Open reproduction of NVIDIA's AVO paper (arXiv:2603.24517): evolutionary search where an autonomous coding agent IS the variation operator — Vary(P)=Agent(P,K,f). Runs on the Claude Code or Codex sess
An open reproduction of AVO: Agentic Variation Operators for Autonomous Evolutionary Search (Chen, Ye, Xu et al., NVIDIA, 2026), runnable on a laptop.
Classical evolutionary search, and the LLM-augmented systems that followed it, decompose the variation operator into a fixed pipeline:
The framework samples parents; the model produces one candidate from them. AVO replaces that whole decomposition with a single autonomous agent run:
The agent sees the full lineage Pt, a domain knowledge base K, and the scoring function f — and decides for itself what to read, what to change, and when to measure. It stops being a candidate generator and becomes the variation operator.
This repo implements that framework, plus the surrounding machinery the paper describes: a git-backed lineage, a correctness-gated score vector, the matches-or-improves commit policy, a supervisor that intervenes on stagnation, and trajectory plots. Two optimisation targets ship with it.
The part that matters: it runs on the session you already have
The default driver does not spawn an agent and does not call an API. It hands the variation prompt to the Claude Code session you are already talking to, and that session does the work. Nothing extra is billed, no ANTHROPICAPIKEY is needed, and the agent doing the optimising is a real general-purpose coding agent — which is exactly what the paper used.
Unattended mode (spawn an agent per step and let it run for days, like the paper's 7-day experiment) is available too, and is opt-in precisely because it spends quota.
From the project README.
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