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
Published:Dec 28, 2025 02:40
•1 min read
•ArXiv
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.”