In large-scale multi-agent systems with shared resource constraints, an upstream planner must iteratively evaluate candidate resource plans -- assessing feasibility, aggregate response, and marginal cost -- before committing to one. Lagrangian relaxation separates local decisions through a broadcast cost signal, but the planner still needs the cost-to-utilization response map to explore plan space, and this map depends on population composition that changes across planning cycles. We propose \emph{population-aware coordination interfaces}: learned primal and dual maps, conditioned on compact population summaries, that the planner queries inside its iterative loop. The primal map predicts aggregate utilization under a proposed cost trajectory; the dual map predicts the cost trajectory for a target plan. By encoding response-relevant population structure, these maps remain reliable across evolving populations without per-cycle retraining, and support coordination of large populations from compact subsamples. We additionally cast Sim2Real transfer as a backtestable procedure, enabling evaluation before deployment. In a supply-chain capacity-control case study, population-aware interfaces reduce forecast error by 16--19\% and capacity violations by 20--51\% relative to population-unaware baselines under composition shift; 20K-agent cohorts support accurate coordination of 500K-agent populations; and simulator-trained primal maps achieve 11.1\% MAPE on real observations versus 13--24\% for baselines.

翻译：大规模多智能体系统在共享资源约束下，上游规划器必须迭代评估候选资源规划方案——即可行性、总体响应和边际成本——之后才能确定最终方案。拉格朗日松弛通过广播成本信号分离本地决策，但规划器仍需成本-利用率响应映射来探索规划空间，而该映射依赖于随规划周期变化的群体构成。我们提出\emph{群体感知协调接口}：基于紧凑群体摘要学习的原始映射和对偶映射，供规划器在其迭代循环中查询。原始映射预测给定成本轨迹下的总体利用率；对偶映射则预测目标规划对应的成本轨迹。通过编码与响应相关的群体结构，这些映射在群体演变过程中保持可靠性，无需每个周期重新训练，并支持从紧凑子样本协调大规模群体。我们还将Sim2Real迁移视为可回溯测试的程序，使得部署前即可进行评估。在供应链容量控制案例研究中，相较于忽略群体构成的基线方法，群体感知接口在构成变化下将预测误差降低16-19%，容量违规减少20-51%；2万智能体群体可支持50万智能体群体的精确协调；基于模拟器训练的原始映射在真实观测数据上实现11.1%的平均绝对百分比误差，而基线方法为13-24%。