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This design overlooks an important source of inductive bias in many cooperative environments, where observations naturally follow a hierarchy such as groups and entities. We propose \\textsc{HiComm}, a plug-in communication module that grounds messages in the sender's hierarchical observation. \\textsc{HiComm} is receiver-driven: the receiver issues a query, and the hierarchy is resolved through a three-stage decoding process that first selects a group, then a sender, and then an entity within that group, returning the corresponding feature slice as the message. This converts communication from unstructured vector transmission into structured information retrieval over the sender's observatio","title":"HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning","url":"https://arxiv.org/abs/2606.29126","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.29126v2 Announce Type: replace \nAbstract: Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat messages as flat dense vectors detached from the structure of the observations they summarize. This design overlooks an important source of inductive bias in many cooperative environments, where observations naturally follow a hierarchy such as groups and entities. We propose \\textsc{HiComm}, a plug-in communication module that grounds messages in the sender's hierarchical observation. \\textsc{HiComm} is receiver-driven: the receiver issues a query, and the hierarchy is resolved through a three-stage decoding process that first selects a group, then a sender, and then an entity within that group, returning the corresponding feature slice as the message. 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