mlsanityData to 7 Oct 2026

The Effect of Belief Boxes and Open-mindedness on Persuasion

arXiv 2512.06573 ↗First posted 6 Dec 2025cs.AI cs.MAcited by 3

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

As multi-agent systems are increasingly utilized for reasoning and decision-making applications, there is a greater need for LLM-based agents to have something resembling propositional beliefs. One simple method for doing so is to include statements describing beliefs maintained in the prompt space (in what we'll call their belief boxes). But when agents have such statements in belief boxes, how does it actually affect their behaviors and dispositions towards those beliefs? And does it significantly affect agents' ability to be persuasive in multi-agent scenarios? Likewise, if the agents are given instructions to be open-minded, how does that affect their behaviors? We explore these and related questions in a series of experiments. Our findings confirm that instructing agents to be open-minded affects how amenable they are to belief change. We show that incorporating belief statements and their strengths influences an agent's resistance to (and persuasiveness against) opposing viewpoints. Furthermore, it affects the likelihood of belief change, particularly when the agent is outnumbered in a debate by opposing viewpoints, i.e., peer pressure scenarios. The results demonstrate the feasibility and validity of the belief box technique in reasoning and decision-making tasks.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 33 of its 67 references that are in the map. First posted on arXiv 6 Dec 2025.

Builds on

33 of 67 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Enhancing LLM Performance Through Debate: An Empirical Study on Multi-Agent Debate for Coding Tasks15 Mar
  2. 2024GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion21 Sep
  3. 2023Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System8 Dec
  4. 2023Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs29 Nov
  5. 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View3 Oct
  6. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May

Other references · 27

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  3. 2020Measuring Massive Multitask Language Understandingcited by 7,768
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  5. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  6. 2024Qwen2 Technical Reportcited by 2,076
  7. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  8. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  9. 2023ChatDev: Communicative Agents for Software Developmentcited by 893
  10. 2024Phi-4 Technical Reportcited by 675
  11. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 297
  12. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 229
15 more
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  2. 2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 148
  3. 2024Improving Multi-Agent Debate with Sparse Communication Topologycited by 103
  4. 2023Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debatecited by 94
  5. 2023Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debatecited by 92
  6. 2023Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaborationcited by 48
  7. 2025Do as We Do, Not as You Think: the Conformity of Large Language Modelscited by 48
  8. 2024Standards for Belief Representations in LLMscited by 30
  9. 2023Personality testing of Large Language Models: Limited temporal stability, but highlighted prosocialitycited by 16
  10. 2024Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?cited by 8
  11. 2025Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefscited by 8
  12. 2025Giving AI Personalities Leads to More Human-Like Reasoningcited by 3
  13. 2025Enhancing Conversational Agents with Theory of Mind: Aligning Beliefs, Desires, and Intentions for Human-Like Interactioncited by 3
  14. 2022Resolving Open-textured Rules with Templated Interpretive Argumentscited by 1
  15. 2024Advancing Interactive Explainable AI via Belief Change Theorycited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
  2. 2026Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
  3. 2026Belief Cascades Drive Persuasion in LLM Agent Networks
  4. 2026Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action
  5. 2025Can LLM Agents Really Debate? A Controlled Study of Multi-Agent Debate in Logical Reasoning
  6. 2026Persuasion Propagation: Does Persuasion Change AI Agent Behavior?
  7. 2026When Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debate
  8. 2025When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs