Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur

Akasha 2 introduces a multimodal architecture that combines Hamiltonian State Space Duality (H-SSD) with Visual-Language Joint Embedding Predictive Architecture (VL-JEPA), utilizing the Mamba-3 Selective State Space Model (SSM) and a Sparse Mixture of Hamiltonian Experts (SMoE-HE). It achieves state-of-the-art video prediction with a Fréchet Video Distance (FVD) of 287, offers 4x faster visual synthesis compared to diffusion models, and provides a 3-18x inference speedup over transformer baselines, all while ensuring energy conservation through a novel holographic memory architecture. This architecture is particularly relevant for practitioners seeking efficient, physics-informed models for real-time applications in visual synthesis and prediction.