Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models

Abstract

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" that reuses optimization data for efficient payment calculation. We provide conditional bounds for approximate strategy-proofness and individual rationality based on finite-budget error and report independence. Experiments demonstrate that IAMFM achieves higher proxy-defined welfare than single-fidelity baselines across diverse budgets.