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 optimization-directed perspective and promising performance. Nevertheless, even with improved performance, current reinforcement learning approaches face challenges similar to conventional methods when dealing with complex datasets. These challenges stem from the inefficient paradigm of using one agent per feature and the inherent complexities present in the datasets. This observation motivates us to investigate and address the above issue and propose a novel approach, namely HRLFS. Our methodology initially employs a Large Language Model (LLM)-based hybrid state extractor to capture each feature's mathematical and semantic characteristics. Based on this information, features are clustered, facilitating the construction of hierarchical agents for each cluster and sub-cluster. Extensive experiments demonstrate the efficiency, scalability, and robustness of our approach. Compared to contemporary or the one-feature-one-agent RL-based approaches, HRLFS improves the downstream ML performance with iterative feature subspace exploration while accelerating total run time by reducing the number of agents involved. 1
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. However, existing benchmarks primarily focus on static question-answering (QA) tasks, failing to capture the knowledge-action gap between understanding tool usage and executing complex bioinformatics workflows. Furthermore, current evaluation paradigms often prioritize algorithmic success rates, while neglecting the biological validity. Moreover, the construction of execution benchmarks is challenging due to complex environmental dependencies and the high cost of manual annotation, leading to poor scalability. In this study, we propose BioFlowBench, a comprehensive benchmark designed to shift from static knowledge assessment to dynamic execution evaluation in bioinformatics tool utilization. First, we construct a multi-layered dataset consisting of 5,071 test samples, including Syntax Understanding, Contextual Application and Real-world Execution. Second, we introduce BioGen, an agent-based pipeline designed for the automated generation of executable benchmarks. By creating compact, low-overhead synthetic data, BioGen facilitates low-cost and large-scale testing. Third, we propose a multi-dimensional evaluation framework comprising static knowledge, structural integrity, functional validity, and efficiency metrics. Our experiments reveal that: (1) 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; (2) specialized agents outperform general models in real-world execution through environmental interaction and iterative refinement; and (3) domain knowledge remains the primary bottleneck, often leading to executable but biologically inaccurate outputs. The code is available at: https://github.com/YufeiHouAnne/BioFlowBench and the dataset can be accessed at: https://www.scidb.cn/detail?dataSetId=aee284681d674f53bfc6dae44635e773.
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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