Algorithmic algorithm development with LLMs

A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks

Abstract

We consider LLM-based algorithm development through a case study on contraction-order optimization for tensor networks with OpenEvolve. We pay particular attention to the choice of the LLM as well as design choices such as evaluation metric and test instances. Our results highlight both the promise of verifier-guided evolutionary coding agents for algorithm development/improvement and the continuing importance of evaluation, validation, and interpretation—and corresponding challenges—by the human scientist.