Researchers from the University of Maryland, Google DeepMind and Simon Fraser University have posted an arXiv preprint, IdeaLens: Detecting AI Ideas in Long-form Writing, describing a detector that converts an article into an outline and judges idea provenance rather than prose style. The team trained on about one million English long-form texts using Pangram 3.3.2 labels, fine-tuned Nvidia's Nemotron-3.5-Lightning-30B-A3B with LoRA, and built a prose-reading control model called ProseLens. In the authors' reported tests, IdeaLens flagged 6.8% of articles as AI when AI wrote from a complete human outline, down from 94.9% with only a topic prompt; when humans rewrote stories from AI-generated outlines, it judged 68% as AI versus 8% for Pangram 4. The authors caution that training labels came from an existing detector, the TwiceTold set had only 50 human rewrites, and the system cannot yet apportion credit in multi-turn human-AI co-creation.