Recoverable Identifier
advisory
doi_compliance
recoverable_identifier
DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.48550/arXiv.2306.12925.Sakshi) 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/10.48550/arXiv.2306. 12925. Sakshi, S., Tyagi, U., Kumar, S., Seth, A., Selvakumar, R., Nieto, O., Duraiswami, R., Ghosh, S., and Manocha, D. MMAU: A massive multi-task audio understanding and reasoning benchmark. InThe Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=TeVAZXr3yv. Santana, I. A. P., Pinhelli, F., Donini, J., Catharin, L., Man- golin, R. B., Feltrim, V . D., Domingues, M. A., et al. Music4all: A new music database and its applications. In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 399–404. IEEE, 2020. Shankar, N. B., Johnson, A., Chance, C., Veeramani, H., and Alwan, A. Coraal qa: A dataset and framework for open domain spontaneous speech question answering from long audio files. InICASSP, pp. 13371–13375, 2024. URL https://doi.org/10.1109/ICASSP48485.2024. 10447109. 11 MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio Singh, A., Fry, A., Perelman, A., Tart, A., Ganesh, A., El-Kishky, A., McLaughlin, A., Low, A., Ostrow, A., Ananthram, A., et al. Openai gpt-5 system card.arXiv preprint arXiv:2601.03267, 2025. Suri, H., Zhang, Q., Huo, W., Liu, Y ., and Guan, C. Me- diaqa: A question answering dataset on medical dia- logues.ArXiv, abs/2108.08074, 2021. URL https: //api.semanticscholar.org/CorpusID:237194607. Tang, C., Yu, W., Sun, G., Chen, X., Tan, T., Li, W., Lu, L., Ma, Z., and Zhang, C. Salmonn: Towards generic hearing
Evidence payload
{
"printed_excerpt": "URL https://doi.org/10.48550/arXiv.2306. 12925. Sakshi, S., Tyagi, U., Kumar, S., Seth, A., Selvakumar, R., Nieto, O., Duraiswami, R., Ghosh, S., and Manocha, D. MMAU: A massive multi-task audio understanding and reasoning benchmark. InThe ",
"reconstructed_doi": "10.48550/arXiv.2306.12925.Sakshi",
"ref_index": 3,
"resolved_title": null,
"verdict_class": "incontrovertible"
}