摘要

人工智能代理經(jīng)歷了從強化學(xué)習(xí)（RL）早期主導(dǎo)地位到由大型語言模型（LLMs）驅(qū)動的智能代理崛起的范式轉(zhuǎn)變，現(xiàn)在正進(jìn)一步邁向RL和LLM能力的協(xié)同融合。這一進(jìn)步賦予了人工智能代理越來越強大的能力。盡管取得了這些進(jìn)展，但要完成復(fù)雜的現(xiàn)實世界任務(wù)，代理需要有效規(guī)劃和執(zhí)行，保持可靠的記憶，并與其他代理順暢協(xié)調(diào)。實現(xiàn)這些能力涉及應(yīng)對始終存在的復(fù)雜信息、操作和互動。鑒于這一挑戰(zhàn)，數(shù)據(jù)結(jié)構(gòu)化可以通過將復(fù)雜無序的數(shù)據(jù)轉(zhuǎn)換為結(jié)構(gòu)良好的形式，使代理能夠更有效地理解和處理，從而發(fā)揮出有希望的作用。在此背景下，圖譜在組織、管理和利用復(fù)雜數(shù)據(jù)關(guān)系方面具有天然優(yōu)勢，為支持高級人工智能代理所需的能力提供了強大的數(shù)據(jù)結(jié)構(gòu)化范式。為此，本調(diào)查首次系統(tǒng)地回顧了圖譜如何賦能人工智能代理。具體來說，我們探討了圖形技術(shù)與核心智能體功能的整合，重點介紹了顯著的應(yīng)用案例，并識別了未來研究的潛在途徑。通過全面審視這一迅速發(fā)展的交叉領(lǐng)域，我們希望激發(fā)下一代AI智能體的發(fā)展，使其能夠應(yīng)對日益復(fù)雜的圖譜挑戰(zhàn)。相關(guān)資源已在GitHub鏈接中收集并持續(xù)更新供社區(qū)參考。

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities

??https://github.com/YuanchenBei/Awesome-Graphs-Meet-Agents??

核心速覽

研究背景

1. 研究問題：這篇文章要解決的問題是如何通過圖技術(shù)增強AI代理的功能，包括規(guī)劃、執(zhí)行、記憶和多智能體協(xié)調(diào)。具體來說，圖技術(shù)如何幫助AI代理更有效地處理復(fù)雜任務(wù)中的信息、操作和交互。
2. 研究難點：該問題的研究難點包括：處理復(fù)雜任務(wù)中的非結(jié)構(gòu)化數(shù)據(jù)、在多智能體系統(tǒng)中實現(xiàn)有效的信息傳遞和協(xié)調(diào)、以及在動態(tài)環(huán)境中維護(hù)和更新記憶。
3. 相關(guān)工作：該問題的研究相關(guān)工作包括強化學(xué)習(xí)（RL）和大語言模型（LLM）在AI代理中的應(yīng)用，以及圖技術(shù)在數(shù)據(jù)組織和知識提取方面的成功應(yīng)用。

研究方法

這篇論文提出了通過圖技術(shù)來增強AI代理功能的方法。具體來說，

1. 圖在代理規(guī)劃中的應(yīng)用：圖可以用于組織任務(wù)推理形式、安排任務(wù)分解過程，并構(gòu)建高效的任務(wù)決策搜索過程。

• 任務(wù)推理：使用知識圖譜輔助推理和結(jié)構(gòu)組織推理來增強LLM代理的任務(wù)理解能力。
• 任務(wù)分解：構(gòu)建任務(wù)依賴圖（TDG）來表示子任務(wù)的依賴關(guān)系，并優(yōu)化執(zhí)行路徑。
• 任務(wù)決策搜索：使用狀態(tài)空間圖（SSG）來表示狀態(tài)之間的轉(zhuǎn)換，并通過搜索算法（如蒙特卡羅樹搜索）來優(yōu)化決策過程。

