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arXiv · 2604.22829

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

Tairan Fu·Francisco Javier Santos-Martín·Javier Conde·Elena Merino-Gómez·Pedro Reviriego
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Abstract

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.

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Tairan Fu, Francisco Javier Santos-Martín, Javier Conde, Elena Merino-Gómez, Pedro Reviriego. 2026-04-19. Lost in Motion: Vision Language Models Fail the Dynamic Gauges Test. https://arxiv.org/abs/2604.22829

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