[Submitted on 15 Sep 2026]
Title:BioPhys-Bridge: A Benchmark for Interdisciplinary Scientific Reasoning in Physics-Grounded Biological Research
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Abstract:Language models face unique challenges in analyzing interdisciplinary scientific research literature. In biophysics research, faithful answers require grounding observed data in source evidence, interpreting it through a quantitative physics model, and linking it to a biological mechanism. To address this challenge, we introduce BioPhys-Bridge, a novel benchmark dataset for evidence-grounded scientific reasoning over biophysical literature. Each case contains evidence blocks, stable evidence IDs, quantitative values, units, equations, assumptions, mechanisms, and next decisions as grounding targets for question answering (QA) and retrieval-augmented generation (RAG). The initial release contains 500 cases, 1,517 agent-facing tasks, and covers six biological domains and nine physical model families, including three sparse families reserved for future expansion. We enforce strict quality gates for all cases in schema, evidence-integrity, quantitative-grounding, source-license, duplicate, unit-normalization, with domain expert review and annotation for 81 cases. Preliminary evaluations show that DeepSeek-V4-Flash obtain the highest evidence-ID $F_1$ score (0.360), followed by Qwen3.7-Max (0.316) and GPT-4o-mini (0.294). BioPhys-Bridge is an interdisciplinary benchmark for evaluating attribution, faithfulness, hallucination reduction, and biological experiment design with complex, multi-step scientific reasoning. Future works will increase the size and complexity of the dataset and perform comprehensive evaluations. Code and data are available in the GitHub repository and on Hugging Face.
Comments: Empirical Methods in Natural Language Processing 2026, 11 pages, 3 figures
Subjects:
Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
ACM classes: K.3.1; I.2.7; I.2.11
Cite as: arXiv:2609.19180 [cs.AI]
(or arXiv:2609.19180v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.19180
arXiv-issued DOI via DataCite
Submission history
From: Qingyang Xu [view email] [v1] Tue, 15 Sep 2026 12:53:16 UTC (1,202 KB)
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