This research challenges conventional wisdom about token pruning in multimodal language models, revealing that token duplication has greater impact than token importance.

Key Findings:

• Vision tokens create significant computational overhead compared to linguistic tokens
• Traditional pruning methods that focus on token importance may be suboptimal
• Token duplication emerges as a more critical factor in determining which tokens to prune
• This insight provides a new direction for making multimodal language models more computationally efficient

Engineering Impact: This research offers AI engineers a fundamentally different approach to optimizing multimodal models, potentially reducing computational requirements while maintaining performance in vision-language systems.

Stop Looking for Important Tokens in Multimodal Language Models: Duplication Matters More