# Imaging-101: Benchmarking LLM Coding Agents on Scientific Computational Imaging

arXiv: https://arxiv.org/abs/2607.10789 · HTML: https://mlsanity.com/paper/2607.10789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 23 of 23)

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- [Experimental robustness of Fourier Ptychography phase retrieval algorithms](https://mlsanity.com/paper/1511.02986/) (2015)
- [AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy](https://mlsanity.com/paper/2505.20538/) (2025)
- [BioDSA-1K: Benchmarking Data Science Agents for Biomedical Research](https://mlsanity.com/paper/2505.16100/) (2025)
- [Optimization methods for MR image reconstruction (long version)](https://mlsanity.com/paper/1903.03510/) (2019)
- [PtyLab.m/py/jl: a cross-platform, open-source inverse modeling toolbox for conventional and Fourier ptychography](https://mlsanity.com/paper/2301.06595/) (2023)
- [Spatiotemporal Maps for Dynamic MRI Reconstruction](https://mlsanity.com/paper/2507.14429/) (2025)
- [Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction](https://mlsanity.com/paper/2512.18161/) (2025)

## Abstract (arXiv)

Computational imaging, which recovers hidden signals from indirect, noisy measurements, underpins quantitative discovery across scientific disciplines, yet building a correct reconstruction pipeline demands deep domain expertise and remains laborious even for domain scientists. We introduce Imaging-101, a benchmark of 57 expert-verified computational imaging tasks spanning six scientific domains, each grounded in a peer-reviewed paper and canonicalized into a standardized four-stage pipeline (preprocessing, forward physics modeling, inverse solver, and visualization) Three evaluation tracks (planning, function-level unit tests, and end-to-end reconstruction) probe distinct agent capabilities across the full pipeline. Evaluating seven frontier LLMs uncovers systematic challenges in applying coding agents to computational imaging that go beyond those exposed by general coding benchmarks, spanning algorithm selection, physical convention handling, and pipeline integration. These findings highlight concrete capability gaps and point toward skill-augmented, domain-specialized agents as a practical path to reliable computational imaging assistance.