Stanford 论文《Worse Together》研究多用户多智能体场景,覆盖 5 个前沿模型、77 个场景和 4 个环境,发现每用户一个智能体的团队在共享资源上表现差于单个服务所有人的协调智能体。
New Stanford paper finds that when each person's agent acts alone on a shared resource, the group does worse than 1 agent serving everyone.
A shared budget or calendar is handled better by 1 agent serving everyone than by 1 agent per user, across 5 frontier models.
Each agent does a sensible job for its own user.
Together they overwrite each other, stall as the team grows, and with no channel they collapsed outright in 2 environments.
On a contested token budget, Opus 5 teams captured 30% of the achievable value against 64% for 1 coordinating agent.
Agents invented facts about other users in more than half of Claude team episodes in the group-ordering environment.
Prefer 1 agent holding everyone's constraints, and if you run 1 per user, make reading peers a condition of committing.
来源:Rohan Paul · x.com