2.圖在代理執(zhí)行中的應(yīng)用：圖可以幫助組織工具的使用和環(huán)境交互。

• 工具使用：構(gòu)建工具圖來清晰地顯示工具之間的連接，使代理能夠高效地使用和管理大量工具。
• 環(huán)境交互：使用場景圖來編碼視覺場景中的對象及其空間或語義關(guān)系，并采用啟發(fā)式方法和學(xué)習(xí)方法來建模這些關(guān)系。

3.圖在代理記憶中的應(yīng)用：圖結(jié)構(gòu)記憶可以有效地揭示各種信息之間的潛在關(guān)聯(lián)。

• 記憶組織：將知識和經(jīng)驗存儲為相互連接的表示，使用知識圖譜和其他結(jié)構(gòu)化形式來組織長期記憶。
• 記憶檢索：使用圖檢索增強生成（graph-based RAG）來準(zhǔn)確高效地檢索有用信息。
• 記憶維護(hù)：動態(tài)更新和細(xì)化記憶表示和圖拓?fù)?，以響?yīng)新的經(jīng)驗和交互。

4.圖在多智能體協(xié)調(diào)中的應(yīng)用：圖可以用于建模智能體之間的通信路徑和任務(wù)分配。

• 任務(wù)特定關(guān)系：使用任務(wù)依賴圖和任務(wù)分配圖來優(yōu)化任務(wù)執(zhí)行和信息交換。
• 環(huán)境特定關(guān)系：根據(jù)特定環(huán)境的特征動態(tài)學(xué)習(xí)智能體之間的關(guān)系權(quán)重。
• 協(xié)調(diào)拓?fù)鋬?yōu)化：通過邊重要性測量、圖自編碼器優(yōu)化和強化學(xué)習(xí)來優(yōu)化多智能體系統(tǒng)的通信拓?fù)洹?/li>

結(jié)果與分析

1. 科學(xué)計算：圖學(xué)習(xí)結(jié)合代理系統(tǒng)在科學(xué)計算領(lǐng)域表現(xiàn)出顯著潛力，特別是在自動化科學(xué)發(fā)現(xiàn)和生物信息學(xué)分析方面。
2. 具身AI：圖基表示在具身AI中提供了強大的工具，增強了場景理解能力，并支持更明智的決策。
3. 游戲AI：圖結(jié)構(gòu)在表示和建模游戲AI中越來越受到關(guān)注，特別是在多智能體游戲和文本游戲中。
4. 代理信息檢索：圖學(xué)習(xí)可以支持結(jié)構(gòu)化的檢索規(guī)劃和自適應(yīng)推理，提高復(fù)雜任務(wù)中的信息檢索效率。
5. 工業(yè)和自動化系統(tǒng)：圖組織和學(xué)習(xí)可以在工業(yè)系統(tǒng)中實現(xiàn)高效的性能，增強系統(tǒng)的可擴(kuò)展性、動態(tài)演化和魯棒性。
6. 人類社會：LLM代理在分析和模擬人類社會行為方面顯示出巨大潛力，特別是在社交網(wǎng)絡(luò)和信息傳播方面。

總體結(jié)論

這篇論文全面系統(tǒng)地回顧了圖技術(shù)與AI代理的交叉領(lǐng)域，探討了圖技術(shù)如何增強代理的規(guī)劃、執(zhí)行、記憶和多智能體協(xié)調(diào)功能。此外，還研究了代理范式如何反過來增強圖學(xué)習(xí)?；谠敿?xì)的回顧，論文總結(jié)了有意義的應(yīng)用、開放問題和未來的研究方向，為下一代面對日益復(fù)雜和混亂任務(wù)信息的代理提供了新的潛在方法。相關(guān)資源已在Github鏈接中組織并持續(xù)更新。

