Author

Bowen Tang

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Jul 2026

GOTS: Greedy Orthogonal Token Selection for High-Resolution Vision-Language Models

This work proposes Greedy Orthogonal Token Selection (GOTS), a training-free and query-agnostic method that achieves higher average performance retention than the strongest evaluated baselines, and a controlled OCRBench study shows that it reduces model-side time-to-first-token after accounting for selection overhead.

Jun Ling, Tao Huang, Junzhuo Liu et al. · arXiv.org · 0 citations