zjunlp/PredictBeforeExecute

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Declared licenceNot declared in the tag or the card (see datasets with no declared licence)
Licence filesNo licence file at the top level of the repository
Declared sourcesNone listed in the card metadata
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Citation text from the card

If you find our corpus helpful, please consider citing our papers 📝 and staring us ⭐️！

```bib
@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://aclanthology.org/2026.acl-long.182/",
    doi = "10.18653/v1/2026.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 So

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dataset_licence({ "id": "zjunlp/PredictBeforeExecute" })
signal: "unclear"
stated_licence: null
sources: 0

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