Can We Predict Before Executing Machine Learning Agents?
Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer 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%. Our code and dataset will be publicly available soon at https://github.com/zjunlp/predict-before-execute.
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Jingsheng Zheng
claimed_verified
2026-03-02T09:41:23.168Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/684bc1be17ae31ba66171292/LFlkU4kArMjSzIbwjXd44.jpeg
Jingsheng Zheng
JohnsonZheng03
user
JohnsonZheng03
69646f2d138cc47cbd7652dc
Jintian Zhang
69646f2d138cc47cbd7652dd
Yujie Luo
69646f2d138cc47cbd7652de
Yuren Mao
69646f2d138cc47cbd7652df
Yunjun Gao
69646f2d138cc47cbd7652e0
Lun Du
69646f2d138cc47cbd7652e1
Huajun Chen
69646f2d138cc47cbd7652e2
Ningyu Zhang
claimed_verified
2026-01-12T15:19:07.432Z
620b3bbb0668e435407c8d0a
/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
Ningyu
user
Ningyu
Can We Predict Before Executing Machine Learning Agents?
Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer 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%. Our code and dataset will be publicly available soon at https://github.com/zjunlp/predict-before-execute.
Autonomous machine learning agents overcome execution bottlenecks by predicting outcomes before physical execution, achieving faster convergence and improved performance through a predict-then-verify approach.
autonomous machine learning agents Generate-Execute-Feedback paradigm Execution Bottleneck World Models Data-centric Solution Preference LLMs Predict-then-Verify loop convergence acceleration