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109 changes: 74 additions & 35 deletions MultiAgentEbook/index.html
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<meta http-equiv="Cache-Control" content="no-store">
<meta http-equiv="Pragma" content="no-cache">
<meta http-equiv="Expires" content="0">

<link rel="icon" type="image/png" sizes="32x32" href="./images/logo.png" />

<title>Multi-Agent Research Outline</title>
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Comprehensive Outline of Large Language Model-based Multi-Agent Research
</h1>
<p>
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.
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.
</p>
<p>Initiated by the <a href="/~https://github.com/OpenBMB/ChatDev"><b>ChatDev</b></a> Group at Tsinghua University.</p>
<p>Initiated by the <a href="/~https://github.com/OpenBMB/ChatDev"><b>ChatDev</b></a> Group at Tsinghua
University.</p>
<div class="btn-group">
<a href="#book" class="btn clr2">Start Reading</a>
<a href="#more-works" class="btn clr3">Learn More</a>
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<img src="./images/bg-pattern.svg" class="bg-pattern" alt="background-pattern" />
<h2 class="section-heading text-center">Multi-Agent Directions</h2>
<div class="content" align="center">
<p>
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.
</p>
<p>
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.
</p>
</div>
<div class="tab-container">
<ul class="tab-nav flex">
Expand All @@ -79,7 +85,10 @@ <h2 class="section-heading text-center">Multi-Agent Directions</h2>
<div class="tab-col-right">
<div class="content">
<p>
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.
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.
</p>
<img src="./images/multi_agent_framework_ts.png" alt="Dataset cover" width="660" align="center" />
</div>
Expand All @@ -92,7 +101,9 @@ <h2 class="section-heading text-center">Multi-Agent Directions</h2>
<div class="tab-col-right">
<div class="content">
<p>
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.
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.
</p>
<img src="./images/multi_agent_framework_ss.png" alt="Dataset cover" width="660" align="center" />
</div>
Expand All @@ -106,9 +117,10 @@ <h2 class="section-heading text-center">Multi-Agent Directions</h2>
<div class="container" id="book">
<h5 class="section-heading text-center">Dive into Each Chapter</h5>
<div class="content" align="center">
<p>
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.
</p>
<p>
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.
</p>
</div>
<div class="browser-cards">
<div class="card">
Expand Down Expand Up @@ -142,8 +154,9 @@ <h4>§4: Simulation</h4>
<section class="cards_row">
<h2 class="section-heading text-center">Learn More</h2>
<div class="content" align="center">
<p>
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.
<p>
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.
</p>
</div>
<div class="container" id="more-works">
Expand All @@ -152,73 +165,91 @@ <h2 class="section-heading text-center">Learn More</h2>
<img src="./images/chatdev_cover.png" alt="Systems cover" />
<h4>ChatDev</h4>
<p>Multi-Agent Collaboration for Software Development</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/iagents_cover.png" alt="Systems cover" />
<h4>iAgents</h4>
<p>Bijective Social Networks of Humans and Agents
</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/agentverse_cover.png" alt="Systems cover" />
<h4>AgentVerse</h4>
<p>General-Purpose Multi-Agent Framework</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/colearning_cover.png" alt="Benchmark cover" />
<h4>Co-Learning</h4>
<p>Cross-Task Experience Co-Leaning for Mutual Growth</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/ei_cover.png" alt="Dataset cover" />
<h4>Co-Evolving</h4>
<p>Continuous Experience Refinement over Time</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/organization.png" alt="Dataset cover" />
<h4>MacNet</h4>
<p>Exploring Collaborative Scaling Law</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/ctc_cover.png" alt="Systems cover" />
<h4>CTC</h4>
<p>Cross-Team Multi-Agent Orchestration</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/chateval_cover.png" alt="Benchmark cover" />
<h4>ChatEval</h4>
<p>Communication for Automated Evaluation</p>
<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>
<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>
<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>
<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>
</div>

