Token-Level Advertising

Abstract:Generative AI is transforming how people access information, challenging traditional advertising mechanisms built around predefined slots. Towards generation-native advertising, we propose the Latent Advertiser Mixture Auction (LAMA), a token-level advertising mechanism that embeds advertiser influence directly into the generation process. Advertisers report local continuation values that induce advertiser-specific next-token policies, from which the platform decodes through a latent mixture while updating an allocation posterior. We show that LAMA satisfies Markov DSIC and IR, and achieves near-optimal KL-regularized welfare. We further develop a learning-based implementation that reconstructs the required reports online from learned local advantages and root values. Proof-of-concept experiments on real-world commercial-search query splits show that LAMA improves platform welfare and revenue while maintaining user-facing response quality, providing initial evidence for the feasibility of generation-native advertising.

Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)

arXiv:2608.27382 [cs.GT]

(or arXiv:2608.27382v1 [cs.GT] for this version)

https://doi.org/10.48550/arXiv.2608.27382 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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