mlsanityData to 7 Oct 2026

A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration

arXiv 2310.02170 ↗First posted 3 Oct 2023cs.CL cs.AI cs.MAcited by 281

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Recent studies show that collaborating multiple large language model (LLM) powered agents is a promising way for task solving. However, current approaches are constrained by using a fixed number of agents and static communication structures. In this work, we propose automatically selecting a team of agents from candidates to collaborate in a dynamic communication structure toward different tasks and domains. Specifically, we build a framework named Dynamic LLM-Powered Agent Network (DyLAN) for LLM-powered agent collaboration, operating a two-stage paradigm: (1) Team Optimization and (2) Task Solving. During the first stage, we utilize an textitagent selection algorithm, based on an unsupervised metric called textitAgent Importance Score, enabling the selection of best agents according to their contributions in a preliminary trial, oriented to the given task. Then, in the second stage, the selected agents collaborate dynamically according to the query. Empirically, we demonstrate that DyLAN outperforms strong baselines in code generation, decision-making, general reasoning, and arithmetic reasoning tasks with moderate computational cost. On specific subjects in MMLU, selecting a team of agents in the team optimization stage improves accuracy by up to 25.0% in DyLAN.

Key facts. Cited by 281 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 9 of its 48 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

9 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug

Other references · 8

  1. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  2. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  3. 2020CodeBLEU: a Method for Automatic Evaluation of Code Synthesiscited by 468
  4. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352
  5. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
  6. 2023Self-collaboration Code Generation via ChatGPTcited by 255
  7. 2023LLM As DBAcited by 18
  8. 2023GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systemscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 57; 2025: 107; 2026: 116.

281 papers.

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  1. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
  2. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 424
  3. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 397
  4. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 194
  5. 2024More Agents Is All You Needcited by 174
  6. 2024The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Surveycited by 164
  7. 2024MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Makingcited by 162
  8. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  9. 2024Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systemscited by 129
  10. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  11. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  12. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 115
38 more
  1. 2025Multi-Agent Design: Optimizing Agents with Better Prompts and Topologiescited by 106
  2. 2025Multi-Agent Collaboration via Evolving Orchestrationcited by 102
  3. 2025AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systemscited by 86
  4. 2024Language Agents as Optimizable Graphscited by 81
  5. 2024Embodied LLM Agents Learn to Cooperate in Organized Teamscited by 81
  6. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 77
  7. 2024LLM-based Multi-Agent Reinforcement Learning: Current and Future Directionscited by 67
  8. 2024LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Playcited by 65
  9. 2024Can Large Language Model Agents Simulate Human Trust Behavior?cited by 62
  10. 2025KVLink: Accelerating Large Language Models via Efficient KV Cache Reusecited by 60
  11. 2025LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agentscited by 58
  12. 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Surveycited by 56
  13. 2024Learning to Use Tools via Cooperative and Interactive Agentscited by 54
  14. 2025Cache-to-Cache: Direct Semantic Communication Between Large Language Modelscited by 54
  15. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 49
  16. 2025MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systemscited by 45
  17. 2025A Survey on the Optimization of Large Language Model-based Agentscited by 45
  18. 2024Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversationscited by 43
  19. 2024Adaptive In-conversation Team Building for Language Model Agentscited by 38
  20. 2024Cost-Effective Online Multi-LLM Selection with Versatile Reward Modelscited by 36
  21. 2025GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modelingcited by 35
  22. 2024Agent-Oriented Planning in Multi-Agent Systemscited by 34
  23. 2025X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMscited by 33
  24. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  25. 2025Flow: Modularized Agentic Workflow Automationcited by 31
  26. 2025LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospectscited by 31
  27. 2025Internet of Agents: Fundamentals, Applications, and Challengescited by 31
  28. 2025AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMscited by 31
  29. 2024SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agentscited by 30
  30. 2025ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Taskscited by 30
  31. 2025Thought Communication in Multiagent Collaborationcited by 28
  32. 2024Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberationcited by 26
  33. 2024AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaborationcited by 26
  34. 2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
  35. 2025MAS-ZERO: Designing Multi-Agent Systems with Zero Supervisioncited by 26
  36. 2025Agentic AI Security: Threats, Defenses, Evaluation, and Open Challengescited by 26
  37. 2026MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarkscited by 26
  38. 2025MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoningcited by 24

50 most cited of 281 All 281 (JSON)

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  1. 2025AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration
  2. 2025Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
  3. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
  4. 2025OPTAGENT: Optimizing Multi-Agent LLM Interactions Through Verbal Reinforcement Learning for Enhanced Reasoning
  5. 2026DHCG: Dynamic Construction of Hierarchical Collaboration Graphs for LLM-Based Multi-Agent Reasoning
  6. 2025MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
  7. 2026Token-Efficient Multi-Agent Collaboration via System One-Guided Computational Division of Labor
  8. 2024Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System