[2512.10551] LLM-Auction: Generative Auction towards LLM-Native Advertising

Computer Science > Computer Science and Game Theory arXiv:2512.10551 (cs) [Submitted on 11 Dec 2025 (v1), last revised 27 Apr 2026 (this version, v2)] Title:LLM-Auction: Generative Auction towards LLM-Native Advertising Authors:Chujie Zhao, Qun Hu, Shiping Song, Dagui Chen, Han Zhu, Jian Xu, Bo Zheng View a PDF of the paper titled LLM-Auction: Generative Auction towards LLM-Native Advertising, by Chujie Zhao and 6 other authors View PDF HTML (experimental) Abstract:The commercialization of LLM applications is the next frontier in online advertising, with LLM-native advertising emerging as a promising paradigm by integrating ads into LLM-generated content. However, classic mechanisms are no longer applicable in this setting where the auction object is shifted from discrete ad slots to distributions over LLM outputs, and existing methods are impractical in industrial scenarios due to ignored externalities or high inference costs. To address these issues, we propose LLM-Auction, the first learning-based generative auction mechanism that integrates auction and generation. By formulating the allocation as preference alignment between LLM outputs and a mechanism objective that balances advertisers' value and user experience, we optimize the LLMs to inherently model allocation externalities without extra inference cost. Theoretically, we identify the allocation monotonicity and continuity of LLM-Auction, and prove that a simple first-price payment rule exhibits favorable incentiv...