The paper "The Hidden Attention of Mamba Models" by Ameen Ali, Itamar Zimerman, and Lior Wolf explores the inner workings of Mamba models, a type of selective state space model (SSM) that has shown remarkable performance in various applications, including natural language processing (NLP), image processing, and computer vision. The authors introduce a novel perspective that views Mamba models as attention-driven models, similar to transformers. This new view allows for the empirical and theoretical comparison of the underlying mechanisms and enables the development of explainability tools for Mamba models. Key contributions of the paper include: 1. **New Perspective**: Show that Mamba models can be viewed as attention-driven models, providing a new way to understand their dynamics. 2. **Hidden Attention Matrices**: Derive and analyze hidden attention matrices within Mamba layers, revealing that they generate significantly more attention matrices than transformers. 3. ** Explainability Tools**: Develop class-agnostic and class-specific explainability techniques based on these hidden attention matrices, including Attention-Rollout and Mamba-Attribution methods. 4. **Theoretical Analysis**: Provide a theoretical analysis of the evolution of attention capabilities in state-space models and their expressiveness. The paper also includes experimental results that demonstrate the effectiveness of the proposed explainability techniques and compares the performance of Mamba models with transformers in terms of explainability metrics. The findings suggest that Mamba models can achieve comparable explainability levels to transformers, highlighting the potential of Mamba models in downstream tasks requiring spatial location information.The paper "The Hidden Attention of Mamba Models" by Ameen Ali, Itamar Zimerman, and Lior Wolf explores the inner workings of Mamba models, a type of selective state space model (SSM) that has shown remarkable performance in various applications, including natural language processing (NLP), image processing, and computer vision. The authors introduce a novel perspective that views Mamba models as attention-driven models, similar to transformers. This new view allows for the empirical and theoretical comparison of the underlying mechanisms and enables the development of explainability tools for Mamba models. Key contributions of the paper include: 1. **New Perspective**: Show that Mamba models can be viewed as attention-driven models, providing a new way to understand their dynamics. 2. **Hidden Attention Matrices**: Derive and analyze hidden attention matrices within Mamba layers, revealing that they generate significantly more attention matrices than transformers. 3. ** Explainability Tools**: Develop class-agnostic and class-specific explainability techniques based on these hidden attention matrices, including Attention-Rollout and Mamba-Attribution methods. 4. **Theoretical Analysis**: Provide a theoretical analysis of the evolution of attention capabilities in state-space models and their expressiveness. The paper also includes experimental results that demonstrate the effectiveness of the proposed explainability techniques and compares the performance of Mamba models with transformers in terms of explainability metrics. The findings suggest that Mamba models can achieve comparable explainability levels to transformers, highlighting the potential of Mamba models in downstream tasks requiring spatial location information.