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" 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.
Keywords
Cite
@article{arxiv.2604.06263,
title = {Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models},
author = {Jiayuan Liu and Barry Wang and Jiarui Gan and Tonghan Wang and Leon Xie and Mingyu Guo and Vincent Conitzer},
journal= {arXiv preprint arXiv:2604.06263},
year = {2026}
}