PlantMarkerBench: A Multi-Species Benchmark for Evidence-Grounded Plant Marker Reasoning

• URL: http://arxiv.org/abs/2605.10032v2
• Date: Tue, 12 May 2026 03:36:26 GMT
• Title: PlantMarkerBench: A Multi-Species Benchmark for Evidence-Grounded Plant Marker Reasoning
• Abstract summary: PlantMarkerBench is a benchmark for evaluating literature-grounded plant marker evidence interpretation from full-text biological papers.<n>It spans four plant species and contains 5,550 sentence-level evidence instances annotated for marker-evidence validity, evidence type, and support strength.<n>We benchmark diverse open-weight and closed-source language models across species and prompting strategies.
• Score: 10.873172077806798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cell-type-specific marker genes are fundamental to plant biology, yet existing resources primarily rely on curated databases or high-throughput studies without explicitly modeling the supporting evidence found in scientific literature. We introduce PlantMarkerBench, a multi-species benchmark for evaluating literature-grounded plant marker evidence interpretation from full-text biological papers. PlantMarkerBench is constructed using a modular curation pipeline integrating large-scale literature retrieval, hybrid search, species-aware biological grounding, structured evidence extraction, and targeted human review. The benchmark spans four plant species -- Arabidopsis, maize, rice, and tomato -- and contains 5,550 sentence-level evidence instances annotated for marker-evidence validity, evidence type, and support strength. We define two benchmark tasks: determining whether a candidate sentence provides valid marker evidence for a gene-cell-type pair, and classifying the evidence into expression, localization, function, indirect, or negative categories. We benchmark diverse open-weight and closed-source language models across species and prompting strategies. Although frontier models achieve relatively strong performance on direct expression evidence, performance drops substantially on functional, indirect, and weak-support evidence, with evidence-type confusion emerging as a dominant failure mode. Open-weight models additionally exhibit elevated false-positive rates under ambiguous biological contexts. PlantMarkerBench provides a challenging and reproducible evaluation framework for literature-grounded biological evidence attribution and supports future research on trustworthy scientific information extraction and AI-assisted plant biology.

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