[Submitted on 20 Oct 2026]
Title:Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
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Abstract:Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML engineering. We introduce Agent4Sci, a long-horizon testbed for measuring how far today's agents can improve the published best models on scientific ML benchmarks, spanning 20 benchmarks across 11 domains. Rather than ranking agents on a leaderboard, Agent4Sci asks what kinds of progress agents can make on these tasks under in-the-wild conditions, and where they fall short. Empirically, today's frontier agents advance the published best on every benchmark, with Claude Opus reaching 10.4% mean relative improvement and no run flagged for academic misconduct. These gains, however, come almost entirely from a bag of ML-engineering tricks rather than research-level insight. Trajectory analysis surfaces three recurring limitations in this setting: novelty miscalibration, resource under-utilization, and rapid collapse into local engineering sweeps. Lightweight plug-in skills for research ideation, experiment management, and scheduled checklists raise Claude Opus from 10.4% to 20.1% and Codex from 4.4% to 8.1%, while yielding more diverse, domain-relevant interventions. Agent4Sci gives the community a way to track and push frontier agents from ML engineering toward domain insight on real-world ML-for-science problems. Code and data are available on GitHub (this https URL) and Hugging Face (this https URL).
Comments: NeurIPS 2026 E&D Track
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2610.28763 [cs.AI]
(or arXiv:2610.28763v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.28763
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ming Zhong [view email] [v1] Tue, 20 Oct 2026 18:03:48 UTC (11,505 KB)
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