OpenAI Blog
Feb 13
GPT-5.2 derives a new result in theoretical physics
A new preprint shows GPT-5. 2 proposing a new formula for a gluon amplitude, later formally proved and verified by OpenAI and academic collaborators.
Extending single-minus amplitudes to gravitons
A new preprint extends single-minus amplitudes to gravitons, with GPT-5. 2 Pro helping derive and verify nonzero graviton tree amplitudes in quantum gravity.
Related stories
arXiv AI
Jun 30
Gravitational Duals from Equations of State II: Large Hierarchies and False Vacua
arXiv:2606. 30117v1 Announce Type: cross Abstract: We investigate the reconstruction of holographic duals for strongly coupled quantum field theories in regimes characterized by large hierarchies and the presence of false vacua.
DeepMind Blog
May 17
Introducing Google Antigravity 2.0
DeepMind Blog
Nov 18, 2025
Introducing Google Antigravity
arXiv Machine Learning
Jun 9
Integral Formulas for Vector Signal Tensor Products
arXiv:2603. 08630v2 Announce Type: replace Abstract: We derive integral formulas that simplify the Vector Signal Tensor Product recently introduced by Xie et al.
arXiv Machine Learning
Jun 16
Learning the generating functional for variance reduction in lattice QCD
arXiv:2606. 15986v1 Announce Type: cross Abstract: The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators.
arXiv Machine Learning
Jun 15
Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks
arXiv:2606. 13941v1 Announce Type: cross Abstract: The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe.
arXiv Machine Learning
Jun 10
Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
arXiv:2606. 10698v1 Announce Type: cross Abstract: In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a frequent bottleneck in state-of-the-art calculations in theoretical particle and gravitational-wave physics.
arXiv Machine Learning
Jun 25
Flexible Gravitational-Wave Parameter Estimation with Transformers
arXiv:2512. 02968v2 Announce Type: replace-cross Abstract: Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge.
arXiv Machine Learning
Jul 24
Neural solutions of coupled ghost and gluon Dyson--Schwinger equations in Landau gauge
arXiv:2607. 21548v1 Announce Type: cross Abstract: The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural representation trained only from renormalized equation residuals.
arXiv Machine Learning
Aug 11
Variance reduction in lattice QCD observables via normalizing flows
arXiv:2603. 02984v2 Announce Type: replace-cross Abstract: Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters.
arXiv Machine Learning
Jul 24
Spectral functions in Minkowski quantum electrodynamics from neural reconstruction
arXiv:2510. 24728v2 Announce Type: replace-cross Abstract: We study neural reconstructions of quenched rainbow quantum electrodynamics (QED) Dyson--Schwinger benchmarks in Minkowski-related kinematics.