Unverified50% confidenceFactExact time
A mathematical proof is either correct or incorrect with almost no gray area, which contrasts with the error tolerance of natural language generation
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50%
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Long-term
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9/21/2026
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UnverifiedMathematical arguments generated by large language models are highly prone to appearing correct while actually being erroneous, and finding the errors requires word-by-word review by professional mathematicians.77% similarVerifiedLarge language models have a miscalibration problem, often answering in a confident tone regardless of whether the answer is correct or incorrect.71% similarUnverifiedThe output of large language models inherently appears plausible (fluency bias), meaning that even when the reasoning chain is wrong, the generated natural language remains grammatically correct and natural in tone, rendering traditional monitoring approaches based on output format or exception capture ineffective.69% similarUnverifiedWriting scoring rules into a prompt in natural language has drawbacks: wording variations across different calls may cause the model's understanding to drift, and the scoring logic cannot be directly parsed or reused by programs.69% similarUnverifiedOOD detection for autoregressive language models is more difficult than for traditional machine learning because the linguistic compositional space is vast, semantic OOD does not correspond to surface-form OOD, and adversaries can construct inputs that appear statistically in-distribution but semantically trigger OOD behavior.68% similar
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