pith. sign in

Recoverable Identifier

arXiv:2604.26258 · detector doi_compliance · incontrovertible · 2026-05-19 20:22:53.939448+00:00

advisory doi_compliance recoverable_identifier

DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2023.emnlp-main.494.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

PMLR, 2018. Pryzant, R., Iter, D., Li, J., Lee, Y ., Zhu, C., and Zeng, M. Automatic prompt optimization with “gradient de- scent” and beam search. In Bouamor, H., Pino, J., and Bali, K. (eds.),Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 7957–7968, Singapore, December 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023. emnlp-main.494. URL https://aclanthology. org/2023.emnlp-main.494/. Pyatkin, V ., Malik, S., Graf, V ., Ivison, H., Huang, S., Dasigi, P., Lambert, N., and Hajishirzi, H. Generaliz- ing verifiable instruction following, 2025. URL https: //arxiv.org/abs/2507.02833. Schick, T., Dwivedi-Yu, J., Dess`ı, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T. Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551, 2023. Tang, J., Fan, T., and Huang, C. Autoagent: A fully- automated and zero-code framework for llm agents.arXiv preprint arXiv:2502.05957, 2025. Yang, C., Wang, X., Lu, Y ., Liu, H., Le, Q. V ., Zhou, D., and Chen, X. Large language models as optimizers. In The Twelfth International Conference on Learning Repre- sentations, 2023. Yang, Z., Qi, P., Zhang, S., Bengio, Y ., Cohen, W., Salakhutdinov, R., and Manning, C. D. HotpotQA: A dataset for diverse, explainable multi-hop question an- swering. In Riloff, E., Chiang, D., Hockenmaier, J., 10 FLOWBOT: Inducing LLM Workflows with Bileve

Evidence payload

{
  "printed_excerpt": "PMLR, 2018. Pryzant, R., Iter, D., Li, J., Lee, Y ., Zhu, C., and Zeng, M. Automatic prompt optimization with \u201cgradient de- scent\u201d and beam search. In Bouamor, H., Pino, J., and Bali, K. (eds.),Proceedings of the 2023 Conference on Empirica",
  "reconstructed_doi": "10.18653/v1/2023.emnlp-main.494.URL",
  "ref_index": 5,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
}