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The paper introduces Akasha 2, a multimodal architecture combining Hamiltonian State Space Duality (H-SSD) with Visual-Language Joint Embedding Predictive Architecture (VL-JEPA) to improve spatiotemporal coherence in latent world models. It uses Mamba-3 SSM augmented with a Sparse Mixture of Hamiltonian Experts (SMoE-HE) to enforce physical conservation laws via symplectic integration. The system achieves state-of-the-art video prediction (FVD: 287), 4x faster visual synthesis than diffusion models using Hamiltonian Flow Matching (HFM) and persistent 3D Gaussian Splatting (3DGS), and 3-18x faster inference than transformers, while maintaining energy conservation.
Physics-inspired inductive biases in neural architectures unlock 4x faster visual synthesis and up to 18x inference speedups while maintaining energy conservation.
We present Akasha 2, a state-of-the-art multimodal architecture that integrates Hamiltonian State Space Duality (H-SSD) with Visual-Language Joint Embedding Predictive Architecture (VL-JEPA). The system leverages the Mamba-3 Selective State Space Model (SSM) augmented by a Sparse Mixture of Hamiltonian Experts (SMoE-HE) that enforces latent physical conservation laws through symplectic integration. For visual synthesis, we introduce Hamiltonian Flow Matching (HFM) and persistent 3D Gaussian Splatting (3DGS), enabling ultra-low latency (<50ms) on mobile hardware. This work establishes a new paradigm in latent world models, achieving unprecedented spatiotemporal coherence through a holographic memory architecture. Our approach demonstrates that incorporating physics-inspired inductive biases into neural architectures yields significant improvements: state-of-the-art video prediction (FVD: 287), 4x faster visual synthesis than diffusion models, and 3-18x inference speedup over transformer baselines while maintaining energy conservation over extended horizons.
