Bridging the Gap: Real-Time Cost Control for Autobidding

作者：Bingzhe Wang, Bo Shen, Yuejia Dou, Qi Qi, Ruohan Qian, Changyuan Li, Xin Yuan, Bin Zou, Yi Wen, Zhi Guo, Shuanglong Li, Lin Liu, Yixuan Zhang, Yixin Su, Wenqiang Liu · 年份：2026 · DOI：10.1145/3770855.3818329 · 研究领域：Computer science、Mathematical optimization、Operations research、Economics、Risk analysis (engineering)、Mathematical economics

In the modern autobidding paradigm, automated agents optimize bids to satisfy high-level advertiser constraints. However, a critical Realization Gap persists between the theoretical guarantee of Auto-bidding Incentive Compatibility (AIC) in expectation and the stochastic reality of ex-post outcomes. This misalignment, driven by delayed conversions and finite data, leads to frequent constraint violations and utility collapse for sparse advertisers. We formally prove an impossibility result: standard mechanisms that couple allocation with payment cannot guarantee realized cost compliance in finite-sample regimes. To resolve this, we introduce the Decoupled First-Price (DFP) mechanism. DFP preserves the efficient allocation rank of standard auctions but decouples the payment rule, dynamically modulating costs via a control parameter to satisfy constraints. We prove that DFP provides strict utility dominance over standard mechanisms for value-maximizing advertisers. To operationalize DFP, we formulate payment adjustment as a Markov Decision Process solved via Proximal Policy Optimization (PPO). Extensive evaluations on the large-scale industrial dataset demonstrate significant reductions in cost deviation. A live production deployment yielded a 2.8% revenue lift and a 1.1% improvement in advertiser retention, validating the commercial necessity of real-time cost control.