Matryoshka empowers large language models (LLMs) to analyze documents vastly exceeding their context windows without traditional RAG or chunking methods. It employs a Recursive Language Model (RLM) approach where the LLM reasons about a query and outputs constrained symbolic commands in the Nucleus DSL. These commands are then executed safely by the Lattice logic engine, which features a robust parser, type inference, and a miniKanren-based solver. A key innovation is its handle-based, in-memory SQLite storage, providing over 97% token savings by presenting LLMs with compact references instead of full data, making it exceptionally efficient for complex and voluminous analysis tasks.
