Adaptive Neural State Spaces: Rethinking Computation in Modern Sequence Models

Traditional Mamba and State Space Model (SSM)architectures allocate fixed computational resources regardless of varying input complexity, leading to inefficient resource utilization across diverse sequence processing tasks. This paper introduces Adaptive Neural State Spaces (ANSS), a novel theoretical framework that enables complexity-aware computation allocation in neural sequence models. The proposed approach incorporates a mathematical formulation for dynamic resource allocation based on input difficulty assessment, providing a principled foundation for adaptive computation in state space architectures. Through rigorous theoretical analysis, formal bounds are established for energy consumption and performance trade-offs under clearly stated assumptions. The theoretical framework demonstrates potential computational savings while maintaining performance guarantees, offering mathematical insights into the fundamental principles governing adaptive neural architectures. This work contributes to the theoretical understanding of efficient sequence modeling and provides a mathematical foundation for future empirical investigations.