activity
20242026
most citedGenerative Modeling for Mathematical Discovery

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

hep-th2026

Calculating Axion-Matter Couplings in String Theory

Joshua N. Benabou, Naomi Gendler, Thomas R. Harvey +1

In this work we calculate, for the first time, the dominant perturbative contributions to axion--matter couplings explicitly in string theory. Concretely, we compute these coupling…

hep-th2026

What Neural Network Field Theory Can and Cannot Realise on a Computer

Thomas R. Harvey

One aim of neural network field theory is to put a quantum or effective field theory on a computer, with the network ensemble itself as the theory. We ask how far that aim can be p…

hep-th2026

Naturalness and Fisher Information

James Halverson, Thomas R. Harvey, Michael Nee

Fine-tuning and naturalness, the sensitivity of low-energy observables to small changes in the fundamental parameters of a theory, are cornerstones of physics beyond the Standard M…

cs.LG2025

The Optimiser Hidden in Plain Sight: Training with the Loss Landscape's Induced Metric

Thomas R. Harvey

We present a class of novel optimisers for training neural networks that makes use of the Riemannian metric naturally induced when the loss landscape is embedded in higher-dimensio…

cs.LG2025

Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks

Thomas R. Harvey, Fabian Ruehle, Kit Fraser-Taliente +1

We present a novel approach to symbolic regression using vision-capable large language models (LLMs) and the ideas behind Google DeepMind's Funsearch. The LLM is given a plot of a…

cs.LG2025★ 1 cited

Generative Modeling for Mathematical Discovery

Jordan S. Ellenberg, Cristofero S. Fraser-Taliente, Thomas R. Harvey +2

We present a new implementation of the LLM-driven genetic algorithm {\it funsearch}, whose aim is to generate examples of interest to mathematicians and which has already had some…

hep-th2024

Not So Flat Metrics

Kit Fraser-Taliente, Thomas R. Harvey, Manki Kim

In order to be in control of the derivative expansion, geometric string compactifications are understood in the context of a large volume approximation. In this letter, we con…