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Tampered ML: Evaluating LLM Judgment of Machine Learning Research

2026-10-23 03:32:38
Soroush Ebadian, Parand A. Alamdari, Joe Benton, Hailey Joren
arXiv_AI
     
Abstract

One approach to accelerating progress on AI alignment is to automate parts of alignment research itself. Doing so requires systems that can perform reliably on hard-to-supervise research tasks, including identifying flaws in research proposals and evaluating whether a proposed experiment supports its intended claim. To evaluate this capability, we introduce Tampered ML, a benchmark of 1,858 item-pairs, each consisting of an experiment proposal reconstructed from a published machine learning paper and a minimally edited counterfactual that introduces a targeted methodological flaw. Models must identify the more methodologically sound proposal in both presentation orders. A human validation study on a stratified sample estimates that 86% of items are valid (95% CI: 81-91%). Frontier models perform above chance but remain far from reliable (with Opus 5 achieving 77.9% accuracy). They are highly sensitive to the order of presentation and favor flawed designs that appear more specific, transparent, or conventional.

Abstract (translated)

URL

https://arxiv.org/abs/2610.32114

PDF

https://arxiv.org/pdf/2610.32114.pdf

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