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CiteAnchor: Source-Aware Grounding for Hallucinated Citation Detection and Resolution

A new arXiv paper (2610.30355v1) introduces CiteAnchor, a source-aware grounding framework for detecting hallucinated citations, and CiteResolve, a benchmark of 423 real-world hallucinated citations annotated for source recoverability. CiteAnchor reaches 0.841 F1 with GPT-5.6 Luna and 0.784 with an open-weight backbone on an existing detection benchmark, beating baselines with the same backbones by 7.9 and 36.8 points, and achieves 74.7% and 70.7% end-to-end task success on CiteResolve, outperforming web search baselines by 16.3 and 65.3 percentage points. The authors released code and data at github.com/oldwizard7/CiteANCHOR_code.

by read1 min views3 publishedOct 23, 2026

arXiv:2610.30355v1 Announce Type: new Abstract: Hallucinated citations make it harder to trace sources and verify the evidence behind scientific claims. Detecting them is difficult because bibliographic records can be incomplete or differ across source types and publication versions. A plausible match alone neither validates the citation nor establishes its underlying source. We introduce CiteResolve, a benchmark of 423 real-world hallucinated citations annotated for source recoverability. We further propose CiteAnchor, a source-aware grounding framework that adapts retrieval and field-level verification to each source type and separately determines citation validity and whether a unique source can be recovered. On an existing detection benchmark, CiteAnchor achieves 0.841 F1 with GPT-5.6 Luna and 0.784 with an open-weight backbone, outperforming the baseline with the same backbones by 7.9 and 36.8 points. On CiteResolve, it achieves 74.7% and 70.7% end-to-end task success, outperforming the corresponding web search baselines by 16.3 and 65.3 percentage points. Our code and data are available at https://github.com/oldwizard7/CiteANCHOR_code.

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