Reproduce “Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks” on OpenResearch

About this paper

Amritesh Banerjee

This work introduces a mathematical framework to quantify semantic error propagation in multi-agent Large Language Model networks, particularly for clinical diagnostics. It demonstrates that prevalent network topologies such as Barabási-Albert and Watts-Strogatz amplify semantic drift and cause diagnostic uncertainty, while proposing a dynamic spectral monitoring algorithm to enforce topological stability.

Key insights

• Traditional scale-free and small-world network topologies, while efficient for binary data, act as 'semantic echo chambers' for continuous semantic information, confining and amplifying hallucinated data within local cliques rather than dispersing it.
• The algebraic connectivity (Fiedler value) of the graph Laplacian is a crucial spectral property dictating network resilience; robust connectivity enables noise dispersion, while collapsed connectivity traps corrupted signals, driving the system to entropy saturation.
• The high-dimensional latent space of LLMs like Bio ClinicalBERT is susceptible to significant semantic degradation under continuous noise, indicating that multi-agent systems require robust topological error correction to maintain clinical context and diagnostic coherence.

Summary by alphaXiv.