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Introduces Blind-Spots-Bench, a benchmark designed to expose persistent failures in modern multimodal AI models on tasks that are trivial for humans. Evaluates a range of models, revealing performance gaps and that no single model dominates across all task types.
This paper studies a deployed LLM-as-judge system for evaluating multi-turn conversational agents and finds it catches far fewer defects than human review, revealing a structured blind-spot taxonomy and routing failures.