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

TL;DR

Akasha 2 presents a physics-informed multimodal architecture that unites Hamiltonian State Space Duality with Visual-Language Joint Embedding Predictive Architecture to achieve energy-conserving latent dynamics and long-horizon coherence. By integrating the Mamba-3 selective SSM, SMoE-HE with symplectic integration, Hamiltonian Flow Matching, and a holographic Akasha memory, the approach delivers state-of-the-art video prediction and substantial speedups over diffusion and transformer baselines, while enabling real-time mobile rendering via 3D Gaussian Splatting. The training framework enforces joint embedding objectives alongside Hamiltonian regularization and stability constraints, yielding robust, energy-aware predictions across vision and language tasks. The work demonstrates the practical impact of physics-inspired inductive biases on efficiency and reliability in latent world models, with implications for scalable, multimodal AI systems.

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.

Figures (6)

• Figure 1: Symplectic leapfrog integration process. The algorithm alternates half-steps in momentum and full steps in position, preserving the phase-space structure and ensuring energy conservation over long trajectories.
• Figure 2: The latent Hamiltonian manifold. The model learns to navigate energy potentials, ensuring physical conservation and long-term stability. Contour lines represent iso-energy surfaces, and trajectories show symplectic evolution paths.
• Figure 3: Akasha 2 overall architecture showing the flow from heterogeneous input streams through Phase-Manifold V-Sync, Mamba-3 backbone, SMoE-HE with symplectic integration, holographic memory cells, and visual synthesis heads.
• Figure 4: Holarchic Akasha Cell structure showing recursive world models. Blue arrows indicate context broadcast from parent to children, while green dashed arrows show local summaries flowing upward. This bidirectional information flow enables multi-scale reasoning.
• Figure 5: Akasha 2 training pipeline. The system processes multimodal inputs through vision and language encoders, applies masking to generate targets, predicts embeddings through the Akasha 2 model, and optimizes using a combination of JEPA, Hamiltonian conservation, and stability losses.