将广告植入生成过程，实现广告与内容的深度融合。
Token-Level Advertising
- 在生成阶段直接嵌入广告商影响，通过潜变量混合分配广告
- 实验显示平台收益与福利提升，用户响应质量保持不变
- 适合探索生成式广告的新范式，尤其是大模型应用方
生成式AI正在改变信息获取方式，挑战传统基于预设位置的广告机制。为此，我们提出一种面向生成原生广告的令牌级广告机制——隐式广告商混合拍卖（LAMA），将广告商影响力直接嵌入生成过程。广告商报告局部延续值，生成专属的下一个词策略，平台通过潜在混合解码并更新分配后验。理论证明LAMA满足马尔可夫动态激励相容（DSIC）和参与激励（IR），且接近最优的KL正则化福利。我们进一步开发了基于学习的实现方法，从学习到的局部优势和根值在线重构所需报告。在真实商业搜索查询数据集上的概念验证实验表明，LAMA在保持用户侧响应质量的前提下，显著提升了平台福利与收入，初步验证了生成式广告的可行性。
原文摘要 · Abstract (English)
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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