RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI

A recursive language model keeps the repository outside the primary prompt and gives the model a programmable environment for inspecting files, dependencies, tests, and configuration. Each pass returns a **bounded observation** rather than the whole codebase.

For monorepo analysis, let the agent write targeted inspection code, preserve its evidence, and make recursive model calls only when the current evidence is insufficient. Capture the **plan, code, observations, subcalls, budget, and final output** for debugging.

RLM is a pattern, not a single implementation, and the talk demonstrates a research playground rather than comparative results on large-codebase tasks. Its claims about proprietary agents using related ideas are partly observational and should not be treated as verified architecture.