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

2026-05-04

AI Summary

This work addresses the challenges in high-energy physics (HEP) literature analysis—namely, heterogeneous document formats, fragmented data sources, and reliance on manual integration—by introducing HEP-CoPilot, a novel framework that integrates multimodal retrieval-augmented generation with multi-agent collaboration. For the first time in HEP, it jointly leverages textual content, structured datasets (e.g., HEPData), and physics-specific figures to enable evidence-driven, physics-aware automated reasoning. The framework automatically retrieves experimental results, reconstructs exclusion limits, and facilitates cross-paper consistency checks. Demonstrated in a CMS new physics search case study, HEP-CoPilot significantly enhances the efficiency and systematicity of interpreting complex experimental findings.

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.

Problem

• Beyond the Standard Model
• heterogeneous information integration
• high-energy physics literature
• experimental constraints
• exclusion limits

Innovation

• retrieval-augmented generation
• multi-agent AI
• high-energy physics
• beyond Standard Model
• multimodal reasoning