Tensor Product Attention Is All You Need

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

<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

<p>The quadratic attention problem has quietly shaped everything you've built with LLMs. RAG pipelines, agentic decomposition, hybrid architectures — these aren't the natural

<!-- SC_OFF --><div class="md"><p>Absolutely unbelievably exciting work, split attention (i.e. a couple of GB) onto local machine and the weights onto another local machine (say a

<!-- SC_OFF --><div class="md"><p>We’ve been working on a probabilistic interpretation of causal self-attention where token embeddings are treated as latent variables. In that

arXiv:2605.15250v1 Announce Type: cross Abstract: Multi-head Latent Attention (MLA), the attention used in DeepSeek-V2/V3, jointly compresses keys and values into a low-rank

&#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;

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1tfpwc6/recent_developments_in_llm_architectures_kv/"> <img alt="Recent Developments in LLM Architectures: