The 1/√d_k scaling in attention isn't Numerical Stability: Here's the actual mat

<!-- SC_OFF --><div class="md"><p>I have analyzed some decoder transformer models using Lyapunov spectral analysis and found that the ratio of the MLP and attention spectral norms

<!-- SC_OFF --><div class="md"><p>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s5cggh/google_turboquant_blew_up_for_kv_cache_heres/"> <img alt="Google TurboQuant blew up for KV cache.

<!-- SC_OFF --><div class="md"><p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2604.21395v2"><strong>https://arxiv.org/abs/2604.21395v2</strong></a></p> <p>For years,

<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>We are open-sourcing TritonSigmoid — a fast, padding-aware sigmoid attention kernel for GPUs.</p> <p>We built this for single-cell foundation

<!-- 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

<!-- 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