Matryoshka

Process documents 100x larger than your LLM's context window—without vector databases or chunking heuristics.

The Problem

LLMs have fixed context windows. Traditional solutions (RAG, chunking) lose information or miss connections across chunks. RLM takes a different approach: the model reasons about your query and outputs symbolic commands that a logic engine executes against the document.

Based on the Recursive Language Models paper.

How It Works

Unlike traditional approaches where an LLM writes arbitrary code, RLM uses Nucleus—a constrained symbolic language based on S-expressions. The LLM outputs Nucleus commands, which are parsed, type-checked, and executed by Lattice, our logic engine.

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   User Query    │────▶│   LLM Reasons   │────▶│ Nucleus Command │
│ "total sales?"  │     │  about intent   │     │  (sum RESULTS)  │
└─────────────────┘     └─────────────────┘     └────────┬────────┘
                                                         │
┌─────────────────┐     ┌─────────────────┐     ┌────────▼────────┐
│  Final Answer   │◀────│ Lattice Engine  │◀────│     Parser      │
│   13,000,000    │     │    Executes     │     │    Validates    │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Why this works better than code generation:

1. Reduced entropy - Nucleus has a rigid grammar with fewer valid outputs than JavaScript
2. Fail-fast validation - Parser rejects malformed commands before execution