論文評價

優(yōu)點與創(chuàng)新

1. 系統(tǒng)綜述：本文提供了圖技術(shù)與AI代理交叉領(lǐng)域的第一個全面系統(tǒng)綜述，涵蓋了從基于強化學(xué)習(xí)（RL）到基于大型語言模型（LLM）的代理范式。
2. 新穎的分類視角：論文引入了一種新穎的分類視角，探討了圖如何增強代理的核心功能：規(guī)劃、執(zhí)行、記憶和多代理協(xié)調(diào)。
3. 雙向創(chuàng)新：論文不僅討論了圖如何增強代理功能，還探討了代理如何反過來推動圖學(xué)習(xí)的進(jìn)步，強調(diào)了雙向創(chuàng)新和整體視角。
4. 應(yīng)用和未來機會：基于綜述，論文進(jìn)一步討論了圖增強AI代理的有意義應(yīng)用、關(guān)鍵挑戰(zhàn)和未來機會。
5. 資源更新：相關(guān)資源在Github鏈接中收集并持續(xù)更新，為研究和工業(yè)社區(qū)提供了寶貴的參考資料。

不足與反思

1. 基準(zhǔn)評估：現(xiàn)有的基準(zhǔn)測試在任務(wù)定義或評估數(shù)據(jù)上存在差異，使得統(tǒng)一評估變得具有挑戰(zhàn)性。此外，針對多代理推理、大規(guī)模動態(tài)環(huán)境中的記憶和協(xié)作等新興復(fù)雜場景，缺乏以圖為中心的代理基準(zhǔn)。
2. 圖基礎(chǔ)模型：盡管有許多基于圖的圖學(xué)習(xí)方法，但在代理功能中廣泛使用的圖操作符仍然缺乏。開發(fā)有效的圖基礎(chǔ)模型（GFMs）是一個有前景的方向，特別是從效果、可解釋性和可擴(kuò)展性（EES）的角度設(shè)計GFMs。
3. 隱私和安全：圖組織和學(xué)習(xí)在建立實體之間的連接時可能會帶來安全問題，包括數(shù)據(jù)隱私和攻擊防御。未來的研究應(yīng)專注于設(shè)計更安全的代理協(xié)調(diào)和環(huán)境交互策略。
4. 多模態(tài)代理：盡管LLM代理在語言空間中取得了顯著進(jìn)展，但多模態(tài)代理（能夠理解和整合文本、視覺和語音等信息流的代理）的研究仍在不斷發(fā)展。圖學(xué)習(xí)可以在多模態(tài)數(shù)據(jù)的抽象和連接中發(fā)揮重要作用。
5. 模型上下文協(xié)議（MCP）：MCP協(xié)議提供了一種標(biāo)準(zhǔn)化手段，用于無縫集成代理應(yīng)用和外部數(shù)據(jù)源、工具和服務(wù)。未來的研究可以探索如何利用圖學(xué)習(xí)增強MCP的兩個有前景的方面：高效的數(shù)據(jù)集成和個性化推薦。
6. 開放代理網(wǎng)絡(luò)（OAN）：一個開放的代理網(wǎng)絡(luò)（OAN）將是一個公共、去中心化的網(wǎng)絡(luò)，其中代理被注冊和編排。未來的研究可以探索如何在OAN中實現(xiàn)圖學(xué)習(xí)，以提高代理網(wǎng)絡(luò)的效率和風(fēng)險控制能力。

關(guān)鍵問題及回答

問題1：圖技術(shù)在AI代理的規(guī)劃功能中具體有哪些應(yīng)用？

1. 任務(wù)推理：使用知識圖譜輔助推理和結(jié)構(gòu)組織推理來增強LLM代理的任務(wù)理解能力。例如，QA-GNN通過結(jié)合語言模型和輔助知識圖譜來提高問答系統(tǒng)的性能，而ToG和KG-CoT則利用基于知識圖譜的檢索增強生成（RAG）來提升LLM代理的任務(wù)推理能力。
2. 任務(wù)分解：構(gòu)建任務(wù)依賴圖（TDG）來表示子任務(wù)的依賴關(guān)系，并優(yōu)化執(zhí)行路徑。TDG是一種有向無環(huán)圖（DAG），用于表示任務(wù)之間的依賴關(guān)系，從而幫助代理識別和執(zhí)行有效的任務(wù)子序列。
3. 任務(wù)決策搜索：使用狀態(tài)空間圖（SSG）來表示狀態(tài)之間的轉(zhuǎn)換，并通過搜索算法（如蒙特卡羅樹搜索）來優(yōu)化決策過程。SSG通過將狀態(tài)和狀態(tài)之間的轉(zhuǎn)換表示為圖的節(jié)點和邊，幫助代理在復(fù)雜環(huán)境中進(jìn)行有效的決策。

