Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning

TL;DR

This paper presents Ptychi-Evolve, an autonomous framework that leverages large language models and evolutionary algorithms to discover novel regularization techniques for ptychography, significantly improving reconstruction quality.

Contribution

It introduces a novel LLM-driven evolutionary approach for automatic regularizer discovery in ptychography, enhancing image reconstruction performance.

Findings

Discovered regularizers outperform conventional methods in SSIM and PSNR.

Framework enables interpretable and reproducible analysis of algorithms.

Achieved up to +0.26 SSIM and +8.3 dB PSNR improvements.

Abstract

Ptychography is a computational imaging technique widely used for high-resolution materials characterization, but high-quality reconstructions often require the use of regularization functions that largely remain manually designed. We introduce Ptychi-Evolve, an autonomous framework that uses large language models (LLMs) to discover and evolve novel regularization algorithms. The framework combines LLM-driven code generation with evolutionary mechanisms, including semantically-guided crossover and mutation. Experiments on three challenging datasets (X-ray integrated circuits, low-dose electron microscopy of apoferritin, and multislice imaging with crosstalk artifacts) demonstrate that discovered regularizers outperform conventional reconstructions, achieving up to +0.26 SSIM and +8.3~dB PSNR improvements. Besides, Ptychi-Evolve records algorithm lineage and evolution metadata, enabling interpretable and reproducible analysis of discovered regularizers.