LeWRON is a new agentic framework that automates construction, auditing, and exploration of finite-temperature effective potentials and gravitational-wave predictions for electroweak phase transitions starting from an input Lagrangian.
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abstract
We uncover an effective and communicative set of agents working with MadGraph. Agentic installation, learning-by-doing training, and user support provide easy access to state-of-the-art simulations and accelerate LHC research. We show in detail how MadAgents interact with inexperienced and advanced users, support a range of simulation tasks, and analyze results. In a second step, we illustrate how MadAgents automatize event generation and run an autonomous simulation campaign, starting from a pdf file of a paper. The updated Claude Code implementation includes a self-improvement loop.
years
2026 7verdicts
UNVERDICTED 7representative citing papers
AgentRivet applies commercial LLMs in an autonomous workflow to extract physics details from ATLAS and CMS papers and generate Rivet routines, achieving few syntax errors but occasional physics implementation issues on two test cases.
Collider-Bench is a new benchmark showing that current LLM agents cannot reliably reproduce LHC analyses at the level of a physicist-in-the-loop.
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
An open-source framework that automates BSM Lagrangian construction, anomaly checks, and mass-matrix derivation from natural-language field specifications by using an LLM only as an orchestration layer over a deterministic symbolic backend.
RooAgent provides an LLM agent interface that translates natural-language prompts into calls to PyROOT analysis functions for high energy physics tasks, with support for multiple AI backends and tested on ZH simulations and ATLAS open data.
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
citing papers explorer
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LeWRON: Agentic Analysis of Electroweak Phase Transitions
LeWRON is a new agentic framework that automates construction, auditing, and exploration of finite-temperature effective potentials and gravitational-wave predictions for electroweak phase transitions starting from an input Lagrangian.
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AgentRivet: an automated system for producing Rivet routines from journal publications
AgentRivet applies commercial LLMs in an autonomous workflow to extract physics details from ATLAS and CMS papers and generate Rivet routines, achieving few syntax errors but occasional physics implementation issues on two test cases.
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Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
Collider-Bench is a new benchmark showing that current LLM agents cannot reliably reproduce LHC analyses at the level of a physicist-in-the-loop.
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Large Language Model-Assisted Framework for BSM Model Building
An open-source framework that automates BSM Lagrangian construction, anomaly checks, and mass-matrix derivation from natural-language field specifications by using an LLM only as an orchestration layer over a deterministic symbolic backend.
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RooAgent: An LLM Agent for Root-Based High Energy Physics Analysis
RooAgent provides an LLM agent interface that translates natural-language prompts into calls to PyROOT analysis functions for high energy physics tasks, with support for multiple AI backends and tested on ZH simulations and ATLAS open data.
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.