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## Abstract
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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.
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---
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license: mit
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language:
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- en
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base_model:
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- openai/gpt-oss-20b
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pipeline_tag: text-generation
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tags:
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- physics
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- agent
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---
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## Abstract
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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.
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