BOUNDARY_SYNC: Measuring Communication-Induced Representational Coupling in Multi-Agent LLM Systems

2026-07-02 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

BOUNDARY_SYNC is a novel protocol designed to measure representational coupling in multi-agent large language model (LLM) systems, quantifying whether inter-agent communication causes output convergence or divergence. It utilizes the Coupling Amplification Factor (CAF = JSD_cond / JSD_baseline), where CAF < 1 indicates homogenization and CAF > 1 signifies diversification. Controlled experiments using GPT-4o (N=30, ~9,900 API calls) revealed that text communication induces significant homogenization, with a CAF of 0.803 [0.740, 0.873]. Image communication similarly caused homogenization, showing a CAF of 0.834 [0.811, 0.858]. Group size influenced coupling direction; K=5 agents led to homogenization, while K=3 agents resulted in diversification (CAF > 1.0). Cross-model replication demonstrated substantial variation (CAF 0.034-0.803), with DeepSeek exhibiting format artifact issues. The study also found coupling to be stateless, driven by prompt context, leading to monotonic convergence under continuous consensus. These findings confirm LLM agent coupling is measurable and controllable at the prompt level, impacting multi-agent system design.

Key takeaway

For Machine Learning Engineers designing multi-agent LLM systems, understanding representational coupling is crucial. You should actively control communication-induced homogenization or diversification by carefully crafting prompts and adjusting agent group sizes. If your goal is diverse outputs, consider smaller groups (e.g., K=3); for consensus, larger groups (e.g., K=5) and specific prompt contexts are effective. This allows you to mitigate unintended convergence or promote desired consensus in your LLM agent deployments.

Key insights

BOUNDARY_SYNC quantifies LLM agent representational coupling, revealing communication-induced homogenization or diversification controllable via prompt design.

Principles

• LLM communication induces measurable representational coupling.
• Coupling direction depends on group size.
• Prompt context, not history, drives coupling.

Method

The BOUNDARY_SYNC protocol measures representational coupling using the Coupling Amplification Factor (CAF = JSD_cond / JSD_baseline) derived from multi-agent LLM outputs.

In practice

• Control LLM agent output convergence via prompt engineering.
• Vary agent group size to influence output diversity.
• Account for cross-model coupling variations.

Topics

• Multi-Agent Systems
• Large Language Models
• Representational Coupling
• Communication Protocols
• GPT-4o
• Prompt Engineering

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.