I did not follow all of this, but wasn't there something about, that those reasoning tokens did not represent internal reasoning, but rather a rough approximation that can be rather misleading, what the model actual does?
My assumption is the model no longer actually thinks in tokens, but in internal tensors. This is advantageous because it doesn't have to collapse the decision and can simultaneously propogate many concepts per context position.
Separately, I think Anthropic are probably the least likely of the big 3 to release a model that uses latent-space reasoning, because it's a clear step down in the ability to audit CoT. There has even been some discussion that they accidentally "exposed" the Mythos CoT to RL [0] - I don't see how you would apply a reward function to latent space reasoning tokens.
[0]: https://www.lesswrong.com/posts/K8FxfK9GmJfiAhgcT/anthropic-...
Literally just a citation of Meta's Coconut paper[1].
Notice the 2027 folk's contribution to the prediction is that this will have been implemented by "thousands of Agent-2 automated researchers...making major algorithmic advances".
So, considering that the discussion of latent space reasoning dates back to 2022[2] through CoT unfaithfulness, looped transformers, using diffusion for refining latent space thoughts, etc, etc, all published before ai 2027, it seems like to be "following the timeline of ai-2027" we'd actually need to verify that not only was this happening, but that it was implemented by major algorithmic advances made by thousands of automated researchers, otherwise they don't seem to have made a contribution here.
[1] https://ai-2027.com/#:~:text=Figure%20from%20Hao%20et%20al.%...
> For example, perhaps models will be trained to think in artificial languages that are more efficient than natural language but difficult for humans to interpret.
What are you, Haiku?
But yeah, in many ways we're at least a year ahead on that timeline.
The first 500 or so tokens are raw thinking output, then the summarizer kicks in for longer thinking traces. Sometimes longer thinking traces leak through, or the summarizer model (i.e. Claude Haiku) refuses to summarize them and includes a direct quote of the passage which it won't summarize. Summarizer prompt can be viewed [here](https://xcancel.com/lilyofashwood/status/2027812323910353105...), among other places.
Some future AGI, not LLM based, that learns from it's own experience based on sensory feedback (and has non-symbolic feedback paths) presumably would at least learn some non-symbolic reasoning, however effective that may be.
I see language more as a protocol for inter-agent communication (including human-human communication) but it contains a lot of inefficiencies and historical baggage and is not necessarily the optimal representation of ideas within a brain.