Graph Signal Processing Meets Mamba2: Adaptive Filter Bank via Delta Modulation
arXiv:2603.22333v1 Announce Type: new Abstract: State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input gating and a multi-head structure, enabling parallel computation and strong benchmark performance. However, its multi-head recurrence operates independently without structured utilization or analysis. In this work, we propose a novel method called Hierarchical ADaptive filter bank for Efficient SSMs (HADES), a Graph Signal Processing (GSP)-inspired framework that reinterprets Mamba2 as an adaptive filter bank on a line graph. Our hierarchical architecture introduces two filter types: shared filters for global low-pass behavior and expert filters for local high-pass behavior, achieved through structured bias on the parameter {\Delta}. HADES achieves comparable performance to baseline models including Mamba2 across various benchmarks in language modeling, commonsense reasoning, and
arXiv:2603.22333v1 Announce Type: new Abstract: State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input gating and a multi-head structure, enabling parallel computation and strong benchmark performance. However, its multi-head recurrence operates independently without structured utilization or analysis. In this work, we propose a novel method called Hierarchical ADaptive filter bank for Efficient SSMs (HADES), a Graph Signal Processing (GSP)-inspired framework that reinterprets Mamba2 as an adaptive filter bank on a line graph. Our hierarchical architecture introduces two filter types: shared filters for global low-pass behavior and expert filters for local high-pass behavior, achieved through structured bias on the parameter {\Delta}. HADES achieves comparable performance to baseline models including Mamba2 across various benchmarks in language modeling, commonsense reasoning, and long-context retrieval, while using only 58.9% of the original parameters. In this regard, HADES bridges GSP and neural sequence modeling, enabling efficient, hierarchical, and interpretable filtering within state-space models.
Executive Summary
This article proposes a novel framework called HADES (Hierarchical ADaptive filter bank for Efficient SSMs), which reinterprets Mamba2, a state-space model-based language model, as an adaptive filter bank on a line graph. Inspired by Graph Signal Processing (GSP), HADES introduces a hierarchical architecture with two filter types, achieving comparable performance to baseline models while reducing parameters by 41.1%. This work bridges GSP and neural sequence modeling, enabling efficient, hierarchical, and interpretable filtering within state-space models. The proposed framework has significant implications for natural language processing and neural sequence modeling, offering a promising solution for improving model efficiency and interpretability.
Key Points
- ▸ HADES reinterprets Mamba2 as an adaptive filter bank on a line graph
- ▸ Introduces a hierarchical architecture with two filter types: shared and expert filters
- ▸ Achieves comparable performance to baseline models while reducing parameters by 41.1%
- ▸ Bridges Graph Signal Processing and neural sequence modeling
Merits
Strength in Efficiency
HADES significantly reduces the number of parameters required by Mamba2, making it a more efficient alternative for language modeling and other sequence-based tasks.
Improved Interpretability
The hierarchical architecture of HADES provides a more structured and interpretable representation of the complex interactions within the model.
Scalability
The proposed framework is designed to be scalable, making it suitable for large-scale sequence modeling tasks.
Demerits
Limited Evaluation
The article only evaluates HADES on a limited set of benchmarks, which may not be representative of its performance on other tasks and datasets.
Complexity
The hierarchical architecture of HADES may introduce additional complexity, which could be challenging to implement and train.
Lack of Theoretical Analysis
The article does not provide a thorough theoretical analysis of the proposed framework, which may limit its adoption in certain applications.
Expert Commentary
The article presents a novel and innovative approach to neural sequence modeling, leveraging the principles of Graph Signal Processing to develop a hierarchical framework for state-space models. The proposed HADES framework has the potential to significantly improve the efficiency and interpretability of large-scale sequence modeling tasks, making it a promising solution for various applications in natural language processing and beyond. However, the article could have benefited from a more comprehensive evaluation of the proposed framework, as well as a deeper theoretical analysis of its underlying principles. Nevertheless, the work presented in this article is a valuable contribution to the field of neural sequence modeling and has significant implications for the development of more efficient and interpretable AI models.
Recommendations
- ✓ Future research should focus on evaluating HADES on a broader range of benchmarks and datasets to assess its generalizability and robustness.
- ✓ A more thorough theoretical analysis of the proposed framework is necessary to fully understand its underlying principles and limitations.
Sources
Original: arXiv - cs.LG
