@inproceedings{tsai-etal-2025-sequential,
    title = "A Sequential Multi-Stage Approach for Code Vulnerability Detection via Confidence- and Collaboration-based Decision Making",
    author = "Tsai, Chung-Nan  and
      Wang, Xin  and
      Lee, Cheng-Hsiung  and
      Lin, Ching-Sheng",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1071/",
    pages = "21162--21168",
    ISBN = "979-8-89176-332-6",
    abstract = "While large language models (LLMs) have shown strong capabilities across diverse domains, their application to code vulnerability detection holds great potential for identifying security flaws and improving software safety. In this paper, we propose a sequential multi-stage approach via confidence- and collaboration-based decision making (ConfColl). The system adopts a three-stage sequential classification framework, proceeding through a single agent, retrieval-augmented generation (RAG) with external examples, and multi-agent reasoning enhanced with RAG. The decision process selects among these strategies to balance performance and cost, with the process terminating at any stage where a high-certainty prediction is achieved. Experiments on a benchmark dataset and a low-resource language demonstrate the effectiveness of our framework in enhancing code vulnerability detection performance."
}