{
  "annotation_id": "ae0fa6d9-2c4b-41de-a750-7f9654ce0ac1",
  "date_created": "2026-02-17T05:53:07.515000Z",
  "date_modified": "2026-02-17T05:53:07.515000Z",
  "file_hash": "a1603b64eda00f7724526a413078d7107f15213aaf1ddf7756fb0a39895f840a",
  "private": false,
  "record": {
    "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 (\u003c50ms) 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.",
    "arxiv_id": "2601.06212",
    "authors": [
      "Yani Meziani"
    ],
    "categories": [
      "cs.CV",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur",
    "url": "https://arxiv.org/abs/2601.06212",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "24a7bf08-4655-4c9e-b3a0-9e653a5da56d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}