# PEAR: A Robust and Flexible Automation Framework for Ptychography Enabled by Multiple Large Language Model Agents

arXiv: https://arxiv.org/abs/2410.09034 · HTML: https://mlsanity.com/paper/2410.09034/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## 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, 1 of 1)

- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)

## Built on by (most cited first, 5 of 5)

- [A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application](https://mlsanity.com/paper/2412.17481/) (2024)
- [Position Bias Mitigates Position Bias:Mitigate Position Bias Through Inter-Position Knowledge Distillation](https://mlsanity.com/paper/2508.15709/) (2025)
- [DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning](https://mlsanity.com/paper/2512.07132/) (2025)
- [Adapting General-Purpose Foundation Models for X-ray Ptychography in Low-Data Regimes](https://mlsanity.com/paper/2511.02503/) (2025)
- [Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning](https://mlsanity.com/paper/2603.05696/) (2026)

## Abstract (arXiv)

Ptychography is an advanced computational imaging technique in X-ray and electron microscopy. It has been widely adopted across scientific research fields, including physics, chemistry, biology, and materials science, as well as in industrial applications such as semiconductor characterization. In practice, obtaining high-quality ptychographic images requires simultaneous optimization of numerous experimental and algorithmic parameters. Traditionally, parameter selection often relies on trial and error, leading to low-throughput workflows and potential human bias. In this work, we develop the "Ptychographic Experiment and Analysis Robot" (PEAR), a framework that leverages large language models (LLMs) to automate data analysis in ptychography. To ensure high robustness and accuracy, PEAR employs multiple LLM agents for tasks including knowledge retrieval, code generation, parameter recommendation, and image reasoning. Our study demonstrates that PEAR's multi-agent design significantly improves the workflow success rate, even with smaller open-weight models such as LLaMA 3.1 8B. PEAR also supports various automation levels and is designed to work with customized local knowledge bases, ensuring flexibility and adaptability across different research environments.