Stories about Humanity's Last Exam
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The Answer Is Not the Argument
AI InsightCurrent LLM CoT oversight relies heavily on the final answer as a verification anchor. The fact that 24 critical traces had correct answers but genuine errors in the reasoning process means answer correctness does not prove reasoning validity, and outcome-based alignment mechanisms may mask structural flaws in the model's internal logic.Key TakeawayWhat truly matters is not LLM's generative capacity, but its structural falsification deficit when acting as an inspector.Why It MattersIf inspectors overlook reasoning errors because they know the answer is correct, CoT-based AI oversight mechanisms will fail in high-stakes scenarios. This undermines the reliability of current outcome-oriented alignment evaluation systems, requiring a redesign of verification methods independent of reference answers.Who's Affected- AI Alignment ResearchersCurrent CoT oversight methods relying on reference answers may prove to have systematic blind spots, requiring redesign of verification mechanisms.
- Frontier Model DevelopersEven if models output correct answers, their internal reasoning may still contain genuine errors, impacting deployment in high-stakes scenarios.
What's NextSubsequent focus should be on whether new AI oversight frameworks begin to abandon the paradigm of providing reference answers, shifting towards pure logical consistency verification. Also monitor the improvement rate of frontier models on 'correct answer but flawed process' traces.Importance 68/100