Token Pruning for Improving Graph-Generating State Space Model Performance (2026.eacl-srw)
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| Challenge: | State Space Models (SSMs) are efficient alternatives to Transformers for sequence modeling, but extending them to two-dimensional vision tasks remains challenging. |
| Approach: | They propose a leaf-guided pruning strategy that accelerates GG-SSM inference . they selectively scales or bypasses secondary refinement computations associated with leaf nodes . |
| Outcome: | The proposed pruning strategy accelerates GG-SSM inference without modifying graph topology . the proposed pruning method achieves throughput improvements with controlled accuracy degradation . |
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| Challenge: | Recent work proposed state-space models as an efficient alternative to transformers. |
| Approach: | They propose to prune state-space models (SSMs) to reduce computation costs by using unstructured pruning methods. |
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Rethinking Token Reduction for State Space Models (2024.emnlp-main)
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Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang
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| Challenge: | State-space models struggle with quadratic computational complexity, limiting their use in long-context tasks and resource-constrained input data. |
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| Challenge: | Existing methods for visual token pruning rely on predefined configurations without determining whether they achieve optimal performance. |
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| Challenge: | Existing approaches to video token pruning face significant computational challenges due to the redundancy inherent in video data. |
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| Challenge: | Long-video understanding is bottlenecked by the high cost of processing massive visual tokens. |
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| Challenge: | Existing methods for efficient inference rely on fixed block masks, which can lead to suboptimal performance. |
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POP: Prefill-Only Pruning for Efficient Large Model Inference (2026.findings-acl)
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| Challenge: | Existing structured pruning methods suffer from significant accuracy degradation . Existing pruning methods are expensive and require specialized hardware and kernels to perform . |
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SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
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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. |
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