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
Embodied agents offer a promising route to automating scientific experimentation, yet their progress is constrained by the lack of reliable and systematic evaluation environments. Existing simulation-based laboratory benchmarks rely heavily on manual task engineering, making it challenging to systematically compile div...
Mao-Kai Qin, Chuan Qin, Qi Zhang et al.· 0 citations
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
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, Qingqing Long, Xiaolei Du et al.· 0 citations
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