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It does not present them as independently verified facts.","excerpt":"We introduce TeamGym, an environment for evaluating and training language model-based agents"},{"dimension":"summary","status":"uncertain","explanation":"The figures are faithful, but two gaps limit understanding. First, the training result depends on unexplained jargon: reinforcement learning on team decision loss, policies and policy-training. A general reader cannot tell what was trained or how. Second, the reading omits the authors' motivation that agents are trained and benchmarked in isolation. That framing explains why the findings matter. Smallest repair: briefly explain the training in plain terms, add the isolation point, and keep the authors' labels communication gap and retrieval gap. The question is also repeated inside the summary.","excerpt":"Yet AI agents are still primarily trained and benchmarked in isolation."},{"dimension":"date","status":"pass","explanation":"The originalPublished date of 2026-10-27 matches the arXiv v1 submission date. It is not the later feed date of 2026-10-29.","excerpt":"[Submitted on 27 Oct 2026]"},{"dimension":"support","status":"pass","explanation":"Each claim matches the abstract: the 22.9-point and 19.9-point drops, the scope of 8 models and 12 corpora, and the training results from two experiments. The limitation about transfer to real-world teams is framed as unknown rather than asserted. 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