When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents
It addresses the problem of understanding implicit coordination in multi-agent AI systems, which is incremental as it builds on existing work on explicit communication.
The paper investigates how LLM-based agents coordinate implicitly through covert communication in multi-agent systems, analyzing game-theoretic settings to characterize when such signals emerge and their impact on strategic outcomes.
LLMs-based agents increasingly operate in multi-agent environments where strategic interaction and coordination are required. While existing work has largely focused on individual agents or on interacting agents sharing explicit communication, less is known about how interacting agents coordinate implicitly. In particular, agents may engage in covert communication, relying on indirect or non-linguistic signals embedded in their actions rather than on explicit messages. This paper presents a game-theoretic study of covert communication in LLM-driven multi-agent systems. We analyse interactions across four canonical game-theoretic settings under different communication regimes, including explicit, restricted, and absent communication. Considering heterogeneous agent personalities and both one-shot and repeated games, we characterise when covert signals emerge and how they shape coordination and strategic outcomes.