ArcQi Tech is dissolving the O(n²) bottleneck at the heart of self-attention. This single constraint defines the cost, length, and locality of every frontier model on Earth.
Self-attention is the engine of the modern transformer. It is also its most expensive part.
Attention scales with the square of the context length. Double the window, quadruple the compute. This is the wall.
ArcQi's research collapses this scaling law. Sub-quadratic attention with preserved expressivity. A structural rewrite of the operation itself.
When the cost of attention bends, everything downstream bends with it. The token economy, the shape of context, the geography of inference.
Million-token windows stop being a premium tier. They become the default unit of thought: entire codebases, legal corpora, patient histories held in working memory, reasoned over end-to-end.
When attention scales gently, inference economics break in the customer's favor. The marginal cost of a long conversation collapses. Agents that loop, reflect, and revise become viable at scale.
Intelligent models, built with long context in mind, reasoning on the workstation, the laptop, the edge device. Private by physics. The hospital, the law firm, the financial institution, the lab. All on their own machines, under their own roof.
The next leap in AI won't come from more compute.
It will come from a better foundation.
Our work is research-first and IP-protected. The summary below is public; selective disclosure is available to partners under NDA.
Technical previews, benchmarks, and the founding thesis. Signal only, no spam.
We're in conversations with a small number of frontier-lab researchers, on-prem AI buyers, infrastructure investors, and journalists. If that's you, write.
partners@arcqitech.ai →