6 papers
MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
Yurii Kvasiuk, Tianyi Li, Owen Colegrove +1
We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorithm optimization framework inspi…
Fine-Tuning Small Reasoning Models for Quantum Field Theory
Nathaniel S. Woodward, Zhiqi Gao, Yurii Kvasiuk +3
Despite the growing application of Large Language Models (LLMs) to theoretical physics, there is little academic exploration into how domain-specific physics reasoning ability deve…
MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models
Tianyi Li, Shihui Zang, Moritz Münchmeyer
We develop a general framework to discover scientific algorithms and apply it to three problems in computational cosmology. Our code, MadEvolve, is similar to Google's AlphaEvolve,…
Reconstruction of Dark Matter and Baryon Density From Galaxies: A Comparison of Linear, Halo Model and Machine Learning-Based Methods
Jordan Krywonos, Yurii Kvasiuk, Matthew C. Johnson +1
For many analyses in cosmology it is necessary to reconstruct the likely distribution of unobserved fields, such as dark matter or non-luminous baryons, from observed luminous trac…
Test-time Scaling Techniques in Theoretical Physics -- A Comparison of Methods on the TPBench Dataset
Zhiqi Gao, Tianyi Li, Yurii Kvasiuk +5
Large language models (LLMs) have shown strong capabilities in complex reasoning, and test-time scaling techniques can enhance their performance with comparably low cost. Many of t…
Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics
Daniel J. H. Chung, Zhiqi Gao, Yurii Kvasiuk +5
We introduce a benchmark to evaluate the capability of AI to solve problems in theoretical physics, focusing on high-energy theory and cosmology. The first iteration of our benchma…