linear attention
linear attention on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on linear attention in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around linear attention, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
How can we solve long-range recall in linear attention? [D]
Addressing long-range recall in linear attention presents a significant challenge, particularly when modeling extensive DNA sequences—easily exceeding one million tokens. Initial explorations reveal that performance on needle-in-a-haystack benchmarks degrades substantially as context length increases, with even established models like HyenaDNA exhibiting recall rates near random chance. This suggests a fundamental limitation within the compressed-state representation inherent to linear attention. Discovering architectural approaches that maintain reliable retrieval without resorting to computationally expensive softmax or large external memory is key.
Continued development of the model based on the SSN [D]
After a six-month hiatus, development continues on Project NORD, now evolving into NORD 5.5 — Flash, a CPU-first spiking language model. This iteration prioritizes architectural simplification, moving away from artificial time dimensions and focusing on causal processing with innovations like sparse MoE and persistent memory banks. The core objective is to optimize for CPU inference, a significant shift from previous iterations. Initial benchmarking will compare NORD 5.5 against earlier versions, evaluating performance metrics like perplexity and RAM usage.