State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading
For applications requiring robust dial reading (e.g., industrial gauges), current MLLMs fail due to reliance on appearance cues; this work diagnoses the problem and provides a solution.
Multimodal LLMs perform poorly on dial-based measurement reading, with sharp accuracy drops under viewpoint/illumination changes. The proposed TriSCA framework improves state consistency and achieves significant gains on controlled and real-world benchmarks.
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. Our probing analysis further reveals that same-state samples under appearance variation are not consistently clustered, while neighboring states fail to preserve the local structure implied by continuous dial values. These findings suggest that existing MLLMs largely ignore the intrinsic state geometry of dial measurement tasks and instead rely on superficial appearance cues. Motivated by this diagnosis, we propose TriSCA, a tri-level state-consistent alignment framework for dial-based measurement reading. Specifically, TriSCA consists of state-distance-aware representation alignment, metadata-grounded observation-to-state supervision, and state-aware objective alignment. Extensive ablation studies and evaluation experiments on controlled clock and gauge benchmarks, together with evaluation on an external real-world benchmark, demonstrate the effectiveness of our method.