CVEvolve: Autonomous Algorithm Discovery for Unstructured Scientific Data Processing

Authors: Ming Du, Xiangyu Yin, Yanqi Luo et al.

arXiv · PDF

Summary

CVEvolve is an autonomous LLM agent system with a zero-code interface that discovers custom data-processing algorithms for scientific imaging tasks (x-ray microscopy registration, Bragg peak detection, diffraction image segmentation). It combines multi-round search with tools for code execution, evaluation, history management, holdout testing, and optional data/visual inspection. The search alternates between discovery (exploring new approaches) and improvement (refining existing ones), using lineage-aware stochastic sampling to balance exploration and exploitation. Across imaging tasks, CVEvolve discovers algorithms that beat baselines, and holdout tracking helps identify candidates that generalize better than later over-optimized ones.

Main takeaways:

• CVEvolve is a zero-code LLM agent harness for autonomous scientific algorithm discovery, targeting domain scientists without coding or image-processing expertise.
• It alternates between discovery (explore new approaches) and improvement (refine existing) actions, with lineage-aware stochastic candidate sampling.
• Tools include code execution, evaluation implementation, history management, holdout testing, and optional data/visual inspection.
• Demonstrated on three scientific imaging tasks: x-ray microscopy registration, Bragg peak detection, and diffraction image segmentation.
• Holdout test tracking helps identify algorithms that generalize better, avoiding over-optimization on the development set.

Relevance

Not related to my persona or midtraining work — this is about autonomous algorithm discovery for scientific imaging. Included because it's an interesting example of LLM-powered iterative search with evaluation and holdout tracking, which could be a useful general-purpose tool if I ever automate experimental design or hyperparameter search.

Threat model

Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses evaluation.

Abstract

arXiv:2605.11359v1 Announce Type: new Abstract: Scientific data processing often requires task-specific algorithms or AI models, creating a barrier for domain scientists who need to analyze their data but may not have extensive computing or image-processing expertise. This barrier is especially pronounced when data are noisy, have a high dynamic range, are sparsely labeled, or are only loosely specified. We introduce CVEvolve, an autonomous agentic harness with a zero-code interface for scientific data-processing algorithm discovery. CVEvolve combines a multi-round search strategy with tools for code execution, evaluation implementation, history management, holdout testing, and optional inspection of scientific data and visual outputs. The search alternates between discovery and improvement actions, and uses lineage-aware stochastic candidate sampling to balance exploration and exploitation. We demonstrate CVEvolve on x-ray fluorescence microscopy image registration, Bragg peak detection, and high-energy diffraction microscopy image segmentation. Across these tasks, CVEvolve discovers algorithms that improve over baseline methods, while holdout test tracking helps identify candidates that generalize better than later over-optimized alternatives. These results show that zero-code, autonomous LLM-powered algorithm development can help domain scientists turn unstructured scientific image data into practical algorithms and downstream scientific discoveries.