Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models (2025.naacl-long)
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| Challenge: | Large pre-trained models have achieved outstanding results in sequence modeling . alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address these inefficiencies. |
| Approach: | They propose to reduce the size and computational overhead of large pre-trained models by removing selected components at different granularities. |
| Outcome: | The proposed models achieve a speedup of up to 1.4x during inference while maintaining accuracy. |
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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 attempts to integrate sparsification with Mamba fail to leverage Mamba's internal structure for fine-grained sparsifying. |
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| Challenge: | Recent studies have shown that Mamba models can be used for multiple domains, including NLP, long-range sequence processing, and computer vision. |
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| Challenge: | a task-agnostic visual encoding yields minimal performance gains on grounding, but Transformers outperform Mamba at in-context multimodal retrieval. |
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Daewon Choi, Seunghyuk Oh, Saket Dingliwal, Jihoon Tack, Kyuyoung Kim, Woomin Song, Seojin Kim, Insu Han, Jinwoo Shin, Aram Galstyan, Shubham Katiyar, Sravan Babu Bodapati
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| Challenge: | Existing studies show that Mamba architectures have room for further optimization in linear projections and state caches. |
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| Challenge: | Mamba models demonstrate superior inference efficiency and competitive performance on short-context tasks, but their capacity to comprehend long contexts is limited compared to transformer-based models. |
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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
| Challenge: | Existing methods for token reduction for SSMs lead to performance drops . a recent study shows that Mamba-2 improves the accuracy of the model by 5.7% to 13.1% . |
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Mamba Knockout for Unraveling Factual Information Flow (2025.acl-long)
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| Challenge: | Recent work has introduced Mamba-based SSM architectures that rival Transformer performance in various settings. |
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Exploring the Limitations of Mamba in COPY and CoT Reasoning (2025.emnlp-main)
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| Challenge: | Inference overhead of Transformers increases linearly with the sequence length, posing challenges for modeling long sequences. |
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