問題2：圖技術(shù)在AI代理的執(zhí)行功能中有哪些具體應(yīng)用？

1. 工具使用：構(gòu)建工具圖來清晰地顯示工具之間的連接，使代理能夠高效地使用和管理大量工具。例如，GPTSwarm通過將代理抽象為有向圖（DAG），每個節(jié)點代表一個函數(shù)，邊表示信息流，從而幫助代理高效地調(diào)用和管理工具。
2. 環(huán)境交互：使用場景圖來編碼視覺場景中的對象及其空間或語義關(guān)系，并采用啟發(fā)式方法和學(xué)習(xí)方法來建模這些關(guān)系。場景圖通過將對象及其關(guān)系表示為圖的節(jié)點和邊，幫助代理更好地理解和交互復(fù)雜的環(huán)境。

問題3：圖技術(shù)在AI代理的記憶功能中有哪些具體應(yīng)用？

1. 記憶組織：將知識和經(jīng)驗存儲為相互連接的表示，使用知識圖譜和其他結(jié)構(gòu)化形式來組織長期記憶。例如，AriGraph將代理的記憶表示為結(jié)構(gòu)化的知識圖譜，包含語義事實和事件，從而幫助代理回憶復(fù)雜的結(jié)構(gòu)和關(guān)系。
2. 記憶檢索：使用圖檢索增強生成（graph-based RAG）來準(zhǔn)確高效地檢索有用信息。例如，G-Retriever和GFM-RAG通過結(jié)合語義相似性和圖度量來設(shè)計定制的檢索器，從而提高信息檢索的準(zhǔn)確性和效率。
3. 記憶維護(hù)：動態(tài)更新和細(xì)化記憶表示和圖拓?fù)?，以響?yīng)新的經(jīng)驗和交互。例如，A-MEM通過動態(tài)索引和鏈接創(chuàng)建互聯(lián)的知識網(wǎng)絡(luò)，從而允許代理在不斷變化的環(huán)境中持續(xù)改進(jìn)其記憶表示。

?? Taxonomy

Graph for Agent Planning

Task Reasoning

Knowledge Graph-Auxiliary Reasoning

• (NAACL 2021) QA-GNN: Reasoning with language models and knowledge graphs for question answering [Paper] [Code]
• (ICLR 2024) Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph [Paper] [Code]
• (ICLR 2024) Reasoning on graphs: Faithful and interpretable large language model reasoning [Paper] [Code]
• (IJCAI 2024) Kg-cot: Chain-of-thought prompting of large language models over knowledge graphs for knowledge-aware question answering [Paper]
• (ACL 2024) Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models [Paper] [Code]
• (WWW 2025) Paths-over-graph: Knowledge graph empowered large language model reasoning [Paper]

Structure-Organized Reasoning

• (NeurIPS 2023) Tree of thoughts: Deliberate problem solving with large language models [Paper] [Code]
• (NAACL 2024) GoT: Effective Graph-of-Thought Reasoning in Language Models [Paper] [Code]
• (AAAI 2024) Graph of thoughts: Solving elaborate problems with large language models [Paper] [Code]
• (AAAI 2025) Ratt: A thought structure for coherent and correct llm reasoning [Paper] [Code]
• (Arxiv 2025) Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning [Paper]

Task Decomposition

• (CoLM 2024) Agentkit: Structured LLM reasoning with dynamic graphs [Paper] [Code]
• (TMLR 2024) Feudal Graph Reinforcement Learning [Paper]
• (NeurIPS 2024) Can Graph Learning Improve Planning in LLM-based Agents? [Paper] [Code]
• (ACL 2024) Villageragent: A graph-based multi-agent framework for coordinating complex task dependencies in minecraft [Paper] [Code]
• (Arxiv 2024) DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning [Paper] [Demo]
• (ICRA 2025) Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy [Paper]
• (Arxiv 2025) DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems [Paper]
• (Arxiv 2025) Plan-over-Graph: Towards Parallelable LLM Agent Schedule [Paper] [Code]

