Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
Collider-Bench is a new benchmark for evaluating whether LLM agents can reproduce experimental analyses from the Large Hadron Collider using only public papers and open scientific software. Agents must build simulation-and-selection pipelines and predict collision event yields. Evaluation uses histogram metrics and an LLM judge to catch failures. Initial results show no agent reliably beats the physicist-in-the-loop solution.
[2605.13950] Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
[Submitted on 13 May 2026]
Title:Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
View a PDF of the paper titled Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction, by Darius A. Faroughy and 4 other authors
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Abstract:Autonomous language-model agents are increasingly evaluated on long-horizon tool-use tasks, but existing benchmarks rarely capture the complexity and nuance of real scientific work. To address this gap, we introduce Collider-Bench, a benchmark for evaluating whether LLM agents can reproduce experimental analyses from the Large Hadron Collider (LHC) using only public papers and open scientific software. Such analyses are often difficult to reproduce because the public toolchain only approximates the software used internally by the experimental collaborations, while the published papers inevitably omit implementation details needed for a faithful reconstruction. Agents must therefore rely on physical reasoning, domain knowledge, and trial-and-error to fill these gaps. Each task requires the agent to turn a published analysis into an executable simulation-and-selection pipeline and submit predicted collision event yields in specified signal regions. These predictions are evaluated with standard histogram metrics that provide continuous fidelity scores without a hand-written rubric. We also report the computational cost incurred by each agent per task. Finally, we evaluate the codebase and full session trace using an LLM judge to catch qualitative failure modes such as fabrications, hallucinations and duplications. We release an initial set of tasks drawn from LHC searches, together with a containerized sandbox and event simulation tools. We evaluate across a capability ladder of general purpose coding agents. Our results show that on average no agent reliably beats the physicist-in-the-loop solution.
Comments: 23 pages | 9 figures | 4 tables | Code: this https URL | Task Corpus: this https URL
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); High Energy Physics - Experiment (hep-ex); High Energy Physics - Phenomenology (hep-ph)
Cite as: arXiv:2605.13950 [cs.LG]
(or arXiv:2605.13950v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.13950
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Darius Faroughy [view email] [v1] Wed, 13 May 2026 18:00:00 UTC (2,988 KB)
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