Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908945340170240 |
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| author | Liu, Jiayuan Wang, Barry Gan, Jiarui Wang, Tonghan Xie, Leon Guo, Mingyu Conitzer, Vincent |
| author_facet | Liu, Jiayuan Wang, Barry Gan, Jiarui Wang, Tonghan Xie, Leon Guo, Mingyu Conitzer, Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06263 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models Liu, Jiayuan Wang, Barry Gan, Jiarui Wang, Tonghan Xie, Leon Guo, Mingyu Conitzer, Vincent Computer Science and Game Theory Artificial Intelligence Information Retrieval Machine Learning 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. |
| title | Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models |
| topic | Computer Science and Game Theory Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2604.06263 |