Large language models have achieved great success in multiple challenging tasks, and their capacity can be further boosted by the emerging agentic AI techniques. This new computing paradigm has already started revolutionising the traditional scientific discovery pipelines. In this work, we propose a novel agentic AI-based knowledge discovery-oriented virtual study group that aims to extract meaningful ageing-related biological knowledge considering highly ageing-related Gene Ontology terms that are selected by hierarchical feature selection methods. We investigate the performance of the proposed agentic AI framework by considering four different model organisms' ageing-related Gene Ontology terms and validate the biological findings by reviewing existing research articles. It is found that the majority of the AI agent-generated scientific claims can be supported by existing literatures and the proposed internal mechanisms of the virtual study group also play an important role in the designed agentic AI-based knowledge discovery framework.

翻译：大语言模型在多项具有挑战性的任务中取得了巨大成功，新兴的代理式AI技术可进一步提升其能力。这种新型计算范式已开始变革传统的科学发现流程。本文提出了一种新颖的基于代理式AI的面向知识发现的虚拟研究组，旨在通过分层特征选择方法筛选出的高度相关衰老基因本体术语，提取有意义的衰老相关生物学知识。我们通过考虑四种不同模式生物的衰老相关基因本体术语，评估所提代理式AI框架的性能，并查阅现有研究文献验证生物学发现。结果表明，AI代理生成的大多数科学主张均有现有文献支持，而虚拟研究组设计的内部机制也在基于代理式AI的知识发现框架中发挥着重要作用。