Arkhon: Hamiltonian State Space Duality for Physics-Informed Multimodal World Models

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Authors: Yani Meziani
Cited by: 0

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