“CoT controllability evals seem very under-elicited” by Arun Jose

Subtitle: Simple prompt optimizations can improve model capability to control their reasoning.

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. 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.

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 ~2-3 times 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).

This [...]

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Outline:

(03:09) Setup

(06:40) Results

(06:43) Aggregate compliance

(07:20) Generalization to held-out controllability tasks

(09:17) Scaling patterns for few-shot prompts

(10:20) Comparison with fine-tuning

(11:02) Appendix A: Accuracy and reasoning length by setting

(12:50) Appendix B: Per-mode results

(13:25) Appendix C: What the zero-shot prompts look like

(15:56) Appendix D: Comparison with GEPA prompt optimization

The original text contained 16 footnotes which were omitted from this narration.

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First published:
September 11th, 2026

Source:
https://blog.redwoodresearch.org/p/cot-controllability-evals-seem-very

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Narrated by TYPE III AUDIO.

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