Latent Update Gate
Instead of always emitting a token, the model can pass its own hidden state forward as the next input through a trained update gate, with a separate head deciding when to stop and actually respond. That's the mechanism, and the loop is trained through, not just run at inference. Being precise about how much of it the released checkpoint actually got: for most of training a configuration bug meant only a single latent step ever reached the loss, so the depth was nominal. Of 82,697 training steps, 2,311 trained at a latent depth above one. That is the honest number, and it is small. What changed in those steps is still worth reporting — deeper thinking went from hurting to helping, and the best measured depth moved from two to four. The question that stayed open is the harder one: whether the latents carry reasoning specific to the question being asked. We re-ran that evaluation against the finished model. The trajectory is far more answer-directed than before, and the answer genuinely depends on it — but it is still not specific to the problem. Swapping in a different question's latent thoughts costs the model almost nothing.
Unscripted capture, not a mockup