HoH框架让AI编码代理能持续改进软件，在三个基准测试中平均提升52%，还能自主开发完整游戏。
Harness-of-Harness(HoH)框架使基于LLM的编码代理能够在自主开发过程中持续改进软件。该框架在GameCraft-Bench、FrontierSWE和ProgramBench三个基准测试中，使用Codex与GPT-5.5、OpenCode与DeepSeek-V4-Pro、Pi与MiniMax-M3三对模型组合，平均相对提升52.25%，最高提升82.86%。在70多次迭代的多日部署中，HoH自主开发了一款具有连贯故事线、完整核心机制、可玩体验、精美视觉效果和集成音频的第一人称射击游戏。
Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement
This paper studies autonomous software development, in which LLM-based coding agents transform high-level requirements into complete, functional, and usable software systems without human intervention. We introduce Harness-of-Harness (HoH), a framework that enables coding agents to continually improve software during autonomous development. HoH operates on existing coding-agent harnesses, and organizes their executions into iterative planning-coding-testing loops. To sustain improvement across loops, HoH balances repair with capability growth, scopes development into small and verifiable increments, separates implementation-time testing from independent evaluation, and constrains verifiable outputs rather than prescribing agent workflows. It progressively exposes deliverables, role-specific tools, and skills, encourages reuse rather than recreation, and maintains versioned project histories. On GameCraft-Bench, FrontierSWE, and ProgramBench, three harness-model pairs (Codex with GPT-5.5, OpenCode with DeepSeek-V4-Pro, and Pi with MiniMax-M3), HoH consistently outperforms the corresponding standalone harnesses, achieving an average relative gain of 52.25 percent and a maximum gain of 82.86 percent after three iterations. In a multi-day deployment with more than 70 iterations, HoH autonomously develops a first-person-shooter game, featuring a coherent storyline, fully implemented core mechanics, human-playable experience, polished visuals and integrated audio. Github: https://github.com/Flesymeb/HarnessOfHarness Project Page: https://flesymeb.github.io/HarnessOfHarness/