reasoning-failure

This paper identifies a failure mode in LLMs where they do not verify the validity of numerical statistics when synthesizing multiple sources, instead relying on the stylistic markers of analytical rigor. The authors term this 'epistemic alignment' and show that it persists across models and domains, resisting prompting-based mitigations.

This paper identifies a failure mode in masked diffusion language models where confidence-based decoding leads to high-confidence errors on complex reasoning tasks, and shows that confidence-aligned training exacerbates this issue while random masking preserves reasoning performance.