Recoverable Identifier
advisory
doi_compliance
recoverable_identifier
DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2024.emnlp-main.983.URL) 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://openreview.net/forum?id=xXTkbTBmqq. T. Nakamura, T. Akiba, K. Fujii, Y. Oda, R. Yokota, and J. Suzuki. Drop-upcycling: Training sparse mixture of experts with partial re-initialization. InThe Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=gx1wHnf5Vp. D. Nathawani, I. Gitman, S. Majumdar, E. Bakhturina, A. Sunil Mahabaleshwarkar, , J. Zhang, and J. Polak Scowcroft. Nemotron-Post-Training-Dataset-v1, July 2025. URL https://huggingface.co/ datasets/nvidia/Nemotron-Post-Training-Dataset-v1. H. H. Nigatu, A. L. Tonja, B. Rosman, T. Solorio, and M. Choudhury. The Zeno’s paradox of ‘low- resource’ languages. In Y. Al-Onaizan, M. Bansal, and Y.-N. Chen, editors,Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17753–17774, Miami, Florida, USA, Nov. 2024. Association for Computational Linguistics. doi: 10.18653/v1/2024. emnlp-main.983. URL https://aclanthology.org/2024.emnlp-main.983/. NVIDIA. Nemotron 3 Nano: Open, efficient mixture-of-experts hybrid Mamba-Transformer model for Agentic reasoning, 2025. URL https://research.nvidia.com/labs/nemotron/files/ NVIDIA-Nemotron-3-Nano-Technical-Report.pdf. Technical report. NVIDIA et al. Nemotron-h: A family of accurate and efficient hybrid mamba-transformer models, 2025a. URL https://arxiv.org/abs/2504.03624. NVIDIA et al. Nvidia nemotron nano 2: An accurate and efficient hybrid mamba-transformer reasoning model, 2025b. URL ht
Evidence payload
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"reconstructed_doi": "10.18653/v1/2024.emnlp-main.983.URL",
"ref_index": 6,
"resolved_title": null,
"verdict_class": "incontrovertible"
}