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.

翻译：叠层衍射成像是一种广泛应用于高分辨率材料表征的计算成像技术，但高质量重建通常需要使用正则化函数，而这些函数在很大程度上仍需人工设计。我们提出了Ptychi-Evolve，一个利用大语言模型发现并进化新型正则化算法的自主框架。该框架将LLM驱动的代码生成与进化机制相结合，包括语义引导的交叉和变异。在三个具有挑战性的数据集（X射线集成电路、低剂量载铁蛋白电子显微镜以及存在串扰伪影的多层切片成像）上的实验表明，所发现的正则化器优于传统重建方法，实现了高达+0.26 SSIM和+8.3~dB PSNR的提升。此外，Ptychi-Evolve记录了算法谱系和进化元数据，使得对所发现正则化器的分析具有可解释性和可复现性。