This seems way cooler than just computation (which is easy to hand off to a tool, and arguably more predictable that way). The broader point here is that you can have your model switch dynamically to/from a kind of attention that scales with the log of the token count, by only exploring the convex hull in a 2D space. A less capable version of attention, to be sure, but one capable of tracing a program’s execution with text representations of registers and stack - which is a meaningful level of flexibility, and one many humans would find difficult to do reliably!

What could you do with an LLM that can go into “focus mode” and generate tokens extremely rapidly? How much more powerful would a reasoning-token-generation phase be that can explore and cull large numbers of paths/hypotheses, so long as they are well defined? Does this have implications for multi-modal models and spatial reasoning?

As the paper suggests:

> These models could be useful in several modes: as a dedicated fast path paired with a slower, more general model; as part of a fast/slow hybrid architecture inside a single system; or as a speculative execution model that proposes tokens quickly while a regular-attention model verifies and accepts them. Regardless of their eventual capability ceiling, they already suggest a powerful systems primitive for speeding up larger models.