Memory-as-Skill for Compositional Generalization in Embodied Multi-Agent Cooperation

Real-world embodied cooperation happens under partial observation and without a central coordinator, so each agent has to learn its own abilities and its teammates' behavior from past interaction. Its memory therefore has to carry both what the agent can do on its own and how it coordinates with teammates. Such a memory is most useful when it generalizes, because new tasks often recombine familiar elements in unseen ways. A memory of raw trajectories supports this poorly, since each trajectory binds actions and coordination to a single episode. We propose Memory-as-Skill, which turns multi-agent interaction traces into a library of individual and cooperation skills, each an abstract behavior with grounded instances. An LLM proposes skills from segmented traces, and a proposal enters the library only after it passes an execution-based admission gate on separate validation episodes. The library is then retrieved at test time to guide execution. On a multi-robot planning benchmark, the admitted library transfers to a held-out subtask combination and to held-out task families with sufficiently capable backbones, and it exceeds existing memory methods that write primitive plans. This transfer requires that skill demos be grounded into the plan by role binding, since a library whose demos the LLM must transcribe by hand fails at the same step as every primitive-plan method. On a decentralized household benchmark, where every memory method shares the same in-context use, the admitted library leads existing memory methods by small margins across open-weight backbones. Ablations show that admission, cooperation skills, and the skill hierarchy each contribute to this transfer.