Korosuke is a research-backed framework built to tackle the growing need for secure and efficient knowledge retrieval in enterprise and organizational environments. The core problem it solves is enabling semantic search while preserving strict access control over sensitive information.
Built using Python, Korosuke introduces a unique method for creating hierarchical security-aware embeddings. These embeddings are capable of enforcing access policies directly within the vector space, ensuring that users only retrieve information they are authorized to access—even during complex semantic queries.
What sets Korosuke apart is its adaptive re-ranking mechanism, which intelligently adjusts search results based on a novel utility function that balances relevance with information confidentiality. This ensures that sensitive but irrelevant results are filtered out without sacrificing the accuracy or depth of the search.
The framework is especially useful for enterprise knowledge systems, secure document repositories, and regulated industries where access compliance and intelligent retrieval go hand in hand.
Korosuke was awarded a Gold Medal for its innovation and research excellence, and its underlying techniques are grounded in advanced information retrieval, access control theory, and applied machine learning. The framework not only contributes academically but also offers practical tools for building secure, intelligent enterprise search systems.