The Cartesian Shortcut: Re-evaluating Vision Reasoning in Polar Coordinate Space
A new research paper titled "The Cartesian Shortcut" challenges the perceived robustness of current Multimodal Large Language Models (MLLMs) in visual reasoning. The authors identify a vulnerability where models exploit orthogonal grid-based layouts in standard benchmarks by converting visual data into textual coordinates, a phenomenon termed the "Cartesian Shortcut." To address this, the team introduces Polaris-Bench, a novel benchmark reformulating 53 visual reasoning tasks into Polar coordinate space while maintaining logical equivalence. Evaluations of 14 state-of-the-art MLLMs reveal a significant performance collapse, with scores dropping from 70–83% on Cartesian layouts to 31–39% on Polar equivalents. This degradation persists despite identical logical constraints, indicating that current models lack topology-invariant visual reasoning capabilities. The findings suggest that high benchmark scores do not necessarily reflect genuine visual understanding, exposing a critical deficiency in how AI systems process spatial relationships beyond simple grid structures.
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The Cartesian Shortcut: Re-evaluating Vision Reasoning in Polar Coordinate Space
A new research paper titled "The Cartesian Shortcut" challenges the perceived robustness of current Multimodal Large Language Models (MLLMs) in visual reasoning. The authors identify a vulnerability where models exploit orthogonal grid-based layouts in standard benchmarks by converting visual data into textual coordinates, a phenomenon termed the "Cartesian Shortcut." To address this, the team introduces Polaris-Bench, a novel benchmark reformulating 53 visual reasoning tasks into Polar coordinate space while maintaining logical equivalence. Evaluations of 14 state-of-the-art MLLMs reveal a significant performance collapse, with scores dropping from 70–83% on Cartesian layouts to 31–39% on Polar equivalents. This degradation persists despite identical logical constraints, indicating that current models lack topology-invariant visual reasoning capabilities. The findings suggest that high benchmark scores do not necessarily reflect genuine visual understanding, exposing a critical deficiency in how AI systems process spatial relationships beyond simple grid structures.
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