@inproceedings{zheng-etal-2026-predict,
    title = "Can We Predict Before Executing Machine Learning Agents?",
    author = "Zheng, Jingsheng  and
      Zhang, Jintian  and
      Luo, Yujie  and
      Mao, Yuren  and
      Gao, Yunjun  and
      Du, Lun  and
      Chen, Huajun  and
      Zhang, Ningyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.182/",
    pages = "3941--3974",
    ISBN = "979-8-89176-390-6",
    abstract = "Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffers from a severe Execution Bottleneck, as hypothesis evaluation relies strictly on expensive physical execution. To bypass these physical constraints, we internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. In this work, we formalize the task of Data-centric Solution Preference and construct a comprehensive corpus of 18,438 pairwise comparisons. We demonstrate that LLMs exhibit significant predictive capabilities when primed with a Verified Data Analysis Report, achieving 61.5{\%} accuracy and robust confidence calibration. Finally, we instantiate this framework in ForeAgent, an agent that employs a Predict-then-Verify loop, achieving a 6x acceleration in convergence while surpassing execution-based baselines by +6{\%}."
}