HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency

TL;DR

HEPTAPOD introduces an orchestration framework leveraging large language models to automate and manage complex high-energy physics workflows, enhancing reproducibility, transparency, and human-in-the-loop capabilities.

Contribution

The paper presents HEPTAPOD, a novel framework enabling LLMs to coordinate HEP workflows with domain-specific tools and structured management, advancing autonomous scientific computing.

Findings

Demonstrated HEPTAPOD's ability to manage BSM Monte Carlo validation pipelines.

Showcased structured, auditable workflow management with LLM integration.

Enabled transparent, human-in-the-loop HEP data analysis processes.

Abstract

Many workflows in high-energy-physics (HEP) stand to benefit from recent advances in transformer-based large language models (LLMs). While early applications of LLMs focused on text generation and code completion, modern LLMs now support orchestrated agency: the coordinated execution of complex, multi-step tasks through tool use, structured context, and iterative reasoning. We introduce the HEP Toolkit for Agentic Planning, Orchestration, and Deployment (HEPTAPOD), an orchestration framework designed to bring this emerging paradigm to HEP pipelines. The framework enables LLMs to interface with domain-specific tools, construct and manage simulation workflows, and assist in common utility and data analysis tasks through schema-validated operations and run-card-driven configuration. To demonstrate these capabilities, we consider a representative Beyond the Standard Model (BSM) Monte Carlo validation pipeline that spans model generation, event simulation, and downstream analysis within a unified, reproducible workflow. HEPTAPOD provides a structured and auditable layer between human researchers, LLMs, and computational infrastructure, establishing a foundation for transparent, human-in-the-loop systems.