发表机构
Dalian University of Technology; Alibaba International Digital Commerce Group; City University of Hong Kong（大连理工大学; 阿里巴巴国际数字商业集团; 香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究长期广告自动出价问题，提出SWAG - Bid分层框架，将其分解为轮次级规划与步骤级执行，规划器用掩码轨迹模型预测市场，控制器通过PSG - AdaLN调整，实验表明该方法在滑动窗口评估下有竞争力。
AI 中文摘要
自动出价系统在诸如每行动成本（CPA）等效率约束下优化出价以最大化价值。现有方法将每天视为独立轮次。但许多广告商产生价值稀疏，每日效率比统计不可靠，影响广告商留存。平台因此评估7天滑动窗口的窗口级效率。这产生跨轮次耦合，每日出价决策影响多达7个重叠窗口。我们提出SWAG - Bid，一个分层框架，将此挑战分解为轮次级规划和步骤级执行。规划器用掩码轨迹模型预测市场并生成候选计划，通过具有指数置信衰减的多窗口模型预测控制采样（MWMS）在所有重叠窗口评分。控制器通过状态自适应门、每步门控自适应层归一化（PSG - AdaLN）调整对该指导的依赖，辅以携带预算和约束信息的回报到目标和成本到目标通道。在AuctionNet - Sparse上的实验和速卖通上的在线A/B测试表明，SWAG - Bid在滑动窗口评估下实现有竞争力的约束满足和价值获取。
英文摘要
Auto-bidding systems optimize bids to maximize value under efficiency constraints such as Cost-Per-Action (CPA). Existing methods treat each day as an independent episode. However, many advertisers produce value so sparsely that per-day efficiency ratios become statistically unreliable, undermining advertiser retention. Platforms therefore evaluate window-level efficiency over sliding windows of $W{=}7$ days, ensuring fair evaluation and long-term advertising effectiveness. This creates cross-episode coupling: each day's bidding decisions affect up to $W$ overlapping windows, so setting daily targets requires anticipating future market conditions. We propose SWAG-Bid (Sliding-Window Aware Generative Auto-Bidding), a hierarchical framework decomposing this challenge into episode-level planning and step-level execution. The planner uses a Masked Trajectory Model to forecast markets and generate candidate plans, scored across all overlapping windows by Multi-Window Model Predictive Control Sampling (MWMS) with exponential confidence decay. The controller adjusts reliance on this guidance through a state-adaptive gate, Per-Step Gated Adaptive Layer Normalization (PSG-AdaLN), complemented by Return-to-Go and Cost-to-Go channels carrying budget and constraint information. Experiments on AuctionNet-Sparse and online A/B tests on AliExpress show that SWAG-Bid achieves competitive constraint satisfaction and value acquisition under sliding-window evaluation.
