Metric-Bench: Exploring In-context Spatial Metric Reasoning in VLMs for Indoor Scenes

Metric reasoning is a critical and challenging task for VisionLanguage Models (VLMs), playing a pivotal role in embodied AI taskssuch as robotic manipulation and autonomous navigation. However, cur-rent spatial reasoning remains bottlenecked by rigid pixel-level supervi-sion; such localized optimization often compromises general multimodalintelligence, triggering performance degradation or catastrophic forget-ting of broad reasoning capabilities. To address these limitations, weintroduce Metric-Bench, a focused benchmark designed to guide metric-spatial reasoning using contextual information. By incorporating in-imagereference objects with known physical dimensions, Metric-Bench guidesmodels to implicitly learn the 2D-to-3D mapping without camera in-trinsics. We further present MetricReasoner, a task-adapted reinforce-ment fine-tuning recipe for reference-grounded metric reasoning, usingstructured prompts and verifiable numerical rewards. Extensive experi-ments on Metric-Bench demonstrate that our approach significantly en-hances spatial metric understanding, outperforming existing and evenlarger proprietary models by 43.1%, while improving downstream em-bodied performance over a spatial-specialized counterpart by 30.4% onRoboSpatial overall accuracy and 9.3% on ERQA, and additionally deliv-ering consistent gains on general benchmarks (15.9% on V⋆Bench, 88.9%on BLINK), indicating that the proposed adaptation does not necessarilycompromise general VLM capabilities.