Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur
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| Format: | Preprint |
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2026
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| _version_ | 1866911365704187904 |
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| author | Meziani, Yani |
| author_facet | Meziani, Yani |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_06212 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur Meziani, Yani Computer Vision and Pattern Recognition Artificial Intelligence 68T07, 68T45, 70H05 I.2.6; I.2.10; I.4.8 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. |
| title | Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T07, 68T45, 70H05 I.2.6; I.2.10; I.4.8 |
| url | https://arxiv.org/abs/2601.06212 |