让大模型在多智能体中学会识真假，不盲从误导者。
Epistemic Context Learning: Building Trust the Right Way in LLM-Based Multi-Agent Systems
- 基于历史交互构建同伴可信度画像，指导决策
- 4B小模型超越30B大模型，准确识别可靠同伴
- 适用于各类多智能体场景，提升信任建模与答案质量
多智能体系统中的个体代理常因盲从误导性同伴而缺乏鲁棒性。我们发现其根源在于奉承倾向和评估同伴可靠性能力不足。为此，我们首次形式化了历史感知参考的学习问题，将同伴的历史互动作为额外输入，使代理能在不确定时估计同伴可靠性并选择可信同伴学习。这将任务从评估推理质量转为基于历史的可靠性估计。我们提出认知上下文学习（ECL）框架，通过显式构建的历史同伴画像来条件化预测，并利用辅助奖励进行强化学习优化。实验表明，ECL使Qwen 3-4B小模型性能超越8倍于己的基准模型（Qwen 3-30B），并在前沿模型上实现接近100%的准确率。ECL在多种多智能体配置下均表现良好，且信任建模准确率与最终答案质量呈强相关性。
原文摘要 · Abstract (English)
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.
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