Unverified50% confidenceFactExact time
High accuracy of VLMs on spatial reasoning tasks may mask fundamental deficiencies at the representation level, relying on lexical statistical shortcuts rather than genuine spatial representations
1
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50%
Confidence
Long-term
Relevance
10/12/2026
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UnverifiedVLMs have limitations when processing high-precision engineering drawings: insufficient accuracy in recognizing small text, a tendency to hallucinate and fabricate dimensions, and limited understanding of GD&T professional symbols.78% similarUnverifiedVLMs score far above chance on compositional benchmarks, yet their internal representations do not truly encode spatial relations78% similarUnverifiedVLM在描述整体场景时表现出色，却在精确定位微小目标时频频出错，这是因为当画面元素密集时单个patch可能包含多个人物导致细节信息在下采样中被稀释或丢失74% similarUnverifiedIn hard sample detection, VLMs have stronger semantic capabilities but at a high computational cost, while lightweight detection models are fast but struggle with hard samples, forming a trade-off between accuracy and efficiency.74% similarUnverifiedAt the representation level, VLMs encode spatial relations significantly less well than lexical information74% similar
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