Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models
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" appro...
| Authors | Jiayuan Liu, Barry Wang, Jiarui Gan et al. |
|---|---|
| Primary category | cs.GT |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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