Research Paper#Large Multimodal Models (LMMs), Visual Token Pruning, Long Context🔬 ResearchAnalyzed: Jan 3, 2026 19:39
Adaptive Visual Token Pruning for Long Context LMMs
Analysis
This paper addresses the computational cost issue in Large Multimodal Models (LMMs) when dealing with long context and multiple images. It proposes a novel adaptive pruning method, TrimTokenator-LC, that considers both intra-image and inter-image redundancy to reduce the number of visual tokens while maintaining performance. This is significant because it tackles a practical bottleneck in the application of LMMs, especially in scenarios involving extensive visual information.
Key Takeaways
- •Addresses the computational cost issue in LMMs with long context and multiple images.
- •Proposes an adaptive pruning method, TrimTokenator-LC, considering intra-image and inter-image redundancy.
- •Achieves significant visual token reduction (up to 80%) while preserving performance.
Reference
“The approach can reduce up to 80% of visual tokens while maintaining performance in long context settings.”