AI RESEARCH
State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading
arXiv CS.CV
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ArXi:2604.26614v1 Announce Type: new Multimodal large language models (MLLMs) have achieved impressive progress on general multimodal tasks, yet they remain brittle on dial-based measurement reading. In this paper, we study this problem through controlled benchmarks and feature-space probing, and show that current MLLMs not only achieve unsatisfactory accuracy on dial-based readout, but also suffer sharp performance drops under viewpoint and illumination changes even when the underlying dial state remains fixed.