Can Reasoning Models Detect Changes to their Chains of Thought?

2026-06-20

AI Summary

This study investigates whether reasoning models can detect human-induced interventions or manipulations in their chain-of-thought (CoT) reasoning—a capability critical for model safety, alignment, and collaborative reliability. We present the first systematic evaluation of mainstream reasoning models across diverse scenarios, including interventions applied during or after reasoning and CoT prefilling using either the model’s own or another model’s reasoning traces. Employing CoT editing, cross-model CoT transfer, and specially designed intervention detection tasks, our empirical analysis reveals that current models exhibit extremely low detection accuracy, struggle to identify both the presence and nature of tampering, and show no significant performance difference between detecting their own versus others’ CoT. These findings underscore a fundamental limitation: contemporary reasoning models lack robust awareness of the integrity of their own reasoning processes.

Abstract

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 identify *how* their CoT was modified; and (iii) models are about as good at detecting changes to their own CoTs as to those of other models.

Problem

• Chain of Thought
• reasoning models
• tampering detection
• model editing
• CoT modification

Innovation

• Chain-of-Thought
• reasoning models
• tampering detection
• model introspection
• AI safety