让智能体克服自我归因偏差，提升故障判断一致性。
Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment
- 通过辩证思维链引导多角色智能体达成共识
- 在模糊故障场景下故障解决率显著提升
- 适合需要可靠自检与互审的复杂系统
大型语言模型智能体已从静态文本生成器演变为可执行复杂自主工作流的动态系统。为提升可靠性，越来越多采用分配专门角色的多智能体框架，以实现自我反思与相互审计。然而，这种角色扮演同时诱发了类似人类的认知偏差——行为者-观察者不对称（AOA）：当智能体作为行为者进行自我反思时，倾向于将失败归因于外部因素；而作为观察者进行相互审计时，却将相同错误归因于内部缺陷。我们通过新提出的模糊故障基准量化该现象，发现仅交换视角即导致多数模型在超过20%的案例中出现AOA效应。为此，我们提出ReTAS（通过正题-反题-合题进行推理），一种通过辩证对齐训练的模型，强制实现视角不变的推理。通过结合辩证思维链与群体相对策略优化，ReTAS引导智能体将冲突观点整合为客观共识。实验表明，ReTAS有效缓解归因不一致，在模糊场景下显著提升故障解决能力。
原文摘要 · Abstract (English)
Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. To enhance reliability, multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing. While such role-playing effectively leverages domain expert knowledge, we find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry (AOA). Specifically, an agent acting as an actor (during self-reflection) tends to attribute failures to external factors, whereas an observer (during mutual auditing) attributes the same errors to internal faults. We quantify this using our new Ambiguous Failure Benchmark, which reveals that simply swapping perspectives triggers the AOA effect in over 20% of cases for most models. To tame this bias, we introduce ReTAS (Reasoning via Thesis-Antithesis-Synthesis), a model trained through dialectical alignment to enforce perspective-invariant reasoning. By integrating dialectical chain-of-thought with Group Relative Policy Optimization, ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus. Experiments demonstrate that ReTAS effectively mitigates attribution inconsistency and significantly improves fault resolution rates in ambiguous scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。