When Chain-of-Thought Fails, the Solution Hides in the Hidden States
Whether intermediate reasoning is computationally useful or merely explanatory depends on whether chain-of-thought (CoT) tokens contain task-relevant information. We present a mechanistic causal analysis of CoT on GSM8K using activation patching: transferring token-level hidden states from a CoT generation to a direct-answer run for the same question, then measuring the effect on final-answer accuracy. Across models, generating after patching yields substantially higher accuracy than both direct-answer prompting and the original CoT trace, revealing that individual CoT tokens can encode sufficient information to recover the correct answer, even when the original trace is incorrect. This t...
| Authors | Houman Mehrafarin, Amit Parekh, Ioannis Konstas |
|---|---|
| Primary category | cs.CL |
| Published | 25 April 2026 |
| Last updated at source | 2 September 2026 |
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