Lit3R: Retrieve-Relate-Read for Evidence-Grounded Question Answering over Scientific Literature
We describe tus-nlp's Lit3R (Retrieve-Relate-Read) system for LitTraceQA, a shared task for literature-grounded question answering that requires systems to retrieve relevant papers, identify supporting evidence, and generate answers. Lit3R combines off-the-shelf retrieval, reranking, and large language model (LLM) components without task-specific training. The retriever iteratively combines BM25-based sparse and dense retrieval, cross-encoder reranking, and LLM-based verification, and complements retrieval based on the question with paper-to-paper expansion. The reader first identifies supporting evidence within individual papers and then synthesizes evidence across papers to produce the ...
| Authors | Akira Ise, Kotaro Kumagai, Yuta Yamaguchi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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