Thinking is implemented as regular autoregressive generations by everyone, meaning its just regular tokens, but they appear between <thinking></thinking> special tokens which are then programmatically removed from what the user can actually see.

Idea somewhat similar to what you describe exist but they make steering/post-training/interpretation much harder.

All open model that have reasoning seem to be doing it in text tokens. Is there any indication that closed models are approaching this somehow fundamentally differently?

Claude does all its thinking in text, its ChatGPT which does not do its reasoning in text. I believe its sort of implied / understood (?) that this is part of Claude's secret sauce over OpenAI. OpenAI will use less tokens, but Claude will be more correct, more of the time.

I saw that idea described as a step in AI 2027 (they call it "neuralese" and eyeballing the site, it's still labeled a hypothetical/future development), but AFAIK no one implemented/deployed this yet.

EDIT:

They link to a Meta paper from 2024/2025 though: https://arxiv.org/pdf/2412.06769/.

That would be a huge deal, meaning we've lost even our shitty, ineffective ways of monitoring agent reasoning stream. Big setback when it comes to alignment and interpretability.

I don't know about Claude, but latest GPT versions still have a readable reasoning stream. It sometimes leaks out when the model gets confused, e.g., during a tool call. If you're curious, looks simplified; less words; extremely compact. They optimize tokens. But remain readable.