Stanford study finds frontier AI models struggle as teammates

A new benchmark shows top AI agents cave to pushy partners and keep flawed code when inheriting work from weaker models Frontier AI models can ace a solo exam and still flunk the group project. That is the core finding of a new Stanford University paper that puts 18 AI models through a battery of teamwork tests. The results suggest that the smartest agent in the room is not necessarily the best colleague. According to the study, top models often yield to pushy teammates and preserve cheating code written by weaker models. What the benchmark measured The paper is titled “Measuring AI Agents as Teammates with Collaborative Intelligence.” It introduces a new test suite called the Collaborative Intelligence Benchmark. It was posted to arXiv on November 2, 2026, under the identifier arXiv:2611.02633. The authors include Sean Wu, Aneesh Pappu, and Jiaru Zou, working with collaborators from the University of Oxford and MIT. The benchmark runs 18 models across 240 tasks. Those tasks span eight dimensions of teamwork, set in real-world scenarios. The settings include aviation, clinical work, coding, negotiations, and misconduct. The top performer was Gemini 4 Argon, which scored 82.2 out of...