CounterCount shows VLMs perform well on factual counting images but degrade on counterfactual edits, revealing reliance on object priors, and introduces an attention reweighting method that improves accuracy by up to 8%.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Reformulating 53 visual reasoning tasks in polar coordinates causes frontier MLLMs to drop from 70-83% to 31-39% accuracy while preserving logical equivalence, revealing a Cartesian shortcut in current benchmarks.
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