I'm working on a continuous chain-of-thought reasoning architecture for generative AI models.

It is similar to Meta's COCONUT. However, instead of the training forward and backwards passes of the reasoning tokens being done serially (slow), they are done in parallel (fast). At inference the reasoning tokens are still decoded serially. The trick is that even though training was done in parallel, the reasoning tokens are still causal and you still get the benefits of increased computational circuit depth.

The kicker is that the architecture is modality agnostic (it doesn't rely on language for its chains of thought), and I want to use it to bring COT reasoning to protein and anti-body generation. Basically, I hope for it to be the OpenAI o1 or DeepSeek R1 for domain-specialized scientific AI.