Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918373999247360 |
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| author | Yin, Xiangyu Du, Ming Deng, Junjing Yang, Zhi Han, Yimo Jiang, Yi |
| author_facet | Yin, Xiangyu Du, Ming Deng, Junjing Yang, Zhi Han, Yimo Jiang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05696 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning Yin, Xiangyu Du, Ming Deng, Junjing Yang, Zhi Han, Yimo Jiang, Yi Computational Engineering, Finance, and Science Artificial Intelligence Computation and Language Numerical Analysis 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. |
| title | Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Computation and Language Numerical Analysis |
| url | https://arxiv.org/abs/2603.05696 |