🤖 AI Summary
This work addresses routing errors and coverage gaps in complex AI systems caused by overconfident misjudgments of expert models near domain boundaries. It formalizes the human cognitive phenomenon of boundary misjudgment as “Transitive Expert Error” (TEE) and maps it onto AI architectures. By introducing multi-expert activation with disagreement detection at the routing layer, boundary-aware calibration at the expert layer, and coverage gap detection during training, the approach transforms an otherwise unobservable cognitive black box into a monitorable and intervenable system design problem. The method effectively identifies TEE signals—such as anomalous routing decisions and decoupling between confidence and accuracy—across diverse architectures including Mixture-of-Experts (MoE), multi-model orchestration, tool-augmented agents, and retrieval-augmented generation (RAG), significantly reducing hallucinatory outputs in cross-domain tasks.
📝 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.