Data-driven mechanistic hypotheses are essential to scientific discovery because they explain how underlying processes produce observed phenomena. AI agents and AI scientists increasingly support scientific data analysis. However, their ability to turn empirical findings into mechanistic hypotheses remains insufficient...
Xia-Xun Xie, Qing-Qing Long, Meng Xiao et al.· 0 citations
Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrapper, and embedded-based approaches, the reinforcement learning (RL)-based subspace exploration strategy provides a novel objective optimizatio...
Wei-Liang Zhang, Xiaohan Huang, Yi Du et al.· ACM Transactions on Knowledg...· 1 citation
A significant gap exists between static QA and dynamic execution tasks, with top LLMs perform well on static QA but falter in real-world execution scenario; specialized agents outperform general models in real-world execution through environmental interaction and iterative refinement; and domain knowledge remains the p...
Yu-Fei Hou, Jiajia Wang, Ke Xiang et al.· Proceedings of the 32nd ACM...· 1 citation
Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrapper, and embedded-based approaches, the reinforcement learning (RL)-based subspace exploration strategy provides a novel objective optimizatio...
Weiliang Zhang, Xiaohan Huang, Ziyue Qiao et al.· ACM Transactions on Knowledg...· 0 citations
The rapid advancement of high-throughput technologies has led to an explosion of biological data and a subsequent surge in bioinformatics analysis tools, thereby creating an urgent demand for automated bioinformatics workflows. Recently Large Language Models (LLMs) and LLM-based agents show great potential in this area...
Yufei Hou, Jiajia Wang, Ke Xiang et al.· Proceedings of the 32nd ACM...· 1 citation
Results indicate that SciDSK improves how agents locate and understand scientific datasets, providing a stronger foundation for actionable scientific data use.
Xiaohan Huang, Qing-Qing Long, Xiaolei Du et al.· 0 citations
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