Video advertising has become a dominant part of digital advertising, but most personalized systems still follow a retrieval-based paradigm: they match pre-existing ad content to users. A new arXiv paper argues that this approach may be limiting and introduces MAPS, a multi-agent personalized script-driven video advertising system enhanced by reinforcement learning.
Instead of retrieving ads, MAPS is designed to generate ad scripts tailored to individual viewers. The use of multiple agents and reinforcement learning suggests a more dynamic, adaptive process than conventional matching, though the abstract does not detail the system's architecture or experimental results.
The paper is available on arXiv under the identifier 2610.25748. As a research announcement, it signals growing interest in generative and reinforcement learning methods for video advertising, but the claims remain preliminary until further details or evaluations are published.