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I Mutation-Tested 50 Python Libraries. Tests in the Young Ones Missed 41% of Planted Faults.
Agentic AIAcross 2,312 planted faults the study also surfaced four real library problems, from a Windows data-loss bug to README examples that do not work as written.
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I Mutation-Tested 50 Python Libraries. Tests in the Young Ones Missed 41% of Planted Faults.
Agentic AIAcross 2,312 planted faults the study also surfaced four real library problems, from a Windows data-loss bug to README examples that do not work as written.
Annotators marked each MNIST and Mukhoti digit yes, no, or unsure, producing image-level soft labels and uncertainty scores.
A simulated scar and data from 31 animals after ischemia show how an inhomogeneous estimator, random labelling, and brms separate tissue loss from real interaction.
Trained on six PathMNIST tissues with a small KEEP adapter, it handled added labels without retraining, though a linear classifier hit 88% on familiar ones.
Jev needs no training and at most three examples per intent; the open rebuild needs labels. Both still fall into the none-of-the-above trap.
A software tester ran six testing skills on Haiku, Sonnet, and Opus; pushy descriptions added only 5 points, and near-miss requests never fired a skill.
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