Perception-Aware Multimodal Spatial Reasoning from Monocular Images

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• The proposed perception-aware multimodal reasoning framework achieves substantial improvements on the SURDS benchmark compared to previous approaches.result 90%
• The framework represents objects using all Visual Reference Tokens (VRTs) within their spatial extent rather than textual bounding-box outputs.method 90%
• The method enables visual evidence and textual reasoning to be processed jointly in a unified token space.technical 90%
• A deterministic ordering strategy makes supervision over inherently unordered VRT sets compatible with autoregressive next-token prediction.technical 85%
• The method outperforms previous approaches including those using RL-based post-training.result 90%
• The framework improves performance across both single-object and multi-object spatial reasoning tasks.result 85%
• Current Vision-Language Models struggle with fine-grained geometric perception under large scale variation and ambiguous object appearance.limitation 80%
• Accurate perception and multimodal reasoning are mutually reinforcing for robust spatial understanding in challenging monocular driving scenarios.technical 80%

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