Latent Advertiser Mixture Auction

LAMA satisfies Markov DSIC and IR properties and achieves near-optimal KL-regularized welfare.Proof-of-concept experiments on real-world commercial-search query splits show improved platform welfare and revenue while maintaining user-facing response quality.

The learning-based implementation reconstructs required reports online from learned local advantages and root values.

Token-Level Advertising

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.

This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include LAMA, generative AI, advertising, token-level.

Source attribution: curated from arXiv cs.LG (@Hanbing Liu, Bowei Zhang, Changyuan Yu, Yinyu Ye, Qi Qi).