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Hyperbolic vision-language models fail to leverage their geometric potential, operating instead in near-Euclidean space, which undermines their hierarchical claims.
Reweighting loss based on spatial proximity can dramatically reduce adjacent-cell errors in table structure recognition, leading to breakthrough performance improvements.
