# Token-Level Advertising

arXiv: https://arxiv.org/abs/2608.27382 · HTML: https://mlsanity.com/paper/2608.27382/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Detecting Generated Native Ads in Conversational Search](https://mlsanity.com/paper/2402.04889/) (2024)
- [Ad Insertion in LLM-Generated Responses](https://mlsanity.com/paper/2601.19435/) (2026)
- [GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing](https://mlsanity.com/paper/2509.14221/) (2025)
- [Towards Better Models of Externalities in Sponsored Search Auctions](https://mlsanity.com/paper/1604.04095/) (2016)
- [LLM Advertisement based on Neuron Auctions](https://mlsanity.com/paper/2605.08326/) (2026)
- [Mechanism Design for Quality-Preserving LLM Advertising](https://mlsanity.com/paper/2605.10964/) (2026)
- [LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots](https://mlsanity.com/paper/2605.16474/) (2026)
- [GSP with General Independent Click-Through-Rates](https://mlsanity.com/paper/1410.3048/) (2014)

## Abstract (arXiv)

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.