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 (
The announcement of Akasha 2 reflects ongoing rapid advancements in multimodal AI architectures, pushing towards more efficient and principled approaches for integrating diverse data types.
This development indicates a significant step towards more sophisticated and potentially physically constrained AI systems, which could lead to breakthroughs in efficiency and robustness for complex tasks.
New methods for visual synthesis and integrated multimodal understanding, leveraging physical laws and advanced state-space models, promise more coherent and capable AI systems.
- · AI research institutions
- · Multimodal AI developers
- · Nvidia
- · AI models lacking principled physical constraints
- · Legacy enterprise AI solutions
Improved performance and efficiency across advanced AI applications requiring visual and language understanding.
Accelerated development of more embodied AI systems and a reduction in training costs due to better architectural principles.
Potential for new forms of AGI that inherently understand and interact with the physical world more effectively.
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Read at arXiv cs.AI