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

• URL: http://arxiv.org/abs/2604.06263v1
• Date: Tue, 07 Apr 2026 02:10:16 GMT
• Title: Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models
• Abstract summary: We propose a unified framework coupling Vickrey-Clarke-Groveswarm incentives with Multi-Fidelity Optimization to maximize expected social welfare.<n>Experiments demonstrate that IAMFM outperforms single-fidelity baselines across diverse budgets.
• Score: 34.926198199616955
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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