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VLMs can miss critical context despite localized attention, but simply enlarging visual spans can dramatically boost comprehension accuracy.
Tabular reasoning gets a boost: decoupling high-level visual perception from granular symbolic reasoning yields better accuracy, especially on large tables, even with smaller models.
