SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19. Rather than proposing a new model, we evaluate a fixed scientific RAG pipeline across three corpus scales: 1,034 chunks (1K papers), 5,160 chunks (5K papers), and 15,480 chunks (15K papers). The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation. Across these settings, hybrid retrieval is more robust than either sparse-only or dense-only retrieval in our setting, reaching Recall@10 of 1.000 at 1K and 15K. In contrast, an MS MARCO-trained cross-en...
| Authors | Kaysarul Anas Apurba, Md. Hasibul Hasan, Rofiqul Alam Shehab et al. |
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| Primary category | cs.CL |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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