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Main Authors: Jin, Daoyuan, Gunner, Nick, Janke, Niko Carvajal, Baruah, Shivranjani, Gold, Kaitlin M., Jiang, Yu
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2508.19383
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author Jin, Daoyuan
Gunner, Nick
Janke, Niko Carvajal
Baruah, Shivranjani
Gold, Kaitlin M.
Jiang, Yu
author_facet Jin, Daoyuan
Gunner, Nick
Janke, Niko Carvajal
Baruah, Shivranjani
Gold, Kaitlin M.
Jiang, Yu
contents Modern plant science increasingly relies on large, heterogeneous datasets, but challenges in experimental design, data preprocessing, and reproducibility hinder research throughput. Here we introduce Aleks, an AI-powered multi-agent system that integrates domain knowledge, data analysis, and machine learning within a structured framework to autonomously conduct data-driven scientific discovery. Once provided with a research question and dataset, Aleks iteratively formulated problems, explored alternative modeling strategies, and refined solutions across multiple cycles without human intervention. In a case study on grapevine red blotch disease, Aleks progressively identified biologically meaningful features and converged on interpretable models with robust performance. Ablation studies underscored the importance of domain knowledge and memory for coherent outcomes. This exploratory work highlights the promise of agentic AI as an autonomous collaborator for accelerating scientific discovery in plant sciences.
format Preprint
id arxiv_https___arxiv_org_abs_2508_19383
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Science
Jin, Daoyuan
Gunner, Nick
Janke, Niko Carvajal
Baruah, Shivranjani
Gold, Kaitlin M.
Jiang, Yu
Artificial Intelligence
Systems and Control
Modern plant science increasingly relies on large, heterogeneous datasets, but challenges in experimental design, data preprocessing, and reproducibility hinder research throughput. Here we introduce Aleks, an AI-powered multi-agent system that integrates domain knowledge, data analysis, and machine learning within a structured framework to autonomously conduct data-driven scientific discovery. Once provided with a research question and dataset, Aleks iteratively formulated problems, explored alternative modeling strategies, and refined solutions across multiple cycles without human intervention. In a case study on grapevine red blotch disease, Aleks progressively identified biologically meaningful features and converged on interpretable models with robust performance. Ablation studies underscored the importance of domain knowledge and memory for coherent outcomes. This exploratory work highlights the promise of agentic AI as an autonomous collaborator for accelerating scientific discovery in plant sciences.
title Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Science
topic Artificial Intelligence
Systems and Control
url https://arxiv.org/abs/2508.19383