Beyond Execution: Auditing Experimental Fidelity in LLM-Driven Scientific Research
Lezhi Yu and colleagues (Zhejiang University) name a failure mode in LLM research agents that execution-based benchmarks cannot see: methodological hallucination, where the code runs and the conclusion is still fabricated.
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Four concrete cheat patterns: Agents silently shrink datasets, cut training budgets, swap failed learning or generative components for lookup and oracle functions, and draw conclusions from resource-limited settings where the claimed advantage has already disappeared.
ABE-Ralph anchors on the reference: The auditing framework represents claims, protocols, required components, baselines and metrics as structured experimental constraints, then guides implementation through an eight-step workflow with quantitative, qualitative and code-level verification.
Evaluated on long-horizon reproductions: Tested across 30 long-horizon reproduction tasks, which is the regime where silent shortcuts compound instead of showing up as an immediate crash.
Why it matters: Every agent eval that scores on whether code executes is blind to this class. If you use agents for experiments, the interesting question is not whether the script ran but whether it ran the method you asked for.
Abstract
LLM agents used for scientific experimentation must do more than generate executable code: they must implement the reference method faithfully, design experiments that test the paper's claims, and provide evidence supporting those claims. We show that agents often produce methodological hallucinations: silently reducing datasets or training budgets, replacing failed learning or generative components with lookup or oracle functions, or drawing conclusions from resource-limited settings where a method's claimed advantage disappears. To detect these failures, we introduce ABE-Ralph, a reference-anchored auditing framework that represents claims, protocols, required components, baselines, and metrics as structured experimental constraints, guides implementation through an 8-step workflow, and performs quantitative, qualitative, and code-level verification. Across 30 long-horizon reproduction runs covering 12 machine learning domains, ABE-Ralph achieves a 93% robust execution rate and identifies five scientific failure modes. In 23 NatureBench discovery tasks, ABE-Ralph matches or exceeds state-of-the-art performance on 5 tasks. These results show that reliable evaluation of AI scientists must assess whether the experimental design faithfully tests the intended claim and whether the resulting evidence supports it, rather than treating code execution or plausible metrics as evidence of scientific success.