An End-to-end Architecture for Collider Physics and Beyond
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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
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Citing papers per year: 2026: 5.
5 papers.
- 2026AI Agents Can Already Autonomously Perform Experimental High Energy Physicscited by 10
- 2026Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproductioncited by 7
- 2026PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Researchcited by 6
- 2026Infrared Subtraction with Artificial Intelligencecited by 0
- 2026Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discoverycited by 0
- 2026Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery8 Oct 2026
- 2026Infrared Subtraction with Artificial Intelligence28 Sep 2026
- 2026Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction13 May 2026
- 2026PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research16 Apr 2026
- 2026AI Agents Can Already Autonomously Perform Experimental High Energy Physics20 Mar 2026