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Hugging Face releases 'physics-intern', an agentic framework for theoretical physics research that doubles the performance of Gemini models on the CritPt benchmark and sets a new state-of-the-art compared to GPT-5.5 Pro.
Physics-intern is an agentic framework for theoretical physics that improves Gemini 3.1 Pro's performance on the CritPt benchmark from 17.7% to 31.4%, achieving a new state-of-the-art.
This paper investigates using Large Language Models, specifically Claude, interfaced with a Computer Algebra System (Maple) to perform algorithmic computations in theoretical physics, such as analyzing cosmological perturbations.
This paper introduces SCALAR, a structured critic-actor loop framework, to evaluate how different interaction patterns between AI agents improve reasoning in theoretical physics problems.
OpenAI researchers and collaborators have published a preprint showing that single-minus graviton tree amplitudes are nonzero under special kinematic conditions (the half-collinear regime), challenging long-held assumptions. Notably, GPT-5.2 Pro was used to derive the gravitational extension from prior gluon results, representing a significant AI-assisted breakthrough in theoretical physics.
GPT-5.2 assisted in deriving a new theoretical physics result showing that single-minus gluon tree amplitudes can be nonzero under specific half-collinear momentum conditions, challenging decades of assumptions in particle physics. The AI model identified patterns in complex Feynman diagram expressions and conjectured a general formula that was subsequently verified through formal proofs.