20 citations · 28 across the 12 of their papers we have counts for
13 papers
Do Quantum Models Scale Like LLMs?
David S. Berman, Ying-Jer Kao, Roger G. Melko +1
In this work, we study the neural scaling laws of RydbergGPT, an autoregressive transformer model trained on qubit projective measurement data gathered from interacting Rydberg ato…
Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks
Tancredi Schettini Gherardini, Edward Hirst, Alexander George Stapleton
The AInstein architecture introduced an unsupervised neural method for solving the Riemannian Einstein equations on arbitrary manifolds. This Physics Informed Neural Network approa…
When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning
Vasilis Niarchos, Constantinos Papageorgakis, Alexander G. Stapleton +1
As large language models (LLMs) show increasing promise on research-level physics reasoning tasks and agentic AI becomes more common, a practical question emerges: How does the int…
Efficient Conformal Block Evaluation with GoBlocks
James Chryssanthacopoulos, Vasilis Niarchos, Constantinos Papageorgakis +1
Conformal blocks in odd spacetime dimensions are not known in closed analytic form. To facilitate efficient computations in the conformal bootstrap, we introduce $\texttt{GoBlocks}…
A Machine Learning Approach to the Nirenberg Problem
Gianfranco Cortés, Maria Esteban-Casadevall, Yueqing Feng +4
This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on for metrics that are pointwise conformal…
Towards Worst-Case Guarantees with Scale-Aware Interpretability
Lauren Greenspan, David Berman, Aryeh Brill +9
Neural networks organize information according to the hierarchical, multi-scale structure of natural data. Methods to interpret model internals should be similarly scale-aware, exp…
A path to natural language through tokenisation and transformers
David S. Berman, Alexander G. Stapleton
Natural languages exhibit striking regularities in their statistical structure, including notably the emergence of Zipf's and Heaps' laws. Despite this, it remains broadly unclear…
Grokking vs. Learning: Same Features, Different Encodings
Dmitry Manning-Coe, Jacopo Gliozzi, Alexander G. Stapleton +4
Grokking typically achieves similar loss to ordinary, "steady", learning. We ask whether these different learning paths - grokking versus ordinary training - lead to fundamental di…
AInstein: Numerical Einstein Metrics via Machine Learning
Edward Hirst, Tancredi Schettini Gherardini, Alexander G. Stapleton
A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry as…
Bayesian RG Flow in Neural Network Field Theories
Jessica N. Howard, Marc S. Klinger, Anindita Maiti +1
The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormal…
NCoder -- A Quantum Field Theory approach to encoding data
David S. Berman, Marc S. Klinger, Alexander G. Stapleton
In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder. The NCoder is a modified autoencoder neural network w…
Bootstrability in Line-Defect CFT with Improved Truncation Methods
V. Niarchos, C. Papageorgakis, P. Richmond +2
We study the conformal bootstrap of 1D CFTs on the straight Maldacena-Wilson line in 4D super-Yang-Mills theory. We introduce an improved truncation scheme with an 'OP…
Bayesian Renormalization
David S. Berman, Marc S. Klinger, Alexander G. Stapleton
In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian Renormalization. The mai…