Harness-of-Harness framework boosts autonomous coding agents by 52% over multi-day runs
rohanpaul_ai · x · 2026-09-06
An arXiv paper introduces Harness-of-Harness (HoH), a framework that lets LLM coding agents autonomously develop and continually improve software over multi-day runs without human intervention.
- HoH wraps existing coding-agent harnesses and organizes execution into iterative planning-coding-testing loops
- Key design: balancing repair with capability growth, small verifiable increments, separating implementation-time testing from independent evaluation, versioned project histories, and reuse over recreation
- Across GameCraft-Bench, FrontierSWE and ProgramBench, three harness-model pairs (Codex+GPT-5.5, OpenCode+DeepSeek-V4-Pro, Pi+MiniMax-M3) consistently beat standalone harnesses, averaging a 52.25% relative gain and up to 82.86% after three iterations
- In a 70+-iteration multi-day deployment, HoH autonomously developed a complete first-person game
The core argument: long-horizon coding needs persistent project state, independent testing, and replanning from real failures—not just more time.
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