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reformat & delete repetitions & add pv
This commit is contained in:
thinkwee
parent
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<meta http-equiv="Cache-Control" content="no-store">
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<meta http-equiv="Pragma" content="no-cache">
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<meta http-equiv="Expires" content="0">
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<link rel="icon" type="image/png" sizes="32x32" href="./images/logo.png" />
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<title>Multi-Agent Research Outline</title>
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@ -42,9 +41,14 @@
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Comprehensive Outline of Large Language Model-based Multi-Agent Research
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</h1>
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<p>
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This project presents an interactive eBook that compiles <b>an extensive collection of research papers on large language model (LLM)-based multi-agent systems</b>. Organized into multiple chapters and <b>continuously updated</b> with significant research, it strives to provide a comprehensive outline for both researchers and enthusiasts in the field. We welcome <b>ongoing contributions</b> to expand and enhance this resource.
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This project presents an interactive eBook that compiles <b>an extensive collection of research papers on
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large language model (LLM)-based multi-agent systems</b>. Organized into multiple chapters and
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<b>continuously updated</b> with significant research, it strives to provide a comprehensive outline for
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both researchers and enthusiasts in the field. We welcome <b>ongoing contributions</b> to expand and enhance
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this resource.
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</p>
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<p>Initiated by the <a href="https://github.com/OpenBMB/ChatDev"><b>ChatDev</b></a> Group at Tsinghua University.</p>
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<p>Initiated by the <a href="https://github.com/OpenBMB/ChatDev"><b>ChatDev</b></a> Group at Tsinghua
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University.</p>
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<div class="btn-group">
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<a href="#book" class="btn clr2">Start Reading</a>
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<a href="#more-works" class="btn clr3">Learn More</a>
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@ -63,9 +67,11 @@
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<img src="./images/bg-pattern.svg" class="bg-pattern" alt="background-pattern" />
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<h2 class="section-heading text-center">Multi-Agent Directions</h2>
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<div class="content" align="center">
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<p>
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Multi-agent systems are currently classified into two categories based on whether the agents are designed to achieve <b>specific task goals under external human instructions</b>: task-solving-oriented systems and social-simulation-oriented systems.
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</p>
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<p>
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Multi-agent systems are currently classified into two categories based on whether the agents are designed to
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achieve <b>specific task goals under external human instructions</b>: task-solving-oriented systems and
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social-simulation-oriented systems.
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</p>
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</div>
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<div class="tab-container">
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<ul class="tab-nav flex">
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@ -79,7 +85,10 @@
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<div class="tab-col-right">
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<div class="content">
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<p>
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Task solving-oriented multi-agent systems employ autonomous agents working collaboratively to tackle complex problems. Cutting-edge research in this direction revolves around three primary areas: facilitating communication among agents, designing effective organizational structures for interaction, and exploring how agents co-evolve over time.
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Task solving-oriented multi-agent systems employ autonomous agents working collaboratively to tackle
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complex problems. Cutting-edge research in this direction revolves around three primary areas:
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facilitating communication among agents, designing effective organizational structures for interaction,
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and exploring how agents co-evolve over time.
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</p>
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<img src="./images/multi_agent_framework_ts.png" alt="Dataset cover" width="660" align="center" />
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</div>
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@ -92,7 +101,9 @@
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<div class="tab-col-right">
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<div class="content">
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<p>
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Social simulation-oriented multi-agent systems concentrate on modeling and analyzing the social behaviors of agents, offering valuable insights into human dynamics and enhances the ability to analyze or predict social phenomena.
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Social simulation-oriented multi-agent systems concentrate on modeling and analyzing the social
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behaviors of agents, offering valuable insights into human dynamics and enhances the ability to analyze
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or predict social phenomena.
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</p>
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<img src="./images/multi_agent_framework_ss.png" alt="Dataset cover" width="660" align="center" />
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</div>
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@ -106,9 +117,10 @@
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<div class="container" id="book">
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<h5 class="section-heading text-center">Dive into Each Chapter</h5>
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<div class="content" align="center">
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<p>
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This ebook contains research papers on the multi-agent layer and above, organized into multiple chapters based on proposed core technologies. Let's dive into each section.
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</p>
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<p>
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This ebook contains research papers on the multi-agent layer and above, organized into multiple chapters based
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on proposed core technologies. Let's dive into each section.
