# Are AI Agents interacting with Online Ads?

arXiv: https://arxiv.org/abs/2504.07112 · HTML: https://mlsanity.com/paper/2504.07112/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [OmniParser for Pure Vision Based GUI Agent](https://mlsanity.com/paper/2408.00203/) (2024)
- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [Attacking Vision-Language Computer Agents via Pop-ups](https://mlsanity.com/paper/2411.02391/) (2024)
- [GUI Agents with Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2411.04890/) (2024)
- [ClickAgent: Enhancing UI Location Capabilities of Autonomous Agents](https://mlsanity.com/paper/2410.11872/) (2024)

## Built on by (most cited first, 4 of 4)

- [Are Large Language Models Sensitive to the Motives Behind Communication?](https://mlsanity.com/paper/2510.19687/) (2025)
- [Machine-Readable Ads: Accessibility and Trust Patterns for AI Web Agents interacting with Online Advertisements](https://mlsanity.com/paper/2507.12844/) (2025)
- [Generative AI Advertising as a Problem of Trustworthy Commercial Intervention](https://mlsanity.com/paper/2605.18673/) (2026)
- [Whose Agent Are You? Multi-Layer Fingerprinting and Attribution of Autonomous Web Agents](https://mlsanity.com/paper/2606.20910/) (2026)

## Abstract (arXiv)

As AI-driven agents become increasingly integrated into the digital ecosystem, they reshape how online advertising is perceived and processed. Particularly in the travel and hotel booking sector, these autonomous systems influence the effectiveness of traditional advertising formats. While visual cues and emotional appeals sway human users, AI agents prioritize structured data such as price, availability, and specifications. This study examines how different AI agents interact with online advertising, whether they incorporate ads into their decision-making processes, and which ad formats prove most effective. We analyze interaction patterns, click behavior, and decision-making strategies through experiments with multimodal language models such as OpenAI GPT-4o, Anthropic Claude 3.7 Sonnet, and Google Gemini 2.0 Flash. Our findings reveal that AI agents neither ignore nor systematically avoid advertisements but instead favor certain features-particularly keywords and structured data. These insights have significant implications for the future design of advertising strategies in AI-dominated digital environments.