Generative advertising in large language model (LLM) responses requires optimizing sponsorship configurations under two strict constraints: the strategic behavior of advertisers and the high cost of stochastic generations. To address this, we propose the Incentive-Aware Multi-Fidelity Mechanism (IAMFM), a unified framework coupling Vickrey-Clarke-Groves (VCG) incentives with Multi-Fidelity Optimization to maximize expected social welfare. We compare two algorithmic instantiations (elimination-based and model-based), revealing their budget-dependent performance trade-offs. Crucially, to make VCG computationally feasible, we introduce Active Counterfactual Optimization, a "warm-start" approach that reuses optimization data for efficient payment calculation. We provide formal guarantees for approximate strategy-proofness and individual rationality, establishing a general approach for incentive-aligned, budget-constrained generative processes. Experiments demonstrate that IAMFM outperforms single-fidelity baselines across diverse budgets.

翻译：在大型语言模型（LLM）的响应中嵌入生成式广告需要满足两项严格约束来优化赞助配置：广告主策略性行为以及随机生成过程的高昂成本。为此，我们提出激励感知多保真机制（IAMFM），该统一框架将维克里-克拉克-格罗夫斯（VCG）激励与多保真优化相结合，以最大化预期社会福利。我们比较了两种算法实例化方案（基于淘汰的方法和基于模型的方法），揭示了其预算依赖的性能权衡。关键突破在于，为使VCG计算可行，我们引入了主动反事实优化——一种通过复用优化数据实现高效支付计算的"暖启动"方法。我们给出了近似策略防护性与个体理性的形式化保证，建立了面向激励相容、预算受限生成过程的通用方法论。实验表明，IAMFM在不同预算条件下均优于单保真基线方法。