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.
The FERMIACC: Agents for Particle Theory
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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.
citing papers explorer
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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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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.