Speaker unverified: belief

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…And do you see the planning and review to always be needed in the future? This is kind of like Andrej Karpathy's idea of like more tokens equal more thinking. But the more you're having it write tokens and think about the outcome and the better result you're probably going to get to, do you think that's always going to be the case? Or that in the future, the model, you can just say, this is the task, and then I'll just return the answer directly and do all of that in the latent space, so to speak? Right. I think in the future, it should hopefully go more as this is a task and I return it. But we need to teach that to the model to train that, which is far from now. Every medium long-term direction that could be relevant here is thinking into latent space. I know some early works are doing that. And that's a way probably to move to first you think, and then you don't have to write all the tokens. Like it's in your head. It doesn't have to be as constricted than a plain text BLM. And once you have done your thoughts, you can just write the final answer or take an action. Just a commentary on that. Anthropic actually cheats at this right now. If you look at the system prompt in Claude Artifacts, I actually have a thinking section that is explicitly removed from the output, which is, I mean, they're still spending the tokens, but before training it, at the prompting level, you can simulate this. And then at iClear, there was the pause token, the backtrack token. I feel like all these are token level stopgap measures. I feel like it's still not the final form. We still need to have, at the architecture level, some kind of variable inference length thing that lets you actually think in latent space, like you're talking about. I don't know if there's any papers that you're thinking about.…

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