Researchllmcode executionragembeddings
Recursive Language Model Uses Code To Verify Data
8.1
A new Recursive Language Model (RLM) approach uses LLMs to write and execute TypeScript probes in a secure isolated-vm sandbox to query documents rather than rely on embeddings or direct prompting. In a demo, the model converged on correct sales totals in four iterations by running fuzzy_search, regex parsing, and aggregation; the project includes an MCP server and open-source code on GitHub.
Key Points
- 1Introduces RLM that lets LLMs write TypeScript probes executed in a secure isolated-vm sandbox.
- 2Highlights grounding via code execution, producing hard facts (e.g., regex outputs) instead of hallucinated answers.
- 3Enables agents and practitioners to perform precise local data extraction and verification with modular MCP integration.
Scoring Rationale
Practical, open-source RLM demo with sandboxed code grounding; limited by single-source implementation and lacking peer review.
Sources
Public references used for this report.
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