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.

翻译：多智能体系统中的个体智能体通常缺乏鲁棒性，容易盲目顺从误导性同伴。我们证明这种弱点源于谄媚倾向以及评估同伴可靠性的能力不足。为解决此问题，我们首先形式化了历史感知参考的学习问题，引入同伴的历史交互作为额外输入，使智能体能够评估同伴可靠性并在不确定时向可信同伴学习。这将任务从评估同伴推理质量转变为基于交互历史估计同伴可靠性。随后我们开发了认知上下文学习：一种将预测建立在基于历史显式构建的同伴档案上的推理框架。我们进一步通过使用辅助奖励的强化学习优化ECL。实验表明，我们的ECL使Qwen 3-4B等小型模型能够通过准确识别可靠同伴，超越其规模8倍的历史不可知基线模型（Qwen 3-30B）。ECL还能将前沿模型提升至接近完美（100%）的性能。我们证明ECL能良好泛化至多种多智能体配置，并发现LLM能有效建模信任关系，揭示信任建模准确度与最终答案质量之间存在强相关性。