Task Decision Searching

• (NeurIPS 2014) Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning [Paper]
• (AAAI 2018, Best Paper) Memory-augmented monte carlo tree search [Paper]
• (ACML 2020) Monte-Carlo Graph Search: the Value of Merging Similar States [Paper]
• (ICAPS 2021) Improving alphazero using monte-carlo graph search [Paper]
• (AI 2024) Evolving interpretable decision trees for reinforcement learning [Paper]
• (AAMAS 2024) Continuous monte carlo graph search [Paper] [Code]
• (ICLR 2024) Promptagent: Strategic planning with language models enables expert-level prompt optimization [Paper] [Code]
• (ICML 2024) Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models [Paper] [Code]

Graph for Agent Execution

Tool Usage

• (ACL 2024) Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs [Paper] [Code]
• (ECCV 2024) ControlLLM: Augment Language Models with Tools by Searching on Graphs [Paper] [Code]
• (ICML 2024) GPTSwarm: Language Agents as Optimizable Graphs [Paper] [Code]
• (Arxiv 2024) ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph [Paper]
• (Arxiv 2025) Graph RAG-Tool Fusion [Paper] [Code]

Environment Interaction

Heuristic-Based Relationship

• (ICRA 2023) Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand [Paper]
• (COR 2024) A Graph Reinforcement Learning Framework for Neural Adaptive Large Neighbourhood Search [Paper]
• (AAMAS 2024) Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing [Paper] [Code]
• (RAL 2024) Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation [Paper] [Code]
• (Arxiv 2024) PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning [Paper]
• (CVPR 2025) GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration [Paper] [Code]
• (Arxiv 2025) Multi-agent Auto-Bidding with Latent Graph Diffusion Models [Paper]
• (Arxiv 2025) A Schema-Guided Reason-while-Retrieve framework for Reasoning on Scene Graphs with Large-Language-Models (LLMs) [Paper]

Learning-Based Relationship

• (NeurIPS Workshop 2018) Deep Multi-Agent Reinforcement Learning with Relevance Graphs [Paper] [Code]
• (CoRL 2023) Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation [Paper]
• (AAMAS 2023) TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems [Paper] [Code]
• (TAI 2024) Reinforcement Learned Multi–Agent Cooperative Navigation in Hybrid Environment with Relational Graph Learning [Paper]
• (NCA 2024) Graph network-based human movement prediction for socially-aware robot navigation in shared workspaces [Paper]

Graph for Agent Memory

Memory Organization

• (JMS 2021) Towards Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph-Based Multi-Agent Reinforcement Learning Approach [Paper]
• (Arxiv 2024) AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents [Paper]
• (Arxiv 2024) On the Structural Memory of LLM Agents [Paper] [Code]
• (Arxiv 2024) From Local to Global: A GraphRAG Approach to Query-Focused Summarization [Paper] [Code]
• (Arxiv 2024) KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models [Paper] [Code]
• (SIGIR 2025) Enhancing the Patent Matching Capability of Large Language Models via the Memory Graph [Paper] [Code]
• (AAAI 2025) LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning [Paper] [Code]
• (WWW 2025) Graphusion: A RAG Framework for Scientific Knowledge Graph Construction with a Global Perspective [Paper] [Code]
• (Arxiv 2025) Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research [Paper] [Code]

Memory Retrieval

• (NeurIPS 2024) G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [Paper] [Code]
• (Arxiv 2024) LightRAG: Simple and Fast Retrieval-Augmented Generation [Paper] [Code]
• (ICLR 2025) Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [Paper] [Code]
• (NAACL 2025) GRAG: Graph Retrieval-Augmented Generation [Paper] [Code]
• (Arxiv 2025) GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation [Paper] [Code]
• (Arxiv 2025) PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths [Paper] [Code]

