[论文解读] Transitive Expert Error and Routing Problems in Complex AI Systems

Forest Mars|arXiv (Cornell University)|Jan 7, 2026

Embodied and Extended Cognition被引用 0

一句话总结

论文定义了传递性专家错误（TEE），并展示领域专家边界在边界处造成漏洞，导致AI系统的路由和覆盖失败，提出了体系结构性干预措施。

ABSTRACT

Domain expertise enhances judgment within boundaries but creates systematic vulnerabilities specifically at borders. We term this Transitive Expert Error (TEE), distinct from Dunning-Kruger effects, requiring calibrated expertise as precondition. Mechanisms enabling reliable within-domain judgment become liabilities when structural similarity masks causal divergence. Two core mechanisms operate: structural similarity bias causes experts to overweight surface features (shared vocabulary, patterns, formal structure) while missing causal architecture differences; authority persistence maintains confidence across competence boundaries through social reinforcement and metacognitive failures (experts experience no subjective uncertainty as pattern recognition operates smoothly on familiar-seeming inputs.) These mechanism intensify under three conditions: shared vocabulary masking divergent processes, social pressure for immediate judgment, and delayed feedback. These findings extend to AI routing architectures (MoE systems, multi-model orchestration, tool-using agents, RAG systems) exhibiting routing-induced failures (wrong specialist selected) and coverage-induced failures (no appropriate specialist exists). Both produce a hallucination phenotype: confident, coherent, structurally plausible but causally incorrect outputs at domain boundaries. In human systems where mechanisms are cognitive black boxes; AI architectures make them explicit and addressable. We propose interventions: multi-expert activation with disagreement detection (router level), boundary-aware calibration (specialist level), and coverage gap detection (training level). TEE has detectable signatures (routing patterns, confidence-accuracy dissociations, domain-inappropriate content) enabling monitoring and mitigation. What remains intractable in human cognition becomes addressable through architectural design.

研究动机与目标

• 识别领域专业边界如何在AI系统中形成系统性漏洞。
• 表征驱动TEE的机制（结构性相似性偏差和权威持久性）。
• 解释路由架构（MoE、多模态编排、RAG）如何表现出路由与覆盖引起的失败。
• 提出在体系结构和训练层面的干预，以检测并缓解TEE。

提出的方法

• 本文分析结构性相似性如何掩盖领域之间的因果差异，导致对表层特征的过度依赖。
• 它将权威持久性和元认知失败识别为跨能力边界维持过度自信的驱动因素。
• 它将TEE映射到AI路由架构，并解释路由决策如何产生错误的专家或覆盖缺失。
• 提出包括多专家激活与分歧检测、边界感知的校准以及覆盖差距检测的干预措施。
• 它讨论可检测的特征，如路由模式和置信度-准确性分离，用于监控。

实验结果

研究问题

• RQ1 在AI系统中，域边界上有哪些机制支撑传递性专家错误（TEE）？
• RQ2 路由架构如何促成路由引发的和覆盖引发的专家系统失败？
• RQ3 在路由器、专家和训练层面可以采取哪些干预来缓解TEE？
• RQ4 有哪些可检测信号指示TEE，如何进行监控？
• RQ5 通过架构设计能否使AI显式揭示人类的认知“黑箱”，以应对人类的认知盲点？

主要发现

• TEE在结构性相似性掩盖跨域的因果差异、权威持久性维持过度自信时出现。
• 共享的词汇和模式可能导致对表面特征的过度加权，导致对域的错误判断。
• 路由架构可能选择错误的专家，或无法覆盖某一领域，导致边界处产生类似幻觉的输出。
• TEE具有可检测的特征，如路由模式和置信度-准确性分离。
• 通过多专家分歧检测、边界感知校准和覆盖差距检测等架构干预，可以缓解TEE。