TritonSigmoid: A fast, padding-aware sigmoid attention kernel for GPUs [R]

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1ubmybr/i_released_a_softmaxfree_attention_model_at_gpt2/"> <img alt="I released a softmax-free

<!-- SC_OFF --><div class="md"><p>The BDH (Dragon Hatchling) paper (arXiv:2509.26507) describes a Hebbian synaptic plasticity mechanism where model weights update during

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2e0d5/built_an_opensource_llm_router_for_consumer_gpus/"> <img alt="Built an open-source LLM router for

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>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>Every resource says &quot;<strong>We scale by 1/√d_k to prevent softmax saturation</strong>.&quot; Almost none of them explain <em>why saturation

<!-- 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