<div class="card">
<img src="./images/autoform_cover.png" alt="Benchmark cover" />
<h4>AutoForm</h4>
<p>Finding Effective Communication Protocals</p>
<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>
<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>
<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>
<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>
</div>

</div>
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</svg>
</button>
<p>
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.
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.
</p>
</div>
<div class="question">
Expand All @@ -249,7 +281,9 @@ <h2 class="section-heading">Frequently Asked Questions</h2>
</svg>
</button>
<p>
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>.
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>.
</p>
</div>
<div class="question">
Expand All @@ -260,7 +294,8 @@ <h2 class="section-heading">Frequently Asked Questions</h2>
</svg>
</button>
<p>
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>.
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>.
</p>
</div>
</div>
Expand All @@ -269,11 +304,15 @@ <h2 class="section-heading">Frequently Asked Questions</h2>

<div class="attribution">
<p>
Initiated by the <a href="/~https://github.com/OpenBMB/ChatDev" target="_blank">ChatDev</a> Group, Tsinghua University
Initiated by the <a href="/~https://github.com/OpenBMB/ChatDev" target="_blank">ChatDev</a> Group, Tsinghua
University
<br>Contact us via <a href="mailto:qianc62@gmail.com">qianc62@gmail.com</a>
<br>
<span style="font-size: 14px;" id="busuanzi_container_site_pv">Total PV <span style="font-size: 14px;" id="busuanzi_value_site_pv"></span></span>
</p>
</div>
<script src="main.js"></script>
<script async src="//busuanzi.ibruce.info/busuanzi/2.3/busuanzi.pure.mini.js"></script>
</body>

</html>
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hamburger.src = toggle ? srcClose : srcHam;
navList.classList.toggle("active");
logoContainer.classList.toggle('active');
document.body.style.position = toggle ? 'fixed' :'static';
document.body.style.position = toggle ? 'fixed' : 'static';
});

tabNavList.forEach((item, index, array) => {
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item.addEventListener("click", () => {
item.classList.toggle("open");
});
});
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36 changes: 0 additions & 36 deletions MultiAgentEbook/papers.csv
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Expand Up @@ -728,42 +728,6 @@ macroeconomic phenomena compared to exist-
ing rule-based or learning-based agents. Our
codes are released at /~https://github.com/
tsinghua-fib-lab/ACL24-EconAgent.",https://arxiv.org/abs/2310.10436,Simulation,Artificial Intelligence (cs.AI),econagent_large_language_model-empowered_20231016,Tsinghua University
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
growing emphasis on data-driven modeling in
macroeconomics, with agent-based modeling
(ABM) emerging as a prominent bottom-up
simulation paradigm. In ABM, agents (e.g.,
households, firms) interact within a macroe-
conomic environment, collectively generating
market dynamics. Existing agent modeling typ-
ically employs predetermined rules or learning-
based neural networks for decision-making.
However, customizing each agent presents sig-
nificant challenges, complicating the modeling
of agent heterogeneity. Additionally, the in-
fluence of multi-period market dynamics and
multifaceted macroeconomic factors are often
overlooked in decision-making processes. In
this work, we introduce EconAgent, a large
language model-empowered agent with human-
like characteristics for macroeconomic simu-
lation. We first construct a simulation envi-
ronment that incorporates various market dy-
namics driven by agents’ decisions regarding
work and consumption. Through the perception
module, we create heterogeneous agents with
distinct decision-making mechanisms.
Fur-
thermore, we model the impact of macroeco-
nomic trends using a memory module, which
allows agents to reflect on past individual ex-
periences and market dynamics. Simulation
experiments show that EconAgent can make
realistic decisions, leading to more reasonable
macroeconomic phenomena compared to exist-
ing rule-based or learning-based agents. Our
codes are released at /~https://github.com/
tsinghua-fib-lab/ACL24-EconAgent.",https://arxiv.org/abs/2310.10436,Simulation,Artificial Intelligence (cs.AI),econagent_large_language_model-empowered_20231016,Tsinghua University
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
ChatGPT have shown remarkable performance
on general language tasks but still struggle on
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