AI RESEARCH
In Line with Context: Repository-Level Code Generation via Context Inlining
arXiv CS.AI
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ArXi:2601.00376v2 Announce Type: replace-cross Repository-level code generation has attracted growing attention in recent years. Unlike function-level code generation, it requires the model to understand the entire repository, reasoning over complex dependencies across functions, classes, and modules. However, existing approaches such as retrieval-augmented generation (RAG) or context-based function selection often fall short: they primarily rely on surface-level similarity and struggle to capture the rich dependencies that govern repository-level semantics. In this paper, we