Memory Maintenance

• (NeurIPS 2024) HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models [Paper] [Code]
• (Arxiv 2024) LightRAG: Simple and Fast Retrieval-Augmented Generation [Paper] [Code]
• (Arxiv 2024) KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph [Paper]
• (Arxiv 2024) AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents [Paper] [Code]
• (AAAI 2025) LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning [Paper] [Code]
• (Arxiv 2025) Zep: A Temporal Knowledge Graph Architecture for Agent Memory [Paper] [Code]
• (Arxiv 2025) A-Mem: Agentic Memory for LLM Agents [Paper] [Code]
• (Arxiv 2025) InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning [Paper]

Graphs for Multi-Agent Coordination

Coordination Message Passing

Task-Specific Relationship

• (NeurIPS 2022) Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning [Paper]
• (ICRA 2025) Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy [Paper]
• (ICLR 2025) Scaling Large Language Model-based Multi-Agent Collaboration [Paper] [Code]
• (Arxiv 2025) DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems [Paper]
• (Arxiv 2025) MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning [Paper]
• (Arxiv 2025) GNNs as Predictors of Agentic Workflow Performances [Paper] [Code]

Environment-Specific Relationship

• (ICASSP 2021) Graphcomm: A Graph Neural Network Based Method for Multi-Agent Reinforcement Learning [Paper]
• (ITSC 2022) Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Interactive Traffic Scenarios [Paper]
• (TITS 2022) Multi-Agent Trajectory Prediction with Heterogeneous Edge-Enhanced Graph Attention Network [Paper]
• (TCCN 2023) MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering [Paper] [Code]
• (TPAMI 2023) Robust Multi-Agent Communication With Graph Information Bottleneck Optimization [Paper]
• (IROS 2024) Transformer-based Multi-Agent Reinforcement Learning for Generalization of Heterogeneous Multi-Robot Cooperation [Paper]
• (ICLR 2025) Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning [Paper] [Code]

Coordination Topology Optimization

• (AAAI 2020) Multi-Agent Game Abstraction via Graph Attention Neural Network [Paper]
• (AAMAS 2021) Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning [Paper] [Code]
• (AAMAS 2021) Multi-Agent Graph-Attention Communication and Teaming [Paper]
• (NeurIPS 2021) Learning Distilled Collaboration Graph for Multi-Agent Perception [Paper] [Code]
• (TNNLS 2022) Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Networks [Paper]
• (AAMAS 2023) Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation [Paper] [Code]
• (ICLR 2024) Learning Multi-Agent Communication from Graph Modeling Perspective [Paper] [Code]
• (Arxiv 2024) G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks [Paper] [Code]
• (COLM 2024) A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration [Paper] [Code]
• (ICML 2024) GPTSwarm: Language Agents as Optimizable Graphs [Paper] [Code]
• (JCISE 2025) Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach [Paper]
• (ICRA 2025) Reliable and Efficient Multi-Agent Coordination via Graph Neural Network Variational Autoencoders [Paper] [Code]
• (ICLR 2025) Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems [Paper] [Code]
• (Arxiv 2025) Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning [Paper] [Code]
• (Arxiv 2025) Adaptive Graph Pruning for Multi-Agent Communication [Paper]

Agents for Graph Learning

Graph Annotation and Synthesis

• (NeurIPS 2020) Graph Policy Network for Transferable Active Learning on Graphs [Paper] [Code]
• (AAAI 2022) Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning [Paper]
• (Arxiv 2024) Exploring the Potential of Large Language Models in Graph Generation [Paper]
• (Arxiv 2024) LLM-Based Multi-Agent Systems are Scalable Graph Generative Models [Paper] [Code]
• (ICLR Workshop 2025) IGDA: Interactive Graph Discovery through Large Language Model Agents [Paper]
• (Arxiv 2025) Plan-over-Graph: Towards Parallelable LLM Agent Schedule [Paper] [Code]
• (Arxiv 2025) GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments [Paper]

