Recoverable Identifier

arXiv:2604.24104 · detector doi_compliance · incontrovertible · 2026-05-19 22:25:11.715282+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.24963/ijcai.2021/630.Survey) was visible in the surrounding text but could not be confirmed against doi.org as printed.

Paper page Integrity report arXiv Try DOI

Evidence text

URL https://doi.org/10.24963/ijcai. 2021/630. Survey Track. Wang, S., Zhang, C., and Zhang, N. Mgsa: Multi-granularity graph structure attention for knowledge graph-to-text gen- eration, 2024a. URL https://arxiv.org/abs/ 2409.10294. Wang, X., Zheng, Z., Ye, F., Xue, D., Huang, S., and Gu, Q. Diffusion language models are versatile protein learners. InProceedings of the 41st International Conference on Machine Learning, ICML’24. JMLR.org, 2024b. Wang, Y ., Zhu, Y ., Zhang, W., Zhuang, Y ., Li, Y ., and Tang, S. Bridging local details and global con- text in text-attributed graphs. In Al-Onaizan, Y ., Bansal, M., and Chen, Y .-N. (eds.),Proceedings of the 2024 Conference on Empirical Methods in Natu- ral Language Processing, pp. 14830–14841, Miami, Florida, USA, November 2024c. Association for Compu- tational Linguistics. doi: 10.18653/v1/2024.emnlp-main

Evidence payload

{
  "printed_excerpt": "URL https://doi.org/10.24963/ijcai. 2021/630. Survey Track. Wang, S., Zhang, C., and Zhang, N. Mgsa: Multi-granularity graph structure attention for knowledge graph-to-text gen- eration, 2024a. URL https://arxiv.org/abs/ 2409.10294. Wang, X",
  "reconstructed_doi": "10.24963/ijcai.2021/630.Survey",
  "ref_index": 18,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}