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 ...