Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More (2025.emnlp-main)
Copied to clipboard
Zichen Wen, Yifeng Gao, Shaobo Wang, Junyuan Zhang, Qintong Zhang, Weijia Li, Conghui He, Linfeng Zhang
| Challenge: | Vision tokens in multimodal large language models often dominate computational overhead due to excessive length compared to linguistic modality. |
| Approach: | They propose a token pruning method which defines an importance criterion for vision tokens and prunes the unimportant vision token during inference. |
| Outcome: | The proposed method can prune 88.9% of vision tokens while maintaining comparable performance. |
Similar Papers
Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem? (2025.findings-acl)
Copied to clipboard
| Challenge: | Multimodal large language models have shown remarkable performance for cross-modal understanding and generation, yet suffer from severe inference costs. |
| Approach: | They propose to prune redundant tokens in MLLMs to reduce computation and storage costs. |
| Outcome: | The proposed method reduces the computational and storage costs of MLLMs by identifying redundant tokens and pruning them. |
TrimTokenator: Towards Adaptive Visual Token Pruning for Large Multimodal Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing token pruning methods rely on costly calibration or suboptimal importance metrics, leading to redundant retained tokens. |
| Approach: | They propose a token pruning strategy that preserves cross-modal alignment and informational diversity. |
| Outcome: | The proposed method maintains strong performance while reducing tokens by 88.9% on two models. |
Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)
Copied to clipboard
| Challenge: | Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts. |
| Approach: | They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy. |
| Outcome: | The proposed method selectively removes less informative tokens while maintaining performance. |
Walk and Read Less: Improving the Efficiency of Vision-and-Language Navigation via Tuning-Free Multimodal Token Pruning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large models achieve strong performance on Vision-and-Language Navigation tasks, but are costly to run in resource-limited environments. |
| Approach: | They propose a method to prune large models to minimize information loss . they use navigation-specific traits to filter the model into foreground and background . |
| Outcome: | The proposed method outperforms previous work on standard VLN benchmarks while saving 50% FLOPS. |
LVPruning: An Effective yet Simple Language-Guided Vision Token Pruning Approach for Multi-modal Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Multi-modal Large Language Models (MLLMs) incur significant computational overhead due to the large number of vision tokens processed, limiting their practicality in resource-constrained environments. |
| Approach: | They propose a language-guided vision token pruning method that can be integrated into existing MLLMs with minimal architectural changes. |
| Outcome: | The proposed method reduces vision tokens by 90% and preserves model performance. |
PruneVid: Visual Token Pruning for Efficient Video Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to video token pruning face significant computational challenges due to the redundancy inherent in video data. |
| Approach: | They propose a training-free visual token pruning method that reduces the redundancy inherent in video data and leverages LLMs’ inherent ability to selectively prune visual tokens irrelevant to specific queries. |
| Outcome: | The proposed method can prune over 80% of tokens while maintaining competitive performance when combined with different video LLMs. |
CrisPrune: Combining Contextual Relevance and Intrinsic Saliency for Efficient Visual Token Pruning in MLLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for visual token pruning compromise the integrity of visual understanding in pursuit of efficiency. |
| Approach: | They propose a model-agnostic method that integrates visual saliency and text relevance to reconcile efficiency with understanding by integrating visual salions and text relevant. |
| Outcome: | The proposed method outperforms state-of-the-art methods on LLaVA-NeXT . it achieves 13 decrease in FLOPs while maintaining 97% of original performance . |
CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to prune redundant vision tokens struggle in shallow layers due to the lack of contextual information. |
| Approach: | They propose a layer-wise contextualized visual token pruning method that uses a plug-and-play Pruning Module to prune redundant vision tokens. |
| Outcome: | The proposed method outperforms training-free pruning methods under equal token budgets and surpasses training based methods with comparable supervision. |
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
Copied to clipboard
Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
| Approach: | They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model. |
| Outcome: | The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks. |
CAPA: Contribution-Aware Pruning and FFN Approximation for Efficient Large Vision-Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Efficient inference in Large Vision Language Models is constrained by the high cost of processing thousands of visual tokens. |
| Approach: | They propose a framework that prunes visual tokens using attention contribution at critical functional transitions and reduces computations using efficient linear approximations. |
| Outcome: | The proposed framework achieves competent efficiency–performance trade-offs with improved robustness. |