The paper "DeciMamba: Exploring the Length Extrapolation Potential of Mamba" by Assaf Ben-Kish, Itamar Zimerman, Shady Abu-Hussein, Nadav Cohen, Amir Globerson, Lior Wolf, and Raja Giryes from Tel Aviv University and Google Research explores the limitations and extensions of the Mamba model in handling long sequences. Mamba, an attention-free network with sub-quadratic complexity, has shown promising performance in various domains but is constrained by its effective receptive field (ERF), which is limited by the sequence length used during training. The authors identify that this ERF restriction leads to poor length-generalization capabilities, particularly in processing sequences longer than those seen during training. To address this issue, the paper introduces *DeciMamba*, a context-extension method specifically designed for Mamba. *DeciMamba* leverages a hidden filtering mechanism within the S6 layer to dynamically pool and discard unimportant tokens, effectively extending the ERF. This method enables Mamba to extrapolate to context lengths that are 25 times longer than those seen during training without additional computational resources. The paper includes a series of visualizations and analyses to demonstrate the effectiveness of *DeciMamba*. Empirical experiments on real-world long-range NLP tasks, such as document retrieval and multi-document QA, show that *DeciMamba* significantly improves Mamba's ability to handle longer sequences. The authors also provide a detailed explanation of the Mamba model and its key components, along with a discussion of related work and future directions. The main contributions of the paper are: 1. Identifying that Mamba has limited length-extrapolation capabilities due to its restricted ERF. 2. Introducing *DeciMamba*, a context-extension technique that leverages the hidden filtering mechanism in Mamba layers to extend the ERF. 3. Demonstrating that *DeciMamba* can extrapolate to context lengths that are 25 times longer than those seen during training, with significant improvements in performance on various tasks. The paper concludes with a discussion of limitations and future work, including the need for further research into biases in large language models and the exploration of different transformer context-extension methods.The paper "DeciMamba: Exploring the Length Extrapolation Potential of Mamba" by Assaf Ben-Kish, Itamar Zimerman, Shady Abu-Hussein, Nadav Cohen, Amir Globerson, Lior Wolf, and Raja Giryes from Tel Aviv University and Google Research explores the limitations and extensions of the Mamba model in handling long sequences. Mamba, an attention-free network with sub-quadratic complexity, has shown promising performance in various domains but is constrained by its effective receptive field (ERF), which is limited by the sequence length used during training. The authors identify that this ERF restriction leads to poor length-generalization capabilities, particularly in processing sequences longer than those seen during training. To address this issue, the paper introduces *DeciMamba*, a context-extension method specifically designed for Mamba. *DeciMamba* leverages a hidden filtering mechanism within the S6 layer to dynamically pool and discard unimportant tokens, effectively extending the ERF. This method enables Mamba to extrapolate to context lengths that are 25 times longer than those seen during training without additional computational resources. The paper includes a series of visualizations and analyses to demonstrate the effectiveness of *DeciMamba*. Empirical experiments on real-world long-range NLP tasks, such as document retrieval and multi-document QA, show that *DeciMamba* significantly improves Mamba's ability to handle longer sequences. The authors also provide a detailed explanation of the Mamba model and its key components, along with a discussion of related work and future directions. The main contributions of the paper are: 1. Identifying that Mamba has limited length-extrapolation capabilities due to its restricted ERF. 2. Introducing *DeciMamba*, a context-extension technique that leverages the hidden filtering mechanism in Mamba layers to extend the ERF. 3. Demonstrating that *DeciMamba* can extrapolate to context lengths that are 25 times longer than those seen during training, with significant improvements in performance on various tasks. The paper concludes with a discussion of limitations and future work, including the need for further research into biases in large language models and the exploration of different transformer context-extension methods.