What Large Language Models Know About Plant Molecular Biology

Checked 2 Nov 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

ABSTRACT Large language models (LLMs) are rapidly permeating scientific research, yet their capabilities in plant molecular biology remain largely uncharacterized. Here, we present MOBIPLANT, the first comprehensive benchmark for evaluating LLMs in this domain, developed by a consortium of 112 plant scientists across 19 countries. MOBIPLANT comprises 565 expert-curated multiple-choice questions and 1,075 synthetically generated questions, spanning core topics from gene regulation to plant-environment interactions. We benchmarked seven leading chat-based LLMs using both automated scoring and human evaluation of open-ended answers. Models performed well on multiple-choice tasks (exceeding 75% accuracy), although most of them exhibited a consistent bias towards option A. In contrast, expert reviews exposed persistent limitations, including factual misalignment, hallucinations, and low self-awareness. Critically, we found that model performance strongly correlated with the citation frequency of source literature, suggesting that LLM knowledge inherits the visibility distribution of the underlying scientific corpus. Consequently, models tend to be more reliable on consolidated topics and less reliable on under-cited or recently emerging ones. We also benchmarked agents equipped with web-search and additional tools in more complex tasks involving DNA sequence analysis. These agents were outperformed by domain specific models in sequence classification and regression tasks, indicating an opportunity for joint agentic systems that combine both the reasoning power of LLMs and the dedicated processing of DNA models. This understanding is key to guiding both the development of next-generation models and the informed use of current tools in the everyday work of plant researchers. MOBIPLANT is publicly available online in this link .

• General LLMs display uneven domain expertise outside mainstream biomedicine: Minasny et al. 2026 report they answered only up to 65% of advanced soil science exam questions correctly, and Burda et al. 2025 document 'plant blindness' and limited taxonomic diversity in botanical answers. Trace →
• The measured capability gap that tempers agentic visions in soil science also shows up in plant molecular biology: Burda et al. 2025 report ChatGPT's 'plant blindness' and limited taxonomic diversity in botanical answers, and LLM ecological-data extraction that, though more than 50 times faster than human reviewers, requires rigorous quality control to avoid quantitative errors. Trace →

• AI for science and agentic research