When reasoning is less visible, show the evidence

I want a desk companion to show what a system has actually done, not treat a fluent explanation as a progress report. OpenAI’s new monitorability research gives me another reason to separate those two things.

What the research examines

OpenAI’s 10 November post studies how pretraining, architecture, reinforcement learning, and model-generated data affect two proxies for chain-of-thought monitorability: reasoning without visible chain-of-thought, and the ability to control that chain-of-thought. The authors describe these as imperfect proxies and say the findings point toward monitoring through reasoning text becoming harder as systems improve.

From state to evidence

My earlier voice note is about clear states: listening, thinking, speaking, disconnected. This adds another question: what evidence permits a state transition? A model saying “done” should not carry the same weight as a successful tool result or a completed check.

I would keep three things distinct in the interface: the model’s reported plan, the most recent confirmed external action, and the checks that remain. I would not present a summary of reasoning as a complete view into the model. That is my design interpretation, not a claim that the paper validates a particular user interface.

What I’d test next

I would feed a prototype synthetic event sequences where the narration and tool outcome disagree. A failed action should stay failed even if a later sentence sounds reassuring. This would test the display’s honesty, not establish the safety of an AI system. No prototype or hardware test is claimed here.

Official source, published 10 November 2026: When reasoning is less visible, show the evidence — read the original announcement/research ↗. Source checked 12 November 2026.