arXiv:2607. 14418v1 Announce Type: new Abstract: Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes.
By Mohammad Rashid, Hema Yoganarasimhan
The paper introduces LLM‑Auction, a learning‑based generative auction mechanism that integrates ad allocation with large language model (LLM) generation. It redefines the auction object from discrete ad slots to distributions over LLM outputs, aligning allocation with advertiser value and user experience while inherently modeling allocation externalities without extra inference cost. The authors prove allocation monotonicity and continuity, show a first‑price payment rule has favorable incentive properties, and demonstrate state‑of‑the‑art allocation efficiency in a simulated LLM‑as‑a‑judge environment.
By Chujie Zhao, Qun Hu, Shiping Song, Dagui Chen, Han Zhu, Jian Xu, Bo Zheng
The paper introduces the Latent Advertiser Mixture Auction (LAMA), a token‑level advertising framework that integrates advertiser influence directly into the text generation process. Advertisers provide local continuation values that shape next‑token policies, and the platform decodes these through a latent mixture while updating an allocation posterior. LAMA is shown to satisfy Markov DSIC and IR, achieve near‑optimal KL‑regularized welfare, and, in proof‑of‑concept experiments on commercial‑search queries, improve platform welfare and revenue without compromising user‑facing response quality.
By Hanbing Liu, Bowei Zhang, Changyuan Yu, Yinyu Ye, Qi Qi
arXiv:2608. 00123v1 Announce Type: cross Abstract: LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within an evolving conversation.
By Yan Fang, Jialin Chen, Chun Gan, Hang Yu, Mingjun Nie, Yeyu Zhang, Fengxiang He, Ching Law
arXiv:2601.19435v2 Announce Type: replace-cross
Abstract: Sustainable monetization of large language models (LLMs) remains a critical open challenge. Traditional search advertising, which relies on s...
By Shengwei Xu, Zhaohua Chen, Xiaotie Deng, Zhiyi Huang, Grant Schoenebeck
arXiv:2511. 00847v5 Announce Type: replace-cross Abstract: The widespread adoption of Large Language Models (LLMs) through Application Programming Interfaces (APIs) induces a critical vulnerability: the potential for dishonest manipulation by service providers.
By Yuhan Cao, Yu Wang, Sitong Liu, Miao Li, Yixin Tao, Tianxing He