aiqbench analysis
This paper introduces Akasha 2, an architecture merging Hamiltonian dynamics with selective state space models to create physics-conserving latent world models. By enforcing symplectic integration within neural networks, it achieves significant speedups in video prediction and visual synthesis while maintaining energy conservation over extended horizons. The work represents a fundamental shift toward incorporating physical laws directly into the core structure of generative AI systems rather than treating them as post-hoc constraints.
Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur
arXiv:2601.06212v2 Announce Type: replace-cross 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.
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.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
I.2.6; I.2.10; I.4.8
arXiv:2601.06212 [cs.CV]
(or arXiv:2601.06212v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2601.06212 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 232 words
