I am on my own GPUs so thought it be cool to reduce token output. you can charge per latent loop as a token i guess.

I wanted a knob to keep thinking going until it was sure it was done.

you lose CoT monitorability which is a big issue since models have become quite powerful and also often deceptive but i do think that efficiency pressure will keep nudging us toward latent reasoning. looped language models are an active research area. but imagine not being able to monitor mythos' thoughts as it's working through a national security need...

LLMs already think in latent space. The generated reasoning tokens are only the surface of what's happening internally. An LLM may write one thing in the traces but decide differently in the latent space. The whole token-based "reasoning" thing was just a clever hack to extend the existing architecture without completely redoing it. In one of Anthropic's recent papers, they added an additional subnetwork trained to map internal states to readable text, so that's probably the vector of further development.

The goal of this auxiliary module is to have the model recurse pre-emit trained on good thinking traces. This includes a 6-to-1 compression of thinking tokens.

Therefore output tokens are decodeable, but are trained compressed. So they are approximations of faster thinking.

Interpretability is a mixed bag even with trained tools on top of existing models.