mlsanityData to 7 Oct 2026

Multi-Agent Consensus Seeking via Large Language Models

arXiv 2310.20151 ↗First posted 31 Oct 2023cs.CL cs.RO cs.SYcited by 50

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

Abstract (arXiv)

Multi-agent systems driven by large language models (LLMs) have shown promising abilities for solving complex tasks in a collaborative manner. This work considers a fundamental problem in multi-agent collaboration: consensus seeking. When multiple agents work together, we are interested in how they can reach a consensus through inter-agent negotiation. To that end, this work studies a consensus-seeking task where the state of each agent is a numerical value and they negotiate with each other to reach a consensus value. It is revealed that when not explicitly directed on which strategy should be adopted, the LLM-driven agents primarily use the average strategy for consensus seeking although they may occasionally use some other strategies. Moreover, this work analyzes the impact of the agent number, agent personality, and network topology on the negotiation process. The findings reported in this work can potentially lay the foundations for understanding the behaviors of LLM-driven multi-agent systems for solving more complex tasks. Furthermore, LLM-driven consensus seeking is applied to a multi-robot aggregation task. This application demonstrates the potential of LLM-driven agents to achieve zero-shot autonomous planning for multi-robot collaboration tasks. Project website: windylab.github.io/ConsensusLLM/.

Key facts. Cited by 50 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 23 of its 37 references that are in the map. First posted on arXiv 31 Oct 2023.

Builds on

23 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug
  3. 2023Building Cooperative Embodied Agents Modularly with Large Language Models5 Jul

Other references · 20

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  7. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  8. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  9. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  10. 2020Towards a Human-like Open-Domain Chatbotcited by 467
  11. 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 361
  12. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 297
8 more
  1. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
  2. 2023Self-collaboration Code Generation via ChatGPTcited by 255
  3. 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 238
  4. 2023Playing repeated games with Large Language Modelscited by 213
  5. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 202
  6. 2023RoCo: Dialectic Multi-Robot Collaboration with Large Language Modelscited by 171
  7. 2023Humanoid Agents: Platform for Simulating Human-like Generative Agentscited by 73
  8. 2019Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanationscited by 71

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 12; 2025: 26; 2026: 10.

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  3. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  4. 2024LLM-based Multi-Agent Reinforcement Learning: Current and Future Directionscited by 67
  5. 2024A Scalable Communication Protocol for Networks of Large Language Modelscited by 43
  6. 2025Multi-agent Embodied AI: Advances and Future Directionscited by 36
  7. 2024Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agentscited by 35
  8. 2025AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMscited by 31
  9. 2023Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate Systemcited by 26
  10. 2024MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulationcited by 18
  11. 2025SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoningcited by 18
  12. 2024An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-Makingcited by 14
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  4. 2026AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agentcited by 12
  5. 2025The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systemscited by 11
  6. 2025Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agentscited by 11
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  8. 2025HiMA-Ecom: Enabling Joint Training of Hierarchical Multi-Agent E-commerce Assistantscited by 9
  9. 2025Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothingcited by 9
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  11. 2024Challenges Faced by Large Language Models in Solving Multi-Agent Flockingcited by 7
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  13. 2025Position: Towards a Responsible LLM-empowered Multi-Agent Systemscited by 7
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  15. 2025Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignmentcited by 7
  16. 2026Can AI Agents Agree?cited by 7
  17. 2025AI Agent Behavioral Sciencecited by 5
  18. 2024Apollonion: Profile-centric Dialog Agentcited by 4
  19. 2025MAPLE: Multi-Agent Adaptive Planning with Long-Term Memory for Table Reasoningcited by 4
  20. 2025Fetch.ai: An Architecture for Modern Multi-Agent Systemscited by 4
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  22. 2026Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placementcited by 4
  23. 2025Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferencescited by 3
  24. 2025Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Codingcited by 3
  25. 2025Negotiating Comfort: Simulating Personality-Driven LLM Agents in Shared Residential Social Networkscited by 2
  26. 2024GAI: Generative Agents for Innovationcited by 1
  27. 2024Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devicescited by 1
  28. 2025Second Opinion Matters: Towards Adaptive Clinical AI via the Consensus of Expert Model Ensemblecited by 1
  29. 2025Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarizationcited by 1
  30. 2025COSMIR: Chain Orchestrated Structured Memory for Iterative Reasoning over Long Contextcited by 1
  31. 2025Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Taskscited by 1
  32. 2026Learning to Seek Help: Dynamic Collaboration Between Small and Large Language Modelscited by 1
  33. 2026Insider Attacks in Multi-Agent LLM Consensus Systemscited by 1
  34. 2026What Do Agents Communicate? Characterizing Information Exchange in Multi-Agent Systemscited by 1
  35. 2026When Models Disagree: Rethinking LLM Evaluation for Public Comment Analysiscited by 0
  36. 2026HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planningcited by 0
  37. 2026A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturingcited by 0
  38. 2026Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMscited by 0

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