One line of evolution seems to be toward some form of latent-space reasoning, as in [1]. Natural language seems like a relatively low-bandwidth channel for intermediate reasoning.
What if instead of a latent they used at a shorter form of note-taking-like reasoning, using more symbol to achieve a denser CoT. We'd get the best of both worlds. WDYT ?
I think https://arxiv.org/abs/2412.06769 is a good description of the premise of reasoning in latent space, although https://arxiv.org/abs/2604.15726 argues it's already what really happens.
It's as if the only way you could think was by writing down a word, erasing all the thoughts from your head, then reading the word you just wrote down and deciding on the next word, etc.
Reasoning purely in latent space means that the model would still produce an output equivalent to tokens but unconstrained e.g. the output could be raw and opaque vectors. A significant downside is that you lose the ability to inspect the reasoning trace. It would also make the reasoning trace potentially larger which has operational issues.
Not completely true: KV is a projection of the activation at each layer's input, so attention heads see (a representation of) all previous tokens' activations at that layer. The hard decision at the LM head doesn't change that.