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Chain-of-thought (CoT) traces often serve as a proxy for how Large Language Models (LLMs) arrive at their answers. However, growing evidence shows that models'CoT often fails to reflect their internal computations and can be changed without affecting their final answers. In this work, we measure and improve the alignme...
DerivAudit is introduced, a framework for auditing whether a memory is actually supported by the history available when it was written, whether supporting evidence lies beyond writer-provided citations, whether the composed memory introduces unsupported meaning, and how write-time admission decisions affect later memor...
This work studies whether smaller language models can serve as efficient and reliable rubric-based judges, and compares three ways of extracting criterion-level judgments from small models: Generative verdicts, Yes/No Logprob margins, and Probe judges.
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