Gestalt Synaptic Coalescence: High-Dimensional Orthogonal Projection and Non-Destructive Modality Fusion in LLMs

This paper investigates the topological limits of fusing disparate neural network manifolds. Traditional weight interpolation induces catastrophic dimensional collapse, particularly within highly sensitive multimodal sub-networks. We propose Gestalt Synaptic Coalescence (GSC), a framework utilizing Zero-Padded Mapping and Orthogonal Synaptic Bridging. By projecting task-specific weights strictly into the null space of a host's pre-trained matrix, we achieve non-destructive modality superposition. Furthermore, we demonstrate that Rank-1 Singular Value Depolarization can peel alignment guardrails without inducing localized wavefunction collapse in the cognitive geometry, offering a mathematically proven zero-loss mechanism for Edge AI deployment.