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This paper introduces the Latent Advertiser Mixture Auction (LAMA), a novel token-level advertising mechanism that integrates advertiser influence directly into the generative AI process. By allowing advertisers to report local continuation values, LAMA induces advertiser-specific next-token policies, optimizing allocation while ensuring Markov DSIC and IR compliance. Experimental results demonstrate that LAMA enhances platform welfare and revenue without compromising the quality of user-facing responses, indicating its potential for transforming advertising in generative AI contexts.
LAMA not only boosts platform revenue but also maintains user response quality, challenging the status quo of traditional advertising models.
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.
