{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:10:31Z","timestamp":1783930231865,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234318","type":"print"},{"value":"9789819234325","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3432-5_34","type":"book-chapter","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T07:41:39Z","timestamp":1783928499000},"page":"421-432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["StructRank: Two-Stage Structured Decision Learning for Efficient Listwise Document Reranking"],"prefix":"10.1007","author":[{"given":"Jie","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanwen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"34_CR1","unstructured":"Dai, M., Yang, C., Si, Q.: S-GRPO: early exit via reinforcement learning in reasoning models. arