The End of the Quadratic Era In the current AI landscape, we are obsessed with "longer context." But as we scale to millions of tokens, we hit the Quadratic Wall: the compute cost of dense attention grows at $O(N^2)$.
At Nare Labs, we believe the future isn't about bigger context windows, but smarter memory management.
Introducing DSM (Dynamic Segmented Memory) DSM is not just "another RAG." It is a structural evolution of how a model interacts with knowledge. We organize information into a Triplet State:
Segments (S): Atomic knowledge units. Hierarchy (T): A dynamic category tree for $O(\log N)$ routing. Graph (G): A semantic associative layer. Why it Matters Extreme Efficiency: During our stress tests, DSM achieved a 401,075x reduction in attention operations compared to dense processing. SLM Empowerment: We proved that a 1.5B parameter model (Qwen-2.5) can navigate complex repositories like SimpleUI and solve cross-file bugs better than models 10x its size. Associative Reasoning: Unlike standard retrieval, DSM "understands" the links between your code files, dependencies, and documentation. The Benchmark In our Mini-SWE Benchmark, DSM allowed a small model to trace a deep-seated bug across multiple configuration files in under 430ms. This is the speed of thought.
Open Source Release We are committed to pushing the boundaries of AI efficiency. Today, we are releasing the DSM Engine core to the public.
"Intelligence is not about how much you can hold in your head, but how quickly you can find what matters."
Explore the code on our GitHub.
🛰️🚀 Nare Labs: Engineering the Cognitive Future.