Repo0通过GPT-5 mini和DeepSeek V3.2实现了更高的功能覆盖率和通过率，是设计驱动零到全代码生成的创新框架。与RPG相比，性能提升显著。
大型语言模型在代码生成方面取得显著进展，但大多数现有系统假设预定义的仓库架构。Repo0是一个针对零到全代码生成的持续结构演化框架，通过GPT-5 mini和DeepSeek V3.2在RepoCraft的六个真实仓库上评估，Repo0在所有设置中实现了最高的功能覆盖率和通过率，与RPG相比，功能覆盖率提高20.08个百分点，通过率提高29.74个百分点。
Repo0: Design-Driven Zero-to-All Code Generation
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.