[2601.05930] Can We Predict Before Executing Machine Learning Agents?

Computer Science > Computation and Language arXiv:2601.05930 (cs) [Submitted on 9 Jan 2026 (v1), last revised 7 Apr 2026 (this version, v2)] Title:Can We Predict Before Executing Machine Learning Agents? Authors:Jingsheng Zheng, Jintian Zhang, Yujie Luo, Yuren Mao, Yunjun Gao, Lun Du, Huajun Chen, Ningyu Zhang View a PDF of the paper titled Can We Predict Before Executing Machine Learning Agents?, by Jingsheng Zheng and 7 other authors View PDF HTML (experimental) Abstract: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 are publicly...