The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space

• URL: http://arxiv.org/abs/2605.09883v1
• Date: Mon, 11 May 2026 02:16:48 GMT
• Title: The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space
• Abstract summary: We identify a pervasive vulnerability, the textbfCartesian Shortcut: visual reasoning benchmarks prevalently build on orthogonal grid-based layouts.<n>Models systematically exploit this property, heavily leveraging text-based deductive reasoning to assist visual problem-solving.<n>We introduce textbfPolaris-Bench, which re-formulates 53 visual reasoning tasks in Polar coordinate space with paired Cartesian counterparts as reference.
• Score: 29.35702264173152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As current Multimodal Large Language Models rapidly saturate canonical visual reasoning benchmarks, a key question emerges: do these strong scores genuinely reflect robust visual understanding? We identify a pervasive vulnerability, the \textbf{Cartesian Shortcut}: visual reasoning benchmarks prevalently build on orthogonal grid-based layouts that can be readily discretized into explicit textual coordinates. Models systematically exploit this property, heavily leveraging text-based deductive reasoning to assist visual problem-solving. To systematically dismantle this shortcut, we introduce \textbf{Polaris-Bench}, which re-formulates 53 visual reasoning tasks in Polar coordinate space with paired Cartesian counterparts as reference, while preserving consistent logical constraints and task semantics -- thus fundamentally breaking the orthogonal prior that models exploit. Comprehensive evaluation across $14$ state-of-the-art MLLMs reveals that frontier models achieving $70$--$83\%$ on Cartesian layouts collapse to $31$--$39\%$ on Polar equivalents, with degradation persisting even under complete logical equivalence. Moreover, reasoning gains observed on Cartesian layouts are severely diminished on Polar equivalents. These findings expose a critical deficiency in current MLLMs: the lack of topology-invariant visual reasoning.

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