@inproceedings{ise-etal-2026-lit3r,
title = "{L}it3{R}: Retrieve{--}Relate{--}Read for Evidence-Grounded Question Answering over Scientific Literature",
author = "Ise, Akira and
Kumagai, Kotaro and
Yamaguchi, Yuta and
Ozaki, Hisanori and
Uematsu, Yukio and
Yamada, Ikuya",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.89/",
pages = "1205--1213",
ISBN = "979-8-89176-478-1",
abstract = "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 final answer and evidence trace. On the official test set, our system ranked 4th on the leaderboard. Our code is available at https://github.com/tus-ist-nlp/littraceqa."
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<abstract>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 final answer and evidence trace. On the official test set, our system ranked 4th on the leaderboard. Our code is available at https://github.com/tus-ist-nlp/littraceqa.</abstract>
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%0 Conference Proceedings
%T Lit3R: Retrieve–Relate–Read for Evidence-Grounded Question Answering over Scientific Literature
%A Ise, Akira
%A Kumagai, Kotaro
%A Yamaguchi, Yuta
%A Ozaki, Hisanori
%A Uematsu, Yukio
%A Yamada, Ikuya
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F ise-etal-2026-lit3r
%X 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 final answer and evidence trace. On the official test set, our system ranked 4th on the leaderboard. Our code is available at https://github.com/tus-ist-nlp/littraceqa.
%U https://aclanthology.org/2026.groundlm-1.89/
%P 1205-1213
Markdown (Informal)
[Lit3R: Retrieve–Relate–Read for Evidence-Grounded Question Answering over Scientific Literature](https://aclanthology.org/2026.groundlm-1.89/) (Ise et al., GroundLM 2026)
ACL