Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks

This paper studies how communication topology affects semantic drift in clinical multi-agent language-model systems. It maps communication uncertainty into a 768-dimensional Bio_ClinicalBERT embedding space and analyzes Barabási–Albert, Watts–Strogatz, and Erdős–Rényi graph structures using an analytical isotropic-variance proxy. The abstract reports entropy saturation near H∞ ≈ 5.947, a 53.29% terminal cosine-similarity degradation, and variance amplification of ρ = 1.5181 in highly clustered architectures versus ρ = 1.0766 for Erdős–Rényi graphs. It proposes monitoring algebraic connectivity through continuous graph-Laplacian eigendecomposition, but the clinical and empirical validity of the proxy remains uncertain.