# Automating High Energy Physics Data Analysis with LLM-Powered Agents arXiv: https://arxiv.org/abs/2512.07785 ยท HTML: https://mlsanity.com/paper/2512.07785/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. - Builds on 13 of its 42 references that are in the map. - First posted on arXiv 8 Dec 2025. ## Placed in - LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/) ## Builds on (most cited first, 13 of 13) - [TinyAgent: Function Calling at the Edge](https://mlsanity.com/paper/2409.00608/) (2024) - [Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://mlsanity.com/paper/2410.04587/) (2024) - [CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis](https://mlsanity.com/paper/2407.09811/) (2024) - [Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device Agents](https://mlsanity.com/paper/2502.04392/) (2025) - [Quantum Many-Body Physics Calculations with Large Language Models](https://mlsanity.com/paper/2403.03154/) (2024) - [A Language Model for Particle Tracking](https://mlsanity.com/paper/2402.10239/) (2024) - [Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics](https://mlsanity.com/paper/2404.08001/) (2024) - [ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization](https://mlsanity.com/paper/1508.07749/) (2015) - [AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA](https://mlsanity.com/paper/2410.15222/) (2024) - [Empirical Research on Utilizing LLM-based Agents for Automated Bug Fixing via LangGraph](https://mlsanity.com/paper/2502.18465/) (2025) - [Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics](https://mlsanity.com/paper/2411.14877/) (2024) - [Physics Event Classification Using Large Language Models](https://mlsanity.com/paper/2404.05752/) (2024) - [HERA: Hybrid Edge-cloud Resource Allocation for Cost-Efficient AI Agents](https://mlsanity.com/paper/2504.00434/) (2025) ## Built on by (most cited first, 10 of 10) - [AI Agents Can Already Autonomously Perform Experimental High Energy Physics](https://mlsanity.com/paper/2603.20179/) (2026) - [Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction](https://mlsanity.com/paper/2605.13950/) (2026) - [GRACE: an Agentic AI for Particle Physics Experiment Design and Simulation](https://mlsanity.com/paper/2602.15039/) (2026) - [Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions](https://mlsanity.com/paper/2604.12364/) (2026) - [gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy](https://mlsanity.com/paper/2605.11269/) (2026) - [Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis](https://mlsanity.com/paper/2606.10381/) (2026) - [Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches](https://mlsanity.com/paper/2606.01145/) (2026) - [AgentRivet: an automated system for producing Rivet routines from journal publications](https://mlsanity.com/paper/2606.13535/) (2026) - [Machine learning for the LHC physics program: a 2025-2026 stocktake](https://mlsanity.com/paper/2609.32874/) (2026) - [Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery](https://mlsanity.com/paper/2610.12293/) (2026) ## Abstract (arXiv) We present a proof-of-principle study demonstrating the use of large language model (LLM) agents to automate a representative high energy physics (HEP) analysis. Using the Higgs boson diphoton cross-section measurement as a case study with ATLAS Open Data, we design a hybrid system that combines an LLM-based supervisor-coder agent with the Snakemake workflow manager. In this architecture, the workflow manager enforces reproducibility and determinism, while the agent autonomously generates, executes, and iteratively corrects analysis code in response to user instructions. We define quantitative evaluation metrics including success rate, error distribution, costs per specific task, and average number of API calls, to assess agent performance across multi-stage workflows. To characterize variability across architectures, we benchmark a representative selection of state-of-the-art LLMs spanning the Gemini and GPT-5 series, the Claude family, and leading open-weight models. While the workflow manager ensures deterministic execution of all analysis steps, the final outputs still show stochastic variation. Although we set the temperature to zero, other sampling parameters (e.g., top-p, top-k) remained at their defaults, and some reasoning-oriented models internally adjust these settings. Consequently, the models do not produce fully deterministic results. This study establishes the first LLM-agent-driven automated data-analysis framework in HEP, enabling systematic benchmarking of model capabilities, stability, and limitations in real-world scientific computing environments. The baseline code used in this work is available at https://huggingface.co/HWresearch/LLM4HEP. This work was accepted as a poster at the Machine Learning and the Physical Sciences (ML4PS) workshop at NeurIPS 2025. The initial submission was made on August 30, 2025.