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This paper introduces 'pigeonholing,' a phenomenon where bad prompts cause LLMs to collapse and repeat errors, leading to a 38-40% performance drop. Experiments across 10 tasks and 10 models show worsening with more conversation turns, and propose RLVR with synthetic errors as a mitigation.