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arXiv · 2608.07370

LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering

Xuye Liu·Yimu Wang·Peng Shi·Bo Xue·Xiangrui Ke·Songcheng Cai·Kath Choi·Di Wu·Freda Shi·Krzysztof Czarnecki
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Abstract

Scientific literature is increasingly used as a knowledge source for language models, retrieval-augmented generation systems, and research assistants, but answering research questions from papers requires more than fluent generation. A reliable system must identify the relevant papers, locate the concrete evidence that supports the answer, and produce a response that is faithful to that evidence. We present LitTraceQA, a benchmark for literature-grounded question answering over scientific papers. Given a research question and a metadata pool of papers, a system must return three connected outputs: canonical paper identifiers, supporting evidence locations, and answers in one or more requested formats, including free-form text, multiple-choice answers, and structured tables. LitTraceQA targets evidence types common in scientific reading: tables, figures, text spans, equations or algorithms, and citation contexts. The public development split contains 55 examples, including 26 hidden-source single-paper questions and 29 multi-paper questions, and provides gold papers, evidence annotations, and answers for local validation. We also analyze a larger final annotation collection with 4,978 unique-question records over 4,859 unique gold papers. By evaluating paper retrieval, evidence grounding, and answer accuracy separately, LitTraceQA provides a testbed for scientific QA systems that produce verifiable answers rather than unsupported summaries.

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Xuye Liu, Yimu Wang, Peng Shi, Bo Xue, Xiangrui Ke, Songcheng Cai, Kath Choi, Di Wu, Freda Shi, Krzysztof Czarnecki. 2026-08-15. LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering. https://arxiv.org/abs/2608.07370

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