AsymComm: Cost-aware Cooperative Communication Between On-device Agents Under Information Asymmetry

As artificial intelligence and robotics advance, it is expected that on-device agents will be deployed in growing numbers, and that many situations will require these agents to cooperate. Such agents usually hold disjoint information, because each has observed a different aspect of the same phenomenon (place, event, etc.). The information between them is therefore asymmetric, and they must communicate to act together. We study how two such agents should communicate, through question answering, and introduce the AsymComm benchmark: two agents running the same model hold disjoint evidence, receive one question to solve together, and must reach a consensus answer, while every bit transmitted, every token read or generated and every round of communication is charged with the time it would cost on an edge device. The most direct protocol is context handover, in which one agent transmits its entire memory to the peer. However, we find that handover has a boundary. As the joint context grows, its accuracy declines well before the model's window is full, while its latency grows. We propose protocols for these constraints. Escalate, a protocol that sends ranked paragraphs in growing batches, is twice as accurate as handover at the largest size at a quarter of its latency. The boundary and a deadline on the answer combine into a rule for choosing a protocol. The rule itself forms a switching protocol, Guide, in which the two agents exchange only the sizes of their contexts and then run the protocol the rule selects; Guide obtains the accuracy of the best protocol at every context size, and because the rule is derived from the benchmark's measurements, a new protocol measured on the same axes enters it where it wins. The benchmark and the code that reproduces every experiment are released.