Wired Together: Learning Persistent Relationships through Reward-Modulated Social Plasticity

Coordination in multi-agent systems is typically specified per task, by a fixed topology, a dialogue protocol, or a planner that assigns work. We study a second axis of design: with whom agents have a relationship. We model multi-agent systems as an adaptive social network whose directed bonds evolve online through reward-modulated social plasticity: social co-activity marks a pair as eligible, and later outcomes consolidate or weaken the bond. The learned graph feeds back into learning, through reward diffusion and bond-gated experience sharing, and into inference, through social deliberation. In WIRE, an embodied environment of five chambers with increasing coordination demands, relational state helps most when it shapes learning. With RL fine-tuning, relational coupling improves cooperative completion in three of four tested configurations, while at inference time social plasticity achieves similar cooperative completion to a centralised orchestrator but higher task return, suggesting benefits on more challenging cooperative behaviour. Different social interaction regimes shape relationships differently: communication builds the strongest bonds, whereas observation or imitation leads to the highest cooperative completion. When studying long-term cooperation, we find that retaining both episodic memory and bonds across different phases of learning favours exploitation of previously successful partners, whereas retaining only bonds balances exploitation and exploration. In this way, the bond graph acts as a compact distillation of social experience while remaining plastic enough to rewire toward new collaborators. Learned relationships thus offer a decentralised complement to orchestration and provide an explicit, inspectable record of which inter-agent relationships have been reinforced through interaction.