How can we solve long-range recall in linear attention? [D]

<!-- 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

<!-- SC_OFF --><div class="md"><p>How do you guys deal with long context, for example while coding, when you’re going back and forth for adjustments or fixing some errors and

<!-- SC_OFF --><div class="md"><p>Hi all,</p> <p>I’m working on a research project exploring how stateless LLM-based chatbots handle long conversations and whether important

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've casually seen LLMs correctly verify exact quotations that they either couldn't or wouldn't quote directly for me. I'm aware that they're

<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>The news about Ulysses Sequence Parallelism enabling million-token contexts is fascinating for local LLMs. While the potential for deeper context

<!-- SC_OFF --><div class="md"><p>I’ve been digging into ANN-based retrieval (HNSW, IVF, etc.) and something keeps showing up once you plug it into a real RAG pipeline.</p>