LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior

Getting multiple AI agents to work together effectively in a shared physical environment is harder than it sounds — agents frequently act on outdated assumptions about their partners or issue redundant, mistimed communications. LLawCo addresses this by having agents reflect on past failures to extract high-level "laws of cooperation," such as knowing when to speak and when to wait, then fine-tuning on these laws so cooperative reasoning becomes intrinsic. Evaluated on the PARTNR-Dialog and TDW-MAT benchmarks, it achieves meaningful gains over state-of-the-art open-source baselines. Applications include household robots, warehouse automation, collaborative AI assistants, and any multi-agent setting requiring fluid, context-sensitive coordination. Authors: Qinhong Zhou, Chuang Gan, Anoop Cherian Paper: https://arxiv.org/abs/2606.28182v1