Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur

A new preprint describes Akasha 2, a multimodal architecture combining Hamiltonian State Space Duality with a visual-language joint embedding framework, claiming state-of-the-art video prediction and significant speed gains over existing models [1][2]. The paper, posted to the open-access repository arXiv on January 8, 2026, and subsequently withdrawn by its author Yani Meziani on June 13, 2026, details a system that integrates a Mamba-3 Selective State Space Model with a Sparse Mixture of Hamiltonian Experts [1][2]. This component enforces latent physical conservation laws through symplectic integration, an approach the authors state yields improved spatiotemporal coherence [2]. arXiv, which was founded in 1991, hosts preprints across physics, computer science, and related fields and is not a peer-reviewed journal [6]. As of late 2024, the repository was receiving approximately 24,000 new articles per month [6]. The Akasha 2 architecture introduces Hamiltonian Flow Matching and persistent 3D Gaussian Splatting for visual synthesis, which the paper claims enables ultra-low latency of under 50ms on mobile hardware [1][2]. For video prediction, the system reports a Fréchet Video Distance score of 287 [1][2]. The authors also report that the architecture is 4 times faster than diffusion models for visual synthesis and achieves a 3- to 18-times inference speedup over transformer baselines, while maintaining energy conservation over extended time horizons [1][2]. The withdrawal notice on the arXiv page provides no explanation for the removal of the paper [1]. The abstract and metadata remain accessible through the repository's standard interface, which also offers links to community-developed tools through the arXivLabs framework [1][4]. arXivLabs, launched as a formalized collaboration space in 2020, allows third-party developers to create experimental features such as citation explorers and recommender systems that appear on article pages [4][5].