Read the Evidence Before Seeing the Answer: Hypothesis-Independent Evidence Reading for Detecting Confident Hallucinations

Evidence-based fact verification normally asks a leading question: the candidate answer is placed in the verifier's context and the model is asked whether the evidence supports it. We study the confident, self-consistent errors on which such verification is most needed and least trusted. Auditing 9,762 of them across eight benchmarks shows a benchmark-dependent mixture of genuine hallucinations, ambiguous questions and wrong gold labels; on the genuine part the best internal signal we tried reaches 0.69 AUROC, versus 0.90 or more once the same model reads a passage, so evidence is needed. Candidate-conditioned verification has a measurable cost: holding question, evidence and verifier fixed and varying only whether the candidate is the verifier's own output, a model judges its own wrong answer more leniently than another model's equally wrong answer (19,718 matched pairs over five benchmarks, $p<10^{-5}$), increasingly so with its confidence, and not at all when neither candidate is its own. We therefore make the evidence-interpretation stage hypothesis-independent: a reader answers the question from the evidence before seeing the candidate, $R(x,e)\to \hat{a}$, and an external matcher compares $\hat{a}$ with the candidate, while the calibrated candidate-conditioned score is retained as a base term. This improves detection on every benchmark tested ($+0.016$ AUROC with benchmarks as units, $p=0.015$), by a similar margin whether or not the verifier produced the answer: what helps is reading the evidence first, not the removal of the ownership bias that motivated it. Routing only the questions that internal signals cannot separate matches the full-retrieval residual error rate at 39% retrieval coverage.