Proactive AI agents are designed to jump into group conversations with useful information before anyone asks. But even a relevant remark can be disruptive if it lands at the wrong moment. A new preprint on arXiv takes up this specific problem, asking not what an agent should say but when it should say it.
The authors frame the issue as a timing question and propose a benchmark for studying it in multi-party chat settings. By isolating timing from other factors like relevance or wording, the work aims to make it possible to systematically evaluate how well an agent decides to interject. The abstract does not yet report results, but the stated goal is to provide a foundation for building agents that contribute at moments that help rather than hinder group work.
As a single preprint, the paper offers a research direction rather than a settled answer. Its contribution at this stage is to define the problem clearly and offer a way to measure progress on it—an important step for anyone designing AI assistants meant to participate in collaborative conversations.