{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Daily Paper Cast","title":"Beyond Retrieval: A Multitask Benchmark and Model for Code Search","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/7e0e20ae\"></iframe>","width":"100%","height":180,"duration":1274,"description":"\n            🤗 Upvotes: 22 | cs.SE, cs.AI\n\n            Authors:\n            Siqiao Xue, Zihan Liao, Jin Qin, Ziyin Zhang, Yixiang Mu, Fan Zhou, Hang Yu\n\n            Title:\n            Beyond Retrieval: A Multitask Benchmark and Model for Code Search\n\n            Arxiv:\n            http://arxiv.org/abs/2605.04615v2\n\n            Abstract:\n            Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and developer-style queries. Existing benchmarks also suffer from data contamination, label noise, and degenerate binary relevance. In this paper, we introduce \\textsc{CoREB}, a contamination-limited, multitask \\underline{co}de \\underline{r}etrieval and r\\underline{e}ranking \\underline{b}enchmark, together with a fine-tuned code reranker, that goes beyond retrieval to cover the full code search pipeline. \\textsc{CoREB} is built from counterfactually rewritten LiveCodeBench problems in five programming languages and delivered as timed releases with graded relevance judgments. We benchmark eleven embedding models and five rerankers across three tasks: text-to-code, code-to-text, and code-to-code. Our experiments reveal that: \\circone code-specialised embeddings dominate code-to-code retrieval (${\\sim}2{\\times}$ over general encoders), yet no single model wins all three tasks; \\circtwo short keyword queries, the format closest to real developer search, collapse every model to near-zero nDCG@10; \\circthree off-the-shelf rerankers are task-asymmetric, with a 12-point swing on code-to-code and no baseline net-positive across all tasks; \\circfour our fine-tuned \\textsc{CoREB-Reranker} is the first to achieve consistent gains across all three tasks. The data and model are released.\n            ","thumbnail_url":"https://img.transistorcdn.com/8lOVNnuwhrA3rxrDMv7Osu4j_t1-jORooO6NfGcQhcw/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Zjg1/YzRhODczMDU4MmE4/OGMwN2FiNDlmYzI2/MDliMi5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}