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</p>
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</div>
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<div class="browser-cards">
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<div class="card">
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@ -142,8 +154,9 @@
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<section class="cards_row">
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<h2 class="section-heading text-center">Learn More</h2>
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<div class="content" align="center">
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<p>
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In addition to the aforementioned resources, we also feature recent research from our lab. If you find our work of interest, we invite you to read, extend, or collaborate.
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<p>
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In addition to the aforementioned resources, we also feature recent research from our lab. If you find our work
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of interest, we invite you to read, extend, or collaborate.
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</p>
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</div>
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<div class="container" id="more-works">
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@ -152,8 +165,10 @@
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<img src="./images/chatdev_cover.png" alt="Systems cover" />
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<h4>ChatDev</h4>
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<p>Multi-Agent Collaboration for Software Development</p>
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<a href="https://arxiv.org/abs/2307.07924" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2307.07924" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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@ -161,64 +176,80 @@
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<h4>iAgents</h4>
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<p>Bijective Social Networks of Humans and Agents
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</p>
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<a href="https://arxiv.org/abs/2406.14928" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/thinkwee/iAgents" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2406.14928" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/thinkwee/iAgents" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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<img src="./images/agentverse_cover.png" alt="Systems cover" />
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<h4>AgentVerse</h4>
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<p>General-Purpose Multi-Agent Framework</p>
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<a href="https://arxiv.org/abs/2308.10848" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/AgentVerse" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2308.10848" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/AgentVerse" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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<img src="./images/colearning_cover.png" alt="Benchmark cover" />
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<h4>Co-Learning</h4>
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<p>Cross-Task Experience Co-Leaning for Mutual Growth</p>
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<a href="https://arxiv.org/abs/2312.17025" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2312.17025" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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<img src="./images/ei_cover.png" alt="Dataset cover" />
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<h4>Co-Evolving</h4>
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<p>Continuous Experience Refinement over Time</p>
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<a href="https://arxiv.org/abs/2405.04219" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2405.04219" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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<img src="./images/organization.png" alt="Dataset cover" />
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<h4>MacNet</h4>
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<p>Exploring Collaborative Scaling Law</p>
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<a href="https://arxiv.org/abs/2406.07155" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2406.07155" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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<img src="./images/ctc_cover.png" alt="Systems cover" />
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<h4>CTC</h4>
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<p>Cross-Team Multi-Agent Orchestration</p>
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<a href="https://arxiv.org/abs/2406.08979" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2406.08979" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/OpenBMB/ChatDev" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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<img src="./images/chateval_cover.png" alt="Benchmark cover" />
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<h4>ChatEval</h4>
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<p>Communication for Automated Evaluation</p>
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<a href="https://arxiv.org/abs/2308.07201" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/thunlp/ChatEval" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2308.07201" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/thunlp/ChatEval" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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<div class="card">
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<img src="./images/autoform_cover.png" alt="Benchmark cover" />
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<h4>AutoForm</h4>
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<p>Finding Effective Communication Protocals</p>
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<a href="https://arxiv.org/abs/2402.18439" class="btnsmall paper"><span class="icon"><img src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/thunlp/AutoForm" class="btnsmall code"><span class="icon"><img src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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<a href="https://arxiv.org/abs/2402.18439" class="btnsmall paper"><span class="icon"><img
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src="images/pdf_normal.png" alt="PDF Icon"></span>Paper</a>
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<a href="https://github.com/thunlp/AutoForm" class="btnsmall code"><span class="icon"><img
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src="images/github_normal.png" alt="GitHub Icon"></span>Code</a>
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</div>
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</div>
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</svg>
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</button>
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<p>
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This ebook gathers leading research on LLM-powered multi-agent systems since 2023, categorized by key perspectives in the field. As this area rapidly evolves, updates will be ongoing.
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This ebook gathers leading research on LLM-powered multi-agent systems since 2023, categorized by key
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perspectives in the field. As this area rapidly evolves, updates will be ongoing.
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</p>
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</div>
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<div class="question">
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@ -249,7 +281,9 @@
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</svg>
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</button>
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<p>
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We encourage open-source collaboration on this project. You can contribute by submitting a pull request with detailed metadata for notable papers in the <a href="https://github.com/OpenBMB/ChatDev/tree/main/MultiAgentEbook/papers.csv">table</a>.
