Reasoning Models Struggle to Control their Chains of Thought

2026-03-05

AI Summary

This study investigates whether reasoning models can control the content of their Chain-of-Thought (CoT) reasoning to evade CoT-based monitoring while preserving correct outputs. We introduce and formally quantify the notion of “CoT controllability” and develop the CoT-Control evaluation suite, which systematically assesses mainstream models’ ability to generate compliant CoT under constraints such as keyword prohibition, using instruction-based tasks, adversarial prompts, and incentive mechanisms. Experimental results reveal that current models—exemplified by Claude Sonnet 4.5—exhibit extremely low CoT controllability (2.7%), substantially lagging behind their output controllability (61.9%). Moreover, this limitation proves resistant to improvement via prompt engineering or incentives, suggesting that CoT monitoring remains a reliable safeguard. The findings further indicate that CoT controllability is closely tied to model scale and training methodology.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Causality
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if models can control what they verbalize in their CoT, it could undermine CoT monitorability. To measure this undesirable capability -- CoT controllability -- we introduce the CoT-Control evaluation suite, which includes tasks that require models to solve problems while adhering to CoT instructions, e.g., reasoning about a genetics question without using the word'chromosome'. We show that reasoning models possess significantly lower CoT controllability than output controllability; for instance, Claude Sonnet 4.5 can control its CoT only 2.7% of the time but 61.9% when controlling its final output. We also find that CoT controllability is higher for larger models and decreases with more RL training, test-time compute, and increased problem difficulty. CoT controllability failures extend even to situations in which models are given incentives (as opposed to direct requests) to evade CoT monitors, although models exhibit slightly higher controllability when they are told they are being monitored. Similarly, eliciting controllability by adversarially optimizing prompts does not meaningfully increase controllability. Our results leave us cautiously optimistic that CoT controllability is currently unlikely to be a failure mode of CoT monitorability. However, the mechanism behind low controllability is not well understood. Given its importance for maintaining CoT monitorability, we recommend that frontier labs track CoT controllability in future models.

Problem

• Chain-of-Thought
• controllability
• reasoning models
• monitorability
• AI safety

Innovation

• Chain-of-Thought Controllability
• CoT Monitoring
• Reasoning Models
• AI Safety
• CoT-Control Evaluation