# Lost in Motion: Vision Language Models Fail the Dynamic Gauges Test

arXiv: https://arxiv.org/abs/2604.22829 · HTML: https://mlsanity.com/paper/2604.22829/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 19 Apr 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Chart understanding and anomaly detection](https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/)

## Builds on (most cited first, 5 of 5)

- [PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding](https://mlsanity.com/paper/2501.16411/) (2025)
- [V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning](https://mlsanity.com/paper/2503.11495/) (2025)
- [See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning](https://mlsanity.com/paper/2301.05226/) (2023)
- [Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench](https://mlsanity.com/paper/2510.26865/) (2025)
- [Learning to Read Analog Gauges from Synthetic Data](https://mlsanity.com/paper/2308.14583/) (2023)

## Abstract (arXiv)

The digital transformation of industrial manufacturing increasingly relies on the ability of autonomous robots to interact with legacy infrastructure, particularly analog gauges. Vision-Language Models (VLMs) have the potential to provide a general solution for gauge reading and have already shown good performance in instrument recognition. However, performing accurate, real-time gauge readings is a more complex task. This paper evaluates state-of-the-art models, including versions from the GPT-5 (5.4 Thinking and 5.3 Instant) and Gemini 3 (Pro and Flash) families, against a set of simple but realistic dynamic gauge reading scenarios. To facilitate this evaluation, we introduce a novel dataset comprising video sequences of three instruments of different gauge types: circular, linear, and Vernier, under diverse motion and speed profiles. Our findings indicate that the evaluated frontier VLMs, under our specific testing conditions, exhibit a limited ability to interpret needle trajectories and scale semantics, failing to provide the traceability and reliability needed for safety-critical monitoring. The results demonstrate that these models have not yet achieved the performance necessary to be classified as trustworthy synthetic instruments under existing IEEE and ISO standards.