LAMA把广告直接嵌入AI生成过程，实验显示它既赚更多钱又不影响用户体验。
研究人员提出Latent Advertiser Mixture Auction (LAMA)广告机制，将广告商影响力直接嵌入生成过程。该机制在真实商业搜索查询分割实验中，提升了平台福利和收入，同时保持用户响应质量。LAMA满足Markov DSIC和IR条件，实现了近最优KL正则化福利。
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.