mlsanityData to 7 Oct 2026

Epistemic Context Learning: Building Trust the Right Way in LLM-Based Multi-Agent Systems

arXiv 2601.21742 ↗First posted 29 Jan 2026cs.AI cs.CL cs.MAcited by 6

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

Abstract (arXiv)

Individual agents in multi-agent (MA) systems often lack robustness, tending to blindly conform to misleading peers. We show this weakness stems from both sycophancy and inadequate ability to evaluate peer reliability. To address this, we first formalize the learning problem of history-aware reference, introducing the historical interactions of peers as additional input, so that agents can estimate peer reliability and learn from trustworthy peers when uncertain. This shifts the task from evaluating peer reasoning quality to estimating peer reliability based on interaction history. We then develop Epistemic Context Learning (ECL): a reasoning framework that conditions predictions on explicitly-built peer profiles from history. We further optimize ECL by reinforcement learning using auxiliary rewards. Our experiments reveal that our ECL enables small models like Qwen 3-4B to outperform a history-agnostic baseline 8x its size (Qwen 3-30B) by accurately identifying reliable peers. ECL also boosts frontier models to near-perfect (100%) performance. We show that ECL generalizes well to various MA configurations and we find that trust is modeled well by LLMs, revealing a strong correlation in trust modeling accuracy and final answer quality.

Key facts. Cited by 6 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 37 of its 51 references that are in the map. First posted on arXiv 29 Jan 2026.

Builds on

37 of 51 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Chain of Agents: Large Language Models Collaborating on Long-Context Tasks4 Jun
  2. 2024TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision10 Mar
  3. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug

Other references · 34

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
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  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
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  5. 2023Let's Verify Step by Stepcited by 3,598
  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  7. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  8. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,678
  9. 2020Learning to summarize from human feedbackcited by 2,562
  10. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  11. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  12. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
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  1. 2025Gemma 3 Technical Reportcited by 1,686
  2. 2025Understanding R1-Zero-Like Training: A Critical Perspectivecited by 1,276
  3. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  4. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  5. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  6. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  7. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  8. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
  9. 2025MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agentscited by 226
  10. 2026MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memorycited by 113
  11. 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 109
  12. 2023ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluationcited by 85
  13. 2023Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMscited by 82
  14. 2025MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learningcited by 80
  15. 2025On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Modelscited by 64
  16. 2024MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debatecited by 54
  17. 2025MARFT: Multi-Agent Reinforcement Fine-Tuningcited by 51
  18. 2025Do as We Do, Not as You Think: the Conformity of Large Language Modelscited by 48
  19. 2025The Majority is not always right: RL training for solution aggregationcited by 46
  20. 2024Conformity in Large Language Modelscited by 30
  21. 2025Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systemscited by 15
  22. 2025Atomic Skills are the Prerequisite: When Reinforcement Learning Synthesizes Compositional Reasoning, and When It Only Amplifiescited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systemscited by 7
  2. 2026Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systemscited by 5
  3. 2026Sigma-Mem: An Online Reliability Memory for LLM-based Multi-Agent Systemscited by 1
  4. 2026Group Perspective Matters: Regulating Debate Relationships Can Mitigate Blind Conformity in Multi-Agent Debatecited by 0
  5. 2026X-Tree: Tokenizing Reusable Experience for Efficient Agent Generalizationcited by 0
  6. 2026BaRe-Mem: Bayesian Reliability Memory for Robust and Adaptive Agent Consultationcited by 0

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