Arkhon: Hamiltonian State Space Duality for Physics-Informed Multimodal World Models
Abstract
PREPRINT: Unofficial Release of the ARKHON Architecture. We present ARKHON (formerly Project Akasha), 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 (
Fields of Study & Subjects
Generative Adversarial Networks and Image Synthesis
Model Reduction and Neural Networks
Multimodal Machine Learning Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 9 January 2026