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

TL;DR

Akasha 2 introduces a physics-inspired multimodal architecture combining Hamiltonian state space duality with visual-language embedding, achieving state-of-the-art video prediction and ultra-fast visual synthesis on mobile hardware.

Contribution

It integrates Hamiltonian state space duality with visual-language embedding architectures, incorporating physics-based inductive biases for improved multimodal prediction and synthesis.

Findings

State-of-the-art video prediction with FVD: 287

4x faster visual synthesis than diffusion models

3-18x inference speedup over transformer baselines

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.