IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

📰 ArXiv cs.AI

Token pruning framework for large vision language models reduces computational cost without requiring retraining

advanced Published 2 Apr 2026

Action Steps

Reformulate attention mechanism in dual form perspective
Identify redundant visual tokens using implicit weight pruning
Prune redundant tokens to reduce computational cost
Evaluate model performance after pruning

Who Needs to Know This

AI engineers and researchers working on large vision language models can benefit from this framework to improve model efficiency and reduce computational costs

Key Insight

💡 Token pruning can be achieved through implicit weight pruning without requiring retraining

Key Takeaways

Token pruning framework for large vision language models reduces computational cost without requiring retraining

Full Article

Title: IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

Abstract:
arXiv:2604.00757v1 Announce Type: cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Existing token pruning methods mitigate this issue through empirical approaches while overlooking the internal mechanism of attention. In this paper, we propose a novel training free token pruning framework grounded in the dual form perspective of attention. We reformulat

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