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.
The FERMIACC: Agents for Particle Theory
9 Pith papers cite this work. Polarity classification is still indexing.
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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.
Quantity-grounded multi-agent decomposition makes LLM-generated collider analysis code inspectable and reliable with 14B-scale models, outperforming prior single-prompt approaches.
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.
A multi-agent LLM framework that runs and audits an astroparticle physics pipeline, reproducing human results for NANOGrav phase-transition fits and flagging limitations.
Shape correlations in CEνNS allow likelihood and CNN analyses to discriminate sterile neutrinos from NSI and approximately localize sterile parameters in favorable regions.
Frontier LLMs with in-context learning and CAS integration solve most algorithmic tasks in theoretical physics when supplied with worked examples.
Future e+e- colliders can constrain new physics through precision Higgs and electroweak measurements in Higgs-coupling, EFT, and SMEFT frameworks, with updated SMEFiT code released.
EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.
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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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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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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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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DarkAgents
A multi-agent LLM framework that runs and audits an astroparticle physics pipeline, reproducing human results for NANOGrav phase-transition fits and flagging limitations.
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Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments
Shape correlations in CEνNS allow likelihood and CNN analyses to discriminate sterile neutrinos from NSI and approximately localize sterile parameters in favorable regions.
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LLMs with in-context learning for Algorithmic Theoretical Physics
Frontier LLMs with in-context learning and CAS integration solve most algorithmic tasks in theoretical physics when supplied with worked examples.
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New Physics Reach through Precision at Future Colliders: a Multi-Pronged Approach
Future e+e- colliders can constrain new physics through precision Higgs and electroweak measurements in Higgs-coupling, EFT, and SMEFT frameworks, with updated SMEFiT code released.
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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.