Reasoning Models Show Promise in Controlling Their 'Chain of Thought'

research #llm 🔬 Research|Analyzed: Mar 9, 2026 04:02•

Published: Mar 9, 2026 04:00

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1 min read

Analysis

This research explores a fascinating new dimension of how we can understand and control the behavior of Large Language Models (LLMs). The development of the CoT-Control evaluation suite is a major step forward, enabling us to test and improve the trustworthiness of reasoning models.

Key Takeaways

•The CoT-Control evaluation suite is a new method for testing and improving the control of reasoning in LLMs.
•Models currently struggle to control their 'Chain of Thought' to the same degree they can control their outputs.
•The research suggests that current models are not easily tricked into misleading 'Chain of Thought' responses, which is promising for monitorability.

Reference / Citation

View Original

"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."

ArXiv AIMar 9, 2026 04:00

* Cited for critical analysis under Article 32.

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