Memory degradation and learning adaptiveness fundamentally shape how groups of AI agents develop and maintain shared understanding—forgetting can actually help stabilize agreements rather than hinder them.
This paper studies how shared meaning emerges between agents in language through a non-partnership coordination game. The researchers simulate agents with different memory capabilities and adaptiveness levels, finding that agents who forget gradually maintain more stable agreements than those with fixed learning rates, while adaptive agents converge faster to shared concepts.