as an example: a colleague ran a hypothesis-generation agent on a prompt about liver ultrasound correlations in large populations. it retrieved 18 papers, applied five “research facets,” and produced five hypotheses with titles, research questions, and experiment plans. the structure was impeccable. but the first hypothesis simply restated the input prompt. the third grabbed a zero-citation paper about skeletal muscle oxygenation and proposed “do this but for liver” with no consideration of whether that makes anatomical sense. the map of how science works was used to produce the appearance of science working. ↩
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From our provider analysis: Microsoft's Azure AI Foundry incorporated Mistral Document AI claiming 95.9% OCR precision. IBM's Docling achieved 37,000 GitHub endorsements with single-step retrieval. Cambrion introduced immediate processing completely bypassing OCR. If these assertions reflect controlled testing, the model environment is progressing. Meanwhile, professionals describe continuing efforts to locate one model that manages their specific documents.