Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks
3.40T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2607.22758.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryThe paper analyzes how communication network topology in multi-agent LLM diagnostic systems affects semantic drift. Using Bio_ClinicalBERT embeddings across Barabási-Albert, Watts-Strogatz, and Erdős-Rényi network models, it finds hub-centric architectures amplify hallucinations, causing 53.29% cosine similarity degradation. It proposes spectral monitoring with algebraic connectivity bounds for topological stability.
Why it mattersQuantifies how multi-agent LLM network topology degrades diagnostic accuracy — hub-centric designs amplify hallucinations rather than containing them. The spectral monitoring and algebraic connectivity bound method is directly applicable to multi-agent system design.
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