How Many Visual Tokens Do Multimodal Language Models Need? Scaling Visual Token Pruning with F^3A

In three linesF³A is a training-free router for visual token pruning in vision-language models. It selects relevant visual tokens via question-conditioned cues without extra LLM forward passes, reducing inference costs while maintaining performance across model scales.

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