Iterative Experience Refinement of Software-Developing Agents
Chen Qian, Jiahao Li, Yufan Dang +8
Autonomous agents powered by large language models (LLMs) show significant potential for achieving high autonomy in various scenarios such as software development. Recent research…
BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Ao Sun, Weilin Zhao, Xu Han +4
Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these att…
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication
Weize Chen, Chenfei Yuan, Jiarui Yuan +6
Natural language (NL) has long been the predominant format for human cognition and communication, and by extension, has been similarly pivotal in the development and application of…
Experiential Co-Learning of Software-Developing Agents
Chen Qian, Yufan Dang, Jiahao Li +9
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scenario…