From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model

TL;DR

HEP-CoPilot is a retrieval-augmented multi-agent AI framework that unifies heterogeneous high-energy physics data and literature to interpret collider search results efficiently.

Contribution

It introduces a novel multimodal AI system that integrates textual, numerical, and graphical data for physics analysis, enabling automated interpretation of experimental results.

Findings

Successfully retrieves relevant measurements from literature.

Reconstructs exclusion limits directly from HEPData.

Performs cross-paper comparisons of experimental constraints.

Abstract

Modern searches for physics beyond the Standard Model produce rapidly expanding literature containing heterogeneous information, including textual analyses, numerical datasets, and graphical exclusion limits. Integrating these distributed sources remains a time-consuming and manual process for physicists. We present HEP-CoPilot, a retrieval-augmented multi-agent AI framework for the exploration and interpretation of high-energy physics literature. The system unifies textual information from publications, structured experimental data from HEPData, and reconstructed physics plots within a multimodal retrieval and reasoning architecture. By combining retrieval-augmented language models with coordinated agent workflows, it enables evidence-grounded reasoning over experimental analyses and structured interpretation of collider results. We evaluate the framework on recent CMS searches for physics beyond the Standard Model. Case studies show that HEP-CoPilot can retrieve relevant measurements, reconstruct exclusion limits directly from HEPData records, and perform cross-paper comparisons of experimental constraints. This enables consistent, physics-aware comparison across analyses without manual data integration. These results demonstrate that retrieval-augmented AI systems can function as scientific co-pilots for particle physics, facilitating navigation of complex literature, structuring heterogeneous evidence, and accelerating the interpretation pipeline for new physics searches.