AI Research
& Innovation
Exploring the frontiers of machine learning, neural networks, and artificial intelligence through rigorous research and interactive demonstrations.
Research Domains
Dive into our research across multiple domains, from theoretical foundations to practical applications.
HAT
Hierarchical Attention Tree - A new spatial-hierarchical database model
→Blades
Hot-swappable capability injection through hidden state transfer
→Journals
Research publications on AI memory, model composition, and inference
→Labs
Interactive experiments and research environments
→Thoughts
Essays, insights, and reflections on AI research
→Featured Research
Our latest and most impactful work
HAT: Hierarchical Attention Tree
A novel spatial-hierarchical database achieving 100% recall at 70x faster build times than HNSW.
Blades: Capability Enhancement
Hot-swappable capability injection through hidden state transfer between specialized models.
ARMS: Attention Reasoning Memory Store
A spatial memory architecture that caches attention states at their coordinates.
Position IS Relationship
Traditional databases need explicit relationships. Vector databases lose structure entirely. HAT preserves both structural relationships AND semantic proximity.
- Multi-resolution queries at session, document, or chunk level
- Structural priors as indices - exploit known hierarchy
- Incremental centroid maintenance without full recomputation
- Sleep-inspired consolidation for index maintenance
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