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.

翻译：我们提出Akasha 2，一种集成哈密顿状态空间对偶性（H-SSD）与视觉-语言联合嵌入预测架构（VL-JEPA）的最先进多模态架构。该系统利用由稀疏混合哈密顿专家（SMoE-HE）增强的Mamba-3选择性状态空间模型（SSM），通过辛积分强制施加潜在物理守恒定律。针对视觉合成，我们引入哈密顿流匹配（HFM）和持久化3D高斯泼溅（3DGS），在移动硬件上实现超低延迟（<50ms）。本工作建立了潜在世界模型的新范式，通过全息记忆架构实现了前所未有的时空一致性。我们的方法证明了将物理启发式归纳偏置融入神经架构可带来显著提升：最先进的视频预测（FVD: 287）、比扩散模型快4倍的视觉合成速度、以及相比Transformer基线3-18倍的推理加速，同时能在长时域上维持能量守恒。