Graph Understanding

• (KDD 2020) Policy-GNN: Aggregation Optimization for Graph Neural Networks [Paper]
• (WWW 2021) SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism [Paper] [Code]
• (NeurIPS 2023) MAG-GNN: Reinforcement Learning Boosted Graph Neural Network [Paper] [Code]
• (ICLR 2023) Agent-based Graph Neural Networks [Paper] [Code]
• (Arxiv 2023) Graph Agent: Explicit Reasoning Agent for Graphs [Paper]
• (Arxiv 2023) A Versatile Graph Learning Approach through LLM-based Agent [Paper]
• (KDD 2024) GraphWiz: An Instruction-Following Language Model for Graph Computational Problems [Paper] [Code]
• (SIGIR 2024) GraphGPT: Graph Instruction Tuning for Large Language Models [Paper] [Code]
• (ICLR 2024) One For All: Towards Training One Graph Model For All Classification Tasks [Paper] [Code]
• (ICML 2024) LLaGA: Large Language and Graph Assistant [Paper] [Code]
• (KDD 2024) ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs [Paper] [Code]
• (Arxiv 2024) GraphAgent: Agentic Graph Language Assistant [Paper] [Code]
• (Arxiv 2024) Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents [Paper]
• (Arxiv 2024) GraphTeam: Facilitating Large Language Model-based Graph Reasoning via Multi-Agent Collaboration [Paper]
• (Arxiv 2024) GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability [Paper] [Code]
• (AAAI 2025) Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience [Paper]

?? Benchmarks and Open-Source Toolkits

General

• (NeurIPS 2021, RL Agent, Multi-Agent Coordination) Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks [Paper] [Code]
• (JMLR 2024, RL Agent, Multi-Agent Coordination) BenchMARL: Benchmarking Multi-Agent Reinforcement Learning [Paper] [Code]
• (JMLR 2025, RL Agent, Agent Memory) Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents [Paper] [Code]
• (EMNLP 2023, LLM Agent, Tool Usage) API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs [Paper] [Code]
• (NeurIPS 2023, LLM Agent, Task Reasoning, Task Decomposition) PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change [Paper] [Code]
• (ICLR 2024, LLM Agent, General) AgentBench: Evaluating LLMs as Agents [Paper] [Code]
• (ICLR 2024, LLM Agent, Tool Usage) ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [Paper] [Code]
• (NeurIPS 2024, LLM Agent, Tool Usage) GTA: A Benchmark for General Tool Agents [Paper] [Code]
• (ICML 2024, LLM Agent, Task Reasoning, Task Decomposition, Tool Usage) TravelPlanner: A Benchmark for Real-World Planning with Language Agents [Paper] [Code]
• (ICLR 2025, LLM Agent, Tool Usage, Agent-Environment Interaction) τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains [Paper] [Code]
• (NAACL 2025, LLM Agent, Tool Usage, Agent-Environment Interaction) ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities [Paper] [Code]
• (Arxiv 2025, LLM Agent, Task Reasoning, Task Decomposition, Multi-Agent Coordination) REALM-Bench: A Real-World Planning Benchmark for LLMs and Multi-Agent Systems [Paper] [Code]

Graph-Related

• (LLM Agent, Tool Usage, Multi-Agent Coordination) LangGraph [Docs] [Code]
• (NeurIPS 2024, LLM Agent, Graph Modeling) GLBench: A Comprehensive Benchmark for Graph with Large Language Models [Paper] [Code]
• (NeurIPS 2024, LLM Agent, Graph Modeling) Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models [Paper] [Code]
• (ICLR 2025, LLM Agent, Graph Modeling) GraphArena: Evaluating and Exploring Large Language Models on Graph Computation [Paper] [Code]
• (ICML 2024, LLM Agent, Task Reasoning, Tool Usage, Multi-Agent Coordination) GPTSwarm: Language Agents as Optimizable Graphs [Paper] [Code]
• (ICLR 2025, LLM Agent, Multi-Agent Coordinatio) Scaling Large Language Model-based Multi-Agent Collaboration [Paper] [Code]
• (Arxiv 2025, LLM Agent, Tool Usage) Graph RAG-Tool Fusion [Paper] [Code]
• (Arxiv 2025, LLM Agent, Task Reasoning, Task Decomposition) GNNs as Predictors of Agentic Workflow Performances [Paper] [Code]

本文轉(zhuǎn)載自??知識圖譜科技??，作者：Wolfgang