71 citations · 113 across the 15 of their papers we have counts for
18 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…
SE-Bench: Benchmarking Self-Evolution with Knowledge Internalization
Jiarui Yuan, Tailin Jin, Weize Chen +1
True self-evolution requires agents to act as lifelong learners that internalize novel experiences to solve future problems. However, rigorously measuring this foundational capabil…
CPMobius: Iterative Coach-Player Reasoning for Data-Free Reinforcement Learning
Ran Li, Zeyuan Liu, Yinghao Chen +8
Large Language Models (LLMs) have demonstrated strong potential in complex reasoning, yet their progress remains fundamentally constrained by reliance on massive high-quality human…
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…
The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training
Weize Chen, Jiarui Yuan, Tailin Jin +4
Recent large language models (LLMs) exhibit impressive reasoning but often over-think, generating excessively long responses that hinder efficiency. We introduce DIET ( DIfficulty-…
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…
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…
Stuffed Mamba: Oversized States Lead to the Inability to Forget
Yingfa Chen, Xinrong Zhang, Shengding Hu +3
Recent advancements in recurrent architectures, such as Mamba and RWKV, have showcased strong language capabilities. Unlike transformer-based models, these architectures encode all…
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…
States Hidden in Hidden States: Implicit Discrete State Representations Emerge in LLMs' Hidden States
Junhao Chen, Shengding Hu, Zhiyuan Liu +1
Large Language Models (LLMs) exhibit emergent abilities that may reveal aspects of their internal mechanisms. We study one such capability: directly performing extended sequences o…
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…
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
Shengding Hu, Yuge Tu, Xu Han +22
The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, pa…
D-Bot: Database Diagnosis System using Large Language Models
Xuanhe Zhou, Guoliang Li, Zhaoyan Sun +6
Database administrators (DBAs) play an important role in managing, maintaining and optimizing database systems. However, it is hard and tedious for DBAs to manage a large number of…
ChatDev: Communicative Agents for Software Development
Chen Qian, Wei Liu, Hongzhang Liu +11
Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a…
Interactive Molecular Discovery with Natural Language
Zheni Zeng, Bangchen Yin, Shipeng Wang +7
Natural language is expected to be a key medium for various human-machine interactions in the era of large language models. When it comes to the biochemistry field, a series of tas…