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We encourage open-source collaboration on this project. You can contribute by submitting a pull request with
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detailed metadata for notable papers in the <a
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href="https://github.com/OpenBMB/ChatDev/tree/main/MultiAgentEbook/papers.csv">table</a>.
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</p>
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</div>
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<div class="question">
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@ -260,7 +294,8 @@
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</svg>
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</button>
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<p>
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You can download all ebook content in CSV format directly from <a href="https://github.com/OpenBMB/ChatDev/tree/main/MultiAgentEbook/papers.csv">here</a>.
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You can download all ebook content in CSV format directly from <a
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href="https://github.com/OpenBMB/ChatDev/tree/main/MultiAgentEbook/papers.csv">here</a>.
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</p>
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</div>
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</div>
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@ -269,11 +304,15 @@
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<div class="attribution">
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<p>
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Initiated by the <a href="https://github.com/OpenBMB/ChatDev" target="_blank">ChatDev</a> Group, Tsinghua University
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Initiated by the <a href="https://github.com/OpenBMB/ChatDev" target="_blank">ChatDev</a> Group, Tsinghua
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University
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<br>Contact us via <a href="mailto:qianc62@gmail.com">qianc62@gmail.com</a>
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<br>
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<span style="font-size: 14px;" id="busuanzi_container_site_pv">Total PV <span style="font-size: 14px;" id="busuanzi_value_site_pv"></span></span>
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</p>
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</div>
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<script src="main.js"></script>
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<script async src="//busuanzi.ibruce.info/busuanzi/2.3/busuanzi.pure.mini.js"></script>
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</body>
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</html>
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@ -15,7 +15,7 @@ hamburger.addEventListener("click", function () {
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hamburger.src = toggle ? srcClose : srcHam;
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navList.classList.toggle("active");
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logoContainer.classList.toggle('active');
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document.body.style.position = toggle ? 'fixed' :'static';
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document.body.style.position = toggle ? 'fixed' : 'static';
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});
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tabNavList.forEach((item, index, array) => {
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item.addEventListener("click", () => {
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item.classList.toggle("open");
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});
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});
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});
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@ -728,42 +728,6 @@ macroeconomic phenomena compared to exist-
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ing rule-based or learning-based agents. Our
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codes are released at https://github.com/
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tsinghua-fib-lab/ACL24-EconAgent.",https://arxiv.org/abs/2310.10436,Simulation,Artificial Intelligence (cs.AI),econagent_large_language_model-empowered_20231016,Tsinghua University
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EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities,"Nian Li, Chen Gao, Mingyu Li, Yong Li, Qingmin Liao",2023.10.16,"The advent of artificial intelligence has led to a
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growing emphasis on data-driven modeling in
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macroeconomics, with agent-based modeling
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(ABM) emerging as a prominent bottom-up
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simulation paradigm. In ABM, agents (e.g.,
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households, firms) interact within a macroe-
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conomic environment, collectively generating
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market dynamics. Existing agent modeling typ-
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ically employs predetermined rules or learning-
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based neural networks for decision-making.
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However, customizing each agent presents sig-
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nificant challenges, complicating the modeling
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of agent heterogeneity. Additionally, the in-
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fluence of multi-period market dynamics and
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multifaceted macroeconomic factors are often
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overlooked in decision-making processes. In
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this work, we introduce EconAgent, a large
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language model-empowered agent with human-
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like characteristics for macroeconomic simu-
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lation. We first construct a simulation envi-
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ronment that incorporates various market dy-
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namics driven by agents’ decisions regarding
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work and consumption. Through the perception
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module, we create heterogeneous agents with
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distinct decision-making mechanisms.
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Fur-
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thermore, we model the impact of macroeco-
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nomic trends using a memory module, which
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allows agents to reflect on past individual ex-
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periences and market dynamics. Simulation
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experiments show that EconAgent can make
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realistic decisions, leading to more reasonable
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macroeconomic phenomena compared to exist-
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ing rule-based or learning-based agents. Our
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codes are released at https://github.com/
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tsinghua-fib-lab/ACL24-EconAgent.",https://arxiv.org/abs/2310.10436,Simulation,Artificial Intelligence (cs.AI),econagent_large_language_model-empowered_20231016,Tsinghua University
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Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate,"Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, Shuming Shi",2023.5.30,"Modern large language models (LLMs) like
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ChatGPT have shown remarkable performance
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on general language tasks but still struggle on
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|||
|
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