AI 写代码终于能自己搭出完整项目架构了
以前让 AI 写一个完整软件项目，它要么只生成零散文件，要么需要你提前把文件夹结构、模块划分都定好。Repo0 反过来：你只给一句话需求，它自己从零长出整个项目架构——先画一张「需求-组件」双图，然后像生物进化一样反复调整模块边界，直到结构稳定了再开始写代码。在 6 个真实项目上，它的功能覆盖率和测试通过率都比现有最强方法高出 20-30 个百分点。这不是你明天能直接用的工具，但它指向一个方向：AI 写代码正在从「拼积木」走向「自己设计图纸再施工」。
📄 原文摘要(英文)
Large language model agents have made substantial progress in code generation, yet most existing systems assume a predefined repository architecture. This assumption does not hold in zero-to-all code generation, where an agent must construct an entire software project directly from natural-language requirements while maintaining a modular repository architecture throughout development. We present Repo0, a continuous structural evolution framework for zero-to-all code generation. Repo0 maintains an explicit architectural state instantiated as a Dual-Directed-Acyclic-Graph (Dual-DAG), consisting of a requirement-level DAG, a component-level DAG, and their alignment relation. Starting from natural-language requirements, it iteratively evolves component boundaries through structural actions guided by modularity metrics until structural convergence, after which the converged architecture guides test-driven development code generation. We evaluate Repo0 on six real-world repositories from RepoCraft using GPT-5 mini and DeepSeek V3.2. Repo0 achieves the highest Functionality Coverage and Pass Rate across all settings. Compared with RPG, the strongest repository-planning baseline, Repo0 improves Functionality Coverage by up to 20.08 percentage points and Pass Rate by up to 29.74 percentage points. Ablation and structural-evolution analyses further demonstrate the importance of the Dual-DAG architectural state, modularity-guided structural evolution, and explicit structural convergence.