Reducing Coordination Failures in LLM Agent Populations via Bilevel Information Allocation

When multiple LLM agents act separately on a shared task, individually reasonable decisions can produce poor collective outcomes: agents may crowd the same scarce resources, duplicate effort, or leave useful alternatives unused. We ask whether these failures can be reduced without updating the acting agents, by changing only which truthful state information each agent receives. The problem is difficult because responses to information are unknown and history-dependent, evaluating an allocation requires costly agent execution, and what is useful for one agent depends on what the others see. We introduce , a Bilevel Response-guided Information Allocation framework with two alternating levels. BRIA learns how the agent population responds to joint information allocations and uses that response model to choose better allocations. The two levels form a closed loop: the learned response guides allocation, the resulting allocations change the histories subsequently reached, and randomized data from those histories are used to update the response model. Across three datasets, BRIA improves response prediction and end-to-end coordination while keeping the amount of information per agent fixed, with consistent reductions in crowd-out. Ablations support history modeling, joint allocation, and repeated response-model updates, showing that jointly optimizing truthful information allocation can improve coordination without updating the acting agents.