AI写代码终于能自己迭代了：多天无人值守做出可玩游戏
过去让AI写软件，它更像一次性交卷：给个需求，吐一版代码，错了就重来，没有“越写越好”这回事。这篇把“AI写代码的流程”本身变成了可迭代的系统：不是让AI直接写最终版，而是把它拆成计划-编码-测试的小循环，每轮只做一小块、能验证的增量，并且把“自己测自己”和“独立验收”分开，防止AI自欺欺人。跑下来，三套主流AI编码工具在三个基准上平均提升52%，最多提升83%;更夸张的是，让它连续跑70多轮、跨好几天，它真做出了一款能玩的第一人称射击游戏，有完整剧情、核心机制、美术和音效。它不是你明天就能拿来替代程序员的工具——但“AI能连续几天自主迭代一个复杂项目”这件事，已经不再是演示视频，而是有数据、有成品的事实。
📄 原文摘要(英文)
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/