Study: Hub-Based Multi-Agent LLMs Cause 53% Semantic Drift in Clinical AI

A new arXiv preprint by Amritesh Banerjee dismantles the assumption that scale-free or small-world network topologies are optimal for multi-agent LLM frameworks, especially in high-stakes clinical diagnostics. By mapping communication uncertainty onto a 768-dimensional Bio_ClinicalBERT embedding space, the study reveals that hub-centric architectures (Barabási–Albert and Watts–Strogatz) cause severe semantic drift: terminal cosine similarity degradation of 53.29% and variance amplification of 51.81% (ρ = 1.5181), compared to stable Erdős–Rényi random networks (ρ = 1.0766). The paper shows that localized dense cliques trap hallucinated data, preventing global consensus and driving the system to a permanent entropy saturation threshold H∞ ≈ 5.947. Instead of reducing errors, these topologies autonomously compound localized hallucinations.

To counter this, Banerjee introduces a dynamic spectral monitoring technique operating at O(N³) time complexity, imposing a strict lower bound on algebraic connectivity (λ₂_min) via continuous eigen-decomposition of the graph Laplacian. This mathematically rigorous method ensures global state diffusion and diagnostic safety. The work underscores that topological stability is non-negotiable for autonomous medical diagnostics, warning that naive adoption of popular network structures can degrade AI reliability. The findings have immediate implications for any multi-agent system where semantic fidelity is critical—from clinical decision support to financial modeling and scientific research.