The Evolved Lens

Ptychographic imaging reconstructs an object from overlapping diffraction patterns — a computational inverse problem where the algorithm matters as much as the data. Standard algorithms use hand-designed regularization terms chosen by domain experts. What if the regularization itself were discovered automatically?

An LLM-driven evolutionary framework generates, evaluates, and refines regularization algorithms for ptychography. The LLM proposes candidate algorithms; evaluation on real data provides fitness; evolution selects and recombines the best. The discovered algorithms outperform manually designed ones by up to +0.26 SSIM and +8.3 dB PSNR across X-ray and electron microscopy datasets.

The structural insight: the LLM is not solving the imaging problem. It is solving the algorithm design problem — a meta-problem where the search space is the space of regularization strategies rather than the space of images. The evolutionary wrapper provides the selection pressure that the LLM’s single-shot generation cannot: it retains algorithmic components that work and discards those that don’t, accumulating design knowledge across generations that no single prompt could elicit.

The through-claim: the bottleneck in computational imaging has shifted from computation to algorithm design. Hardware is fast enough. Data is abundant. What limits reconstruction quality is the human designer’s ability to anticipate which regularization assumptions match the physics of the specific imaging modality. Automating the design search doesn’t require understanding the physics — it requires searching the space of assumptions and letting the data adjudicate. The LLM provides the generative capacity; evolution provides the judgment.