# A VLM-based Method for Visual Anomaly Detection in Robotic Scientific Laboratories

arXiv: https://arxiv.org/abs/2506.05405 · HTML: https://mlsanity.com/paper/2506.05405/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization](https://mlsanity.com/paper/2401.06949/) (2024)
- [Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection](https://mlsanity.com/paper/2310.19070/) (2023)
- [Knowledge-based Embodied Question Answering](https://mlsanity.com/paper/2109.07872/) (2021)
- [Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection](https://mlsanity.com/paper/2406.11507/) (2024)
- [Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance Video](https://mlsanity.com/paper/2011.05054/) (2020)
- [Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing](https://mlsanity.com/paper/2004.10977/) (2020)
- [Large Language Models for Video Surveillance Applications](https://mlsanity.com/paper/2501.02850/) (2025)

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

- [LABSHIELD: A Multimodal Benchmark for Safety-Critical Reasoning and Planning in Scientific Laboratories](https://mlsanity.com/paper/2603.11987/) (2026)
- [FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios](https://mlsanity.com/paper/2604.07413/) (2026)
- [O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning](https://mlsanity.com/paper/2607.18142/) (2026)
- [Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies](https://mlsanity.com/paper/2607.18325/) (2026)
- [LabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making](https://mlsanity.com/paper/2610.19780/) (2026)

## Abstract (arXiv)

In robot scientific laboratories, visual anomaly detection is important for the timely identification and resolution of potential faults or deviations. It has become a key factor in ensuring the stability and safety of experimental processes. To address this challenge, this paper proposes a VLM-based visual reasoning approach that supports different levels of supervision through four progressively informative prompt configurations. To systematically evaluate its effectiveness, we construct a visual benchmark tailored for process anomaly detection in scientific workflows. Experiments on two representative vision-language models show that detection accuracy improves as more contextual information is provided, confirming the effectiveness and adaptability of the proposed reasoning approach for process anomaly detection in scientific workflows. Furthermore, real-world validations at selected experimental steps confirm that first-person visual observation can effectively identify process-level anomalies. This work provides both a data-driven foundation and an evaluation framework for vision anomaly detection in scientific experiment workflows.