I might be missing something here as a non-expert, but isn’t chain-of-thought essentially asking the model to narrate what it’s “thinking,” and then monitoring that narration?
That feels closer to injecting a self-report step than observing internal reasoning.
When we think, our thoughts are composed of both nonverbal cognitive processes (we have access to their outputs, but generally lack introspective awareness of their inner workings), and verbalised thoughts (whether the “voice in your head” or actually spoken as “thinking out loud”).
Of course, there are no doubt significant differences between whatever LLMs are doing and whatever humans are doing when they “think” - but maybe they aren’t quite as dissimilar as many argue? In both cases, there is a mutual/circular relationship between a verbalised process and a nonverbal one (in the LLM case, the inner representations of the model)
That feels closer to injecting a self-report step than observing internal reasoning.
Of course, there are no doubt significant differences between whatever LLMs are doing and whatever humans are doing when they “think” - but maybe they aren’t quite as dissimilar as many argue? In both cases, there is a mutual/circular relationship between a verbalised process and a nonverbal one (in the LLM case, the inner representations of the model)
As far as I understand it, it’s a generated narration conditioned by the prompt, not direct access to internal reasoning.
Source: all of mechinterp
Implement hooks in codex then.
Similar performance with 7% of tokens as chain of thought.
https://arxiv.org/abs/2502.18600