Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation
The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repository context. Since real-world repositories often exceed the input length limits of LLMs, existing approaches commonly adopt retrieval-augmented generation (RAG) to provide repository-specific context. Despite improving repository-context retrieval, existing methods typically provide context as task-level support, without explicitly identifying the critical tokens that require fine-grained repository context during generation. During the autoreg
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 61%A fully local, self-hosted repo index for coding agents (Rust, MIT, runs offline) →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Kefeng Duan →
“Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation”
- LinkedLinked via arxiv author · 85%Dewu Zheng →
“Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation”
- LinkedLinked via arxiv author · 85%Yanlin Wang →
“Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation”
- LinkedLinked via arxiv author · 85%Terry Yue Zhuo →
“Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation”