Abstract
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.
Abstract (translated)
我们介绍了Akasha 2，这是一种最先进的多模态架构，将汉密尔顿状态空间对偶性（H-SSD）与视觉语言联合嵌入预测架构（VL-JEPA）结合在一起。该系统利用了由稀疏汉密尔顿专家混合模型（SMoE-HE）增强的Mamba-3选择性状态空间模型(SSM)，通过辛几何积分方法强制执行潜在的物理守恒定律。在视觉合成方面，我们引入了汉密尔顿流匹配(HFM)和持久3D高斯点阵(3DGS)，这使得在移动硬件上实现极低延迟（<50毫秒）成为可能。这项工作建立了一种新的潜在世界模型范式，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，在神经网络架构中加入物理学启发的归纳偏差可以带来显著改进：视频预测达到了最先进的水平（FVD: 287），视觉合成速度是扩散模型的4倍，并且推理速度比Transformer基线快3-18倍，同时还能保持长时间范围内的能量守恒。
URL
https://arxiv.org/abs/2601.06212
