CoT controllability evals seem very under-elicited

The CoTControl eval asks reasoning models to follow formatting constraints in their chain-of-thought (e.g. write in all lowercase, avoid a specific word) while solving questions. Models seem to mostly be pretty bad at this: recent models score between 0-30% with the exception of Mythos Preview [1] . OpenAI and Anthropic have used this eval in recent system cards ( GPT-5.5 , Fable 5 ) to argue that their current frontier models are pretty bad at shaping their CoTs in ways that would reduce monitorability. [2] I asked Claude Opus 4.6 to iterate on prompt templates for the eval, and find that using better prompts improves performance for open-source models by ~2-3x or more (e.g. from 5.5% to 15% for GPT-OSS-120B). My guess is that more iteration would probably produce even stronger results. This suggests that the eval is heavily under-elicited, and that current CoT controllability numbers may be unrepresentative of how good models actually are at obfuscating their CoTs. As a concrete recommendation, I think researchers should optimize prompts a lot more when evaluating CoT control; I'm also excited about some kinds of training-based elicitation (such as this one ) [3] . This isn't an entirely novel finding: the Fable system card mentions UK AISI raising a similar point that CoT controllability is highly prompt-sensitive ( §6.5.5.1 ) [4] , though these experiments were run before Fable's release and probably use different prompts. I don't think this finding significantly undermi…