Monodratic: learned product-hash routing for sparse causal attention [R]

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1s7p0au/tracer_learntodefer_for_llm_classification_with/"> <img alt="TRACER: Learn-to-Defer for LLM

<!-- SC_OFF --><div class="md"><p>We are investigating whether recurring LLM workloads can be replaced, where appropriate, by automatically constructed pipelines of regexes,

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>Recently, I started working on DNA sequence modeling and decided to explore <strong>linear attention</strong>, mainly because DNA sequences can

<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I’ve been doing some work in the continual learning space and wanted to share an open-source framework we put together called

arXiv:2605.11603v1 Announce Type: new Abstract: The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and

arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely

<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