AI系统Aleks自主探索葡萄病害数据，自动发现关键特征并建模。
Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Science
- 构建多智能体框架，融合领域知识与机器学习自主研究。
- 在葡萄病害案例中逐步识别出有意义的生物特征并优化模型。
- 适合植物科学领域想加速数据驱动发现的研究者使用。
现代植物科学依赖大规模异构数据集，但实验设计、数据预处理和可重复性问题限制了研究效率。本文提出Aleks，一个基于人工智能的多智能体系统，将领域知识、数据分析与机器学习整合于结构化框架中，实现数据驱动的自主科学发现。给定研究问题与数据集后，Aleks无需人工干预即可迭代提出问题、探索不同建模策略并优化解决方案。在葡萄藤红斑病的案例研究中，Aleks逐步识别出具有生物学意义的特征，并收敛到可解释且性能稳健的模型。消融实验证明领域知识和记忆机制对保持结果一致性至关重要。该探索性工作展示了代理型AI作为植物科学中自主合作者的巨大潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。