rnn

Weekly Paper Notes — one of the top picks from the 2026-06-06 CS paper digest. Area: AI / ML. Authors: Akarsh Kumar, Phillip Isola (MIT) arXiv: 2606.06479 · PDF TL;DR This paper proposes Supervised Memory Training (SMT), a way to pretrain nonlinear RNNs without ever doing backpropagation through time (BPTT). The trick: replace recurrent credit assignment with a supervised problem over memory transitions. A Transformer-based “memory encoder” is first trained with a predictive-state objective — it learns a representation m_t that retains exactly the information about the past needed to predict the future....

Weekly Video Notes — a short article distilling one talk from the weekly digest. Source video and key frames are embedded throughout. Pathway staged something unusual: a panel debate, framed as a literal boxing match, on whether the transformer is the final architecture of the AI era — or whether we are already living through the dawn of a post-transformer one. In the blue corner, defending the belt: Łukasz Kaiser, co-author of Attention Is All You Need and one of the minds behind GPT-4 and o-series reasoning models at OpenAI....