New preprint: Thinking with Looped Flows

In Thinking with Looped Flows, led by Ayhan Suleymanzade, we propose looped flows: recurrent models trained with local denoising objectives at decreasing noise levels, so that their hidden states carry useful computation forward even when gradients only cover a few updates. Inference then becomes integrating a probability flow coupled with these recurrent states.