Heterogeneous AI agents can efficiently synchronize their understanding in distributed networks by translating belief updates through edge-deployed models, without requiring shared training or identical architectures.
This paper proposes a framework for AI agents in 6G networks to synchronize their understanding (beliefs) despite using different AI models and operating under different constraints.