Research Publications
Our peer-reviewed publications, working papers, and research findings spanning AI memory systems, neural network architectures, and computational efficiency.
Featured Research
ARMS: A Computational Attention Manifold for Persistent AI Memory
We introduce ARMS (Attention Reasoning Memory Store), a hexagonal architecture for storing and retrieving computed AI attention states in native high-dimensional space. Unlike traditional approaches that project, index, retrieve, and reconstruct states—losing information at each step—ARMS stores states at their actual coordinate positions enabling exact restoration. Inspired by game engine spatial partitioning and the biological hippocampus, ARMS achieves O(log n) retrieval with 100% accuracy through hierarchical container trees. Our validated ARM prototype demonstrates 5,372× compression ratios with perfect reconstruction, proving that attention manifolds are both sparse and cacheable.
Hierarchical Attention Tree: Extending LLM Context Through Structural Memory
We present the Hierarchical Attention Tree (HAT), a novel index structure that extends the effective context of language models by an order of magnitude. A model with 10K native context achieves 100% recall on 60K+ token conversations through hierarchical attention state storage and retrieval, with 3.1ms average latency. Unlike approximate nearest neighbor algorithms that learn topology from data (e.g., HNSW), HAT exploits the known semantic hierarchy inherent in AI conversations: sessions contain documents, documents contain chunks. Our experiments demonstrate 100% recall vs 70% for HNSW on hierarchically-structured data, 70× faster index construction, and that simple centroid-based routing outperforms geometric sophistication.