@inproceedings{wang-etal-2026-dsmentor,
    title = "{DSM}entor: Curriculum-Guided Inference with Online Memory for Data-Science {LLM} Agents",
    author = "Wang, He  and
      Li, Alexander Hanbo  and
      Hu, Yiqun  and
      Zhang, Sheng  and
      Kobayashi, Hideo  and
      Zhang, Jiani  and
      Zhu, Henghui  and
      Hang, Chung-Wei  and
      Ng, Patrick",
    editor = "Gupta, Vivek  and
      Ding, Kaize  and
      Kokel, Harsha  and
      Zhao, Yue  and
      Agarwal, Amit  and
      Wang, Yu  and
      Glass, Michael  and
      Zhang, Yu  and
      Srinivas, Kavitha  and
      Chen, Xiusi  and
      Hassanzadeh, Oktie  and
      Zhu, Qi  and
      Chang, Shuaichen  and
      Luo, Yuan",
    booktitle = "Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the {LLM} Era ({SURG}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.surgellm-1.12/",
    pages = "190--208",
    ISBN = "979-8-89176-406-4",
    abstract = "Large language model (LLM) agents have shown strong capabilities in generating code to solve complex data science problems, yet they often overlook the impact of task order during inference. We present DSMentor, an inference-time optimization framework that applies curriculum learning{---}progressing from easier to harder tasks{---}to enhance LLM performance on challenging data science tasks. Guided by a mentor and supported by a growing long-term memory, DSMentor organizes problems by difficulty, retains prior experiences, and leverages them to guide subsequent reasoning. Extensive experiments on DSEval and QRData benchmarks show that DSMentor with Claude-3.5-Sonnet improves pass rates by up to 5.2{\%} over baseline agents and achieves an 8.8{\%} gain over GPT-4 with Program-of-Thoughts prompting. These results highlight the effectiveness of curriculum-based inference strategies in advancing LLM agents."
}