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In medical, legal, and scientific applications this failure causes direct harm, and detecting it from internal model states offers a path to safer deployment. A growing body of work reports that this problem is increasingly tractable, with recent methods achieving high detection performance on widely used benchmarks. We show, however, that much of this apparent progress does not survive scrutiny. Four of the six corpora embed the ground-truth answer directly in the input prompt. A na\\\"{i}ve text-similarity baseline we call \\textsc{TxTemb} exploits this to achieve near-perfect detection scores without any access to model internals. To measure what genuine detection capability remains once these artifacts are controlled, we conduct a large-scale evaluation spanning twenty-two detection methods, twelve open-source models","title":"PARALLAX: Separating Genuine Hallucination Detection from Benchmark Construction Artifacts","url":"https://arxiv.org/abs/2605.17028","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.17028v1 Announce Type: cross \nAbstract: Large language models (LLMs) hallucinate with confidence: their outputs can be fluent, authoritative, and simply wrong. In medical, legal, and scientific applications this failure causes direct harm, and detecting it from internal model states offers a path to safer deployment. A growing body of work reports that this problem is increasingly tractable, with recent methods achieving high detection performance on widely used benchmarks. We show, however, that much of this apparent progress does not survive scrutiny. Four of the six corpora embed the ground-truth answer directly in the input prompt. A na\\\"{i}ve text-similarity baseline we call \\textsc{TxTemb} exploits this to achieve near-perfect detection scores without any access to model internals. 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