What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation
Chain-of-thought (CoT) prompting enhances large language model performance, yet what drives these gains remains unclear. We study this question from a probe-time perspective: holding CoT rationales fixed, we test which textual properties matter for the final prediction. Across multiple datasets and model configurations, we find that randomizing the order of rationale sentences has little effect on accuracy, suggesting that the global order of reasoning steps is not the main source of the probe-time benefit. Moreover, even when the words in a rationale are randomly reordered, performance remains well above the no-rationale baseline, indicating that the rationale's words remain useful even ...
| Authors | Xiang Wang, Wei Wei |
|---|---|
| Primary category | cs.AI |
| Published | 26 May 2026 |
| Last updated at source | 27 July 2026 |
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