Can Reasoning Models Detect Changes to their Chains of Thought?
There are many reasons one may want to edit a model's chain of thought (CoT) -- e.g., to prefill it with reasoning from a stronger model or to remove steps that may yield unsafe outputs. The success of these interventions plausibly depends on a model's inability to notice them, as the model may alter its behavior if it suspects tampering. In this work, we study whether recent reasoning models are able to detect such interventions on their CoTs under a variety of conditions: both during reasoning and after it, and when prefilled both with their own CoTs and with those of other models. Broadly, we find that (i) models exhibit only very modest detection accuracy; (ii) models struggle to iden...
| Authors | Sathvik Napa, Utkarsh Singh, Chengyuan Xue et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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