A PHENOMENOLOGICAL EXPERT SYSTEM FOR SPATIOTEMPORAL ACTION ANALYSIS USING SEQUENTIAL VERTEX COUPLING AS THE RAMP NETWORK FRAMEWORK FOR VERIFIABLE SPATIAL INTELLIGENCE

Abstract

This paper presents a novel approach to object identifiable visualization by integrating the RAMP network model into a dynamic environment. While traditional methods struggle with temporal coherence and "ghosting" artifacts, our model leverages H2T to map 2D projections into a consistent 4D spatiotemporal volume.

"We present RAMP, an Expert System designed for conclusive action analysis. Unlike probabilistic AI, RAMP utilizes a deterministic Zig-Zag Information Flow to ground visual perception in a persistent knowledge base..."

Reconstructing 3D objects in dynamic 4D sequences often results in "hallucinations"—erratic temporal flickering and non-physical geometric artifacts. This paper introduces a stabilized projection framework using the RAMP network. We propose a novel Constant Weight (W) constraint on the top-side vertices of the projection squares, linked via a Head-to-Tail (H2T) connectivity model. Our mathematical proof demonstrates that this configuration acts as a geometric anchor, suppressing stochastic noise. Experimental results show an 85.2% reduction in hallucination rates and a 27.3% improvement in PSNR, providing a high-fidelity solution for verifiable spatial intelligence.