Advanced Neural Holographic Architecture: Integrating 3D Gaussian Wave Splatting and Phase-Optimized Neural CGH for Real-Time Agentic Interaction

This paper presents a theoretical and computational framework for next-generation neural holographic systems. Overcoming the physical limitations of current pseudo-holographic displays and the severe latency bottlenecks of traditional Computer-Generated Holography (CGH), we propose a topological integration of 3D Gaussian Wave Splatting (GWS) with Phase-Optimized Neural CGH. By formulating 3D Gaussian primitives with explicit depth-dependent spherical phase curvature via anisotropic tensor projection, and mitigating twin-image artifacts through a Neural-Gerchberg-Saxton (GS) optimization loop, this framework enables the sub-50ms real-time reconstruction of volumetric avatars. This macroscopic application of non-commutative geometric principles demonstrates the physical viability of ultra-low latency, true free-space holographic interaction for Embodied Agentic AI.