There has been a lot of debate about what’s the right way to preserve monitorability of frontier AI and whether policies should target inputs (like model architecture) or outputs (like results on monitorability evals).
I think they should target both. With latent architectures, models would no longer need CoT, and it will be harder to make a case that the model is sufficiently monitorable and easy to study given the current state of science.
We've written a post arguing that latent reasoning architectures (aka 'neuralese') would substantially increase misalignment risk via making oversight much harder.
In the extreme, we could see massive 'neuralese hivemind swarms' where the agents think and communicate in latents, likely making oversight nearly entirely reliant on observing the actions these agents take. (And these agents would have huge amounts of time to reason about obfuscating their actions if they wanted to do so...) Individual agents doing extensive latent reasoning would also be concerning; in the post we discuss how above some threshold of latent reasoning, agents may be able to perform difficult-to-detect and reliable steganography for communication and further reasoning.
We argue both that latent reasoning architectures would make chain-of-thought no longer very useful for oversight (by eliminating or greatly reducing the need for verbalized reasoning) and that, without these architectures, it's likely the value of chain-of-thought for oversight could be preserved.