Multi-Agent LLM Networks Face Semantic Drift Risks
A new study on arXiv challenges assumptions about communication efficiency in multi-agent LLM diagnostic frameworks. Researchers mapped uncertainty trajectories onto a 768-dimensional Bio_ClinicalBERT embedding space using Barabási–Albert (BA) and Watts–Strogatz (WS) networks. They found that structural bottlenecks compromise diagnostic safety, with phase transition matrices showing localized dense cliques confine hallucinated data, preventing global consensus. The system reaches a permanent entropy saturation threshold of H∞ ≈ 5.947, with severe terminal cosine similarity degradation.
Key facts
- Study published on arXiv with ID 2607.22758
- Uses Bio_ClinicalBERT embedding space
- Analyzes BA and WS network topologies
- Identifies structural bottlenecks in multi-agent communication
- Phase transition matrices show localized dense cliques trap hallucinated data
- Entropy saturation threshold measured at H∞ ≈ 5.947
- Terminal cosine similarity degradation observed
- Challenges assumption that scale-free or small-world networks are optimal for semantic data
Entities
Institutions
- arXiv