Author

Durgesh Kalwar

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#artificial intelligence Preprint Sep 2026

Correct Answers, Invalid Traces: What Verifiable Grade-School Math Reveals About Chain-of-Thought Traces

Chain-of-thought traces are widely read as records of how models reach their answers, informing debugging, agent auditing, and claims about reasoning. Testing this interpretation is difficult because natural-language thinking traces are rarely mechanically verifiable. We revisit it in iGSM, a synthetic grade-school mat...

Ratish Puduppully, Pranabendu Misra, Paarth Iyer et al. · 0 citations

#artificial intelligence Jun 2026

Masked Distillation: Internalizing the Chain-of-Thought in Language Models

This work introduces \textit{masked distillation}, a knowledge-distillation framework in which a student LLM is trained to predict only the solution tokens conditioned on the question, while a reasoning teacher provides feedback on the student's responses after conditioning on the question and its own CoT trace.

Durgesh Kalwar, Vardhan Palod, Subbarao Kambhampati · arXiv.org · 0 citations