Retrieval systems
Fast, precise, workload-aware retrieval that does not weaken correctness.
Research
Research for dependable context systems.
The long-term thesis remains focused: intelligent systems need infrastructure that treats freshness, scope, retrieval, and proof as first-class systems problems.
Problems that we care about
We study the systems problems that make AI context trustworthy when source knowledge changes under active agents.
Context infrastructure
Freshness, routing, compression, reuse, and proof for long-context systems without context rot.
Memory architectures
Temporal coherence and pollution control without becoming a generic remember-everything product.
Protocols for intelligence
Interfaces that let models, agents, and stores exchange state reliably.
Published Research articles and notes
Public notes on the technical boundaries behind freshness-aware retrieval, safe reuse, and context proof systems.
Research 001
Hybrid Semantic Cache: cache invalidation for high-dimensional similarity search.
A note on why similarity caches need invalidation semantics, freshness ownership, and traceable reuse boundaries before they can be trusted in production retrieval.
Read the research