Unverified50% confidenceOpinionExact time
VLMs perform excellently on tasks such as image captioning and visual question answering, but their spatial understanding ability has long been controversial, and models may guess the answer correctly through language priors rather than truly constructing spatial representations.
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50%
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Long-term
Relevance
10/13/2026
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UnverifiedHaving a VLM directly output pixel-level precise coordinates is using it in a way it is not good at; large models excel at semantic understanding and coarse-grained localization, but are unreliable in geometric precision.79% similarUnverifiedMultimodal large models are already quite capable at macro-level comprehension tasks such as describing images and answering visual questions, but still fall short in micro-level capabilities such as fine-grained localization and high-distraction visual search.74% similarUnverifiedVLM 视觉方案（如 WebVoyager、SeeAct）存在幻觉问题、坐标定位精度不足，且每次操作需消耗大量 token 处理图像信息72% similarUnverifiedTraditional spatial reasoning benchmarks usually test a model's ability to read visible relations in static images, which belongs to the perceptual level.71% similarUnverifiedViSkill stores skill cards in visual form and feeds them to the VLM as image context during inference, letting the model's own visual encoder process geometric structure, rather than first translating spatial relations into text through an external pipeline.71% similar
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