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The cited work could not be located through any authoritative source.","detected_doi":"10.5555/3766078.3766274","detected_arxiv_id":null,"ref_index":109,"audited_at":"2026-05-19T22:57:54.994142Z"},{"detector":"doi_compliance","finding_type":"unresolvable_identifier","severity":"critical","verdict_class":"cross_source","note":"Identifier '10.18653/v1/2025.findingsacl.1059' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. 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The cited work could not be located through any authoritative source.","snippet":"Wu D., Xia S.-T. and Wang Y . (2020). Adversarial weight perturbation helps robust generalization. Adv. Neural Inf. Process. Syst. 33:2958-2969. DOI:10.5555/3495724","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.5555/3495724","arxiv_id":null,"ref_index":84,"raw_excerpt":"Wu D., Xia S.-T. and Wang Y . (2020). Adversarial weight perturbation helps robust generalization. Adv. Neural Inf. Process. Syst. 33:2958-2969. 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The cited work could not be located through any authoritative source.","snippet":"Shafahi A., Huang W.R., Najibi M., et al. (2018). Poison frogs! Targeted clean-label poisoning attacks on neural networks. Adv. Neural Inf. Process. Syst. 31:6106-6116. DOI:10.5555/3327345.3327509","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.5555/3327345.3327509","arxiv_id":null,"ref_index":78,"raw_excerpt":"Shafahi A., Huang W.R., Najibi M., et al. (2018). Poison frogs! Targeted clean-label poisoning attacks on neural networks. Adv. Neural Inf. Process. Syst. 31:6106-6116. 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The cited work could not be located through any authoritative source.","snippet":"Gu T., Dolan-Gavitt B. and Garg S. (2017). Badnets: Iden- tifying vulnerabilities in the machine learning model supply chain. arXiv preprint. DOI:10.48550/arXiv.1708","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.48550/arxiv.1708","arxiv_id":null,"ref_index":76,"raw_excerpt":"Gu T., Dolan-Gavitt B. and Garg S. (2017). Badnets: Iden- tifying vulnerabilities in the machine learning model supply chain. arXiv preprint. 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The cited work could not be located through any authoritative source.","snippet":"Biggio B., Nelson B. and Laskov P . (2012). Poisoning attacks against support vector machines. arXiv preprint. DOI:10.5555/3042573.3042761","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.5555/3042573.3042761","arxiv_id":null,"ref_index":74,"raw_excerpt":"Biggio B., Nelson B. and Laskov P . (2012). Poisoning attacks against support vector machines. arXiv preprint. 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The cited work could not be located through any authoritative source.","snippet":"Tramèr F ., Zhang F ., Juels A., et al. (2016). Stealing machine learning models via prediction APIs. Proc. USENIX Secur. Symp. 2016:601-618. DOI:10.5555/3241094","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.5555/3241094","arxiv_id":null,"ref_index":71,"raw_excerpt":"Tramèr F ., Zhang F ., Juels A., et al. (2016). Stealing machine learning models via prediction APIs. Proc. USENIX Secur. Symp. 2016:601-618. 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The cited work could not be located through any authoritative source.","snippet":"Gao Z., Brantley K. and Joachims T. (2024). Reviewer2: Optimizing review generation through prompt generation. arXiv preprint. DOI:10.48550/arXiv.2402","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.48550/arxiv.2402","arxiv_id":null,"ref_index":29,"raw_excerpt":"Gao Z., Brantley K. and Joachims T. (2024). Reviewer2: Optimizing review generation through prompt generation. arXiv preprint. 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The cited work could not be located through any authoritative source.","snippet":"Liu R. and Shah N.B. (2023). Reviewergpt? An exploratory study on using large language models for paper reviewing. arXiv preprint. DOI:10.48550/arXiv.2306","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.48550/arxiv.2306","arxiv_id":null,"ref_index":28,"raw_excerpt":"Liu R. and Shah N.B. (2023). Reviewergpt? An exploratory study on using large language models for paper reviewing. arXiv preprint. 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The cited work could not be located through any authoritative source.","snippet":"Leyton-Brown K., Mausam., Nandwani Y ., et al. (2024). Matching papers and reviewers at large conferences. Ar- tif. Intell. 331:104119. DOI:10.1016/j.artint.2024","arxiv_id":"2604.23593","detector":"doi_compliance","evidence":{"doi":"10.1016/j.artint.2024","arxiv_id":null,"ref_index":27,"raw_excerpt":"Leyton-Brown K., Mausam., Nandwani Y ., et al. (2024). Matching papers and reviewers at large conferences. Ar- tif. Intell. 331:104119. 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