[R] Causal self-attention as a probabilistic model over embeddings

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<p>Most "multimodal" AI products today are really a relay team. One model plans, then hands the job to an image model, a video model, a detector, or a robot policy. Reka's new

<!-- SC_OFF --><div class="md"><p><em>Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory

&#32; submitted by &#32; <a href="https://www.reddit.com/user/141_1337"> /u/141_1337 </a> <br /> <span><a href="https://arxiv.org/abs/2604.03589v1">[link]</a></span> &#32;

arXiv:2603.22300v1 Announce Type: cross Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce

<!-- SC_OFF --><div class="md"><p>I’m not related to this research in any way, but I thought it was worth taking a look at. Uses some ideas from Bayes’ Theorem and Bayesian

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I'm an independent researcher sharing Monodratic, a sparse causal-attention architecture with learned product-hash

arXiv:2604.22167v1 Announce Type: cross Abstract: Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of