AI RESEARCH
On the Challenges and Opportunities of Learned Sparse Retrieval for Code
arXiv CS.CL
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ArXi:2603.22008v1 Announce Type: cross Retrieval over large codebases is a key component of modern LLM-based software engineering systems. Existing approaches predominantly rely on dense embedding models, while learned sparse retrieval (LSR) remains largely unexplored for code. However, applying sparse retrieval to code is challenging due to subword fragmentation, semantic gaps between natural-language queries and code, diversity of programming languages and sub-tasks, and the length of code documents, which can harm sparsity and latency. We