Cornell Paper: Stealing LLM Reasoning Capabilities Without Chain-of-Thought Traces
burny_tech · x · 2026-08-11
A paper from Cornell researchers demonstrates that hiding chain-of-thought (CoT) traces does not prevent the theft of a large language model's reasoning capabilities.
The authors introduce Trace Inversion, a method that reconstructs detailed, synthetic reasoning traces using only a black-box model's inputs, final answers, and optional summaries. The study shows that fine-tuning student models on these inverted traces significantly improves their reasoning, enabling effective distillation from proprietary LLMs. This proves that concealing CoT is insufficient to protect underlying reasoning logic.
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