BOUNDARY_SYNC: Measuring Communication-Induced Representational Coupling in Multi-Agent LLM Systems
arXiv:2607.01600v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed as communicating agents, does inter-agent communication cause outputs to converge? We introduce BOUNDARY_SYNC, a protocol measuring representational coupling via the Coupling Amplification Factor (CAF = JSD_cond / JSD_baseline), where CAF 1 indicates diversification. In controlled GPT-4o experiments (N=30, ~9,900 API calls), we measure coupling in text and image communication. Key findings: (1) text communication causes significant homogenization (CAF=0.803 [0.740, 0.873], d=1.30, p 1.0 (point estima
The proliferation of multi-agent LLM systems in various applications necessitates understanding how communication shapes their collective behavior and outputs.
This research provides a crucial metric for evaluating the effectiveness and potential risks of multi-agent LLM systems, specifically regarding representational coupling and convergence.
The introduction of BOUNDARY_SYNC offers a standardized protocol to measure representational coupling, informing design choices for more robust and diversified multi-agent AI systems.
- · AI developers
- · Multi-agent system designers
- · AI safety researchers
- · Undifferentiated multi-agent systems
- · Organizations deploying unsophisticated AI agents
System designers will gain a quantifiable method to optimize communication strategies for multi-agent LLMs.
This understanding could lead to the development of novel communication protocols that prevent unwanted homogenization in AI agent networks.
Future regulations or best practices for AI agent deployment might incorporate metrics like the Coupling Amplification Factor to ensure diverse and robust outcomes.
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