Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur
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.
Cite
@article{arxiv.2601.06212,
title = {Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur},
author = {Yani Meziani},
journal= {arXiv preprint arXiv:2601.06212},
year = {2026}
}
Comments
12 pages, 6 figures, 3 tables. Includes appendices with pseudocode and implementation details. Supplementary materials eventually at github.com/yanimeziani/akasha