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arXiv · 2608.27382

Token-Level Advertising

Hanbing Liu·Bowei Zhang·Changyuan Yu·Yinyu Ye·Qi Qi
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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.

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Hanbing Liu, Bowei Zhang, Changyuan Yu, Yinyu Ye, Qi Qi. 2026-08-27. Token-Level Advertising. https://arxiv.org/abs/2608.27382

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