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 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.
Quantity-grounded multi-agent decomposition makes LLM-generated collider analysis code inspectable and reliable with 14B-scale models, outperforming prior single-prompt approaches.
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.
EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.
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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Articulating Assumptions in AI-Generated Scientific Analyses through Task Decomposition
Quantity-grounded multi-agent decomposition makes LLM-generated collider analysis code inspectable and reliable with 14B-scale models, outperforming prior single-prompt approaches.
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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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EasyScan_HEP 2: Agent-Ready Parameter Scans for High-Energy Physics
EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.
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Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.
- Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics