mlsanityData to 14 Oct 2026

An End-to-end Architecture for Collider Physics and Beyond

arXiv 2603.14553 ↗First posted 15 Mar 2026hep-ph cs.AI cs.MAcited by 5

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Abstract (arXiv)

We present, to our knowledge, the first language-driven agent system capable of executing end-to-end collider phenomenology tasks, instantiated within a decoupled, domain-agnostic architecture for autonomous High-Energy Physics phenomenology. Guided only by natural-language prompts supplemented with standard physics notation, ColliderAgent carries out workflows from a theoretical Lagrangian to final phenomenological outputs without relying on package-specific code. In this framework, a hierarchical multi-agent reasoning layer is coupled to Magnus, a unified execution backend for phenomenological calculations and simulation toolchains. We validate the system on representative literature reproductions spanning leptoquark and axion-like-particle scenarios, higher-dimensional effective operators, parton-level and detector-level analyses, and large-scale parameter scans leading to exclusion limits. These results point to a route toward more automated, scalable, and reproducible research in collider physics, cosmology, and physics more broadly.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 1 of its 46 references that are in the map. First posted on arXiv 15 Mar 2026.

Builds on

1 of 46 references.

References · 1

  1. 2026Agentic AI -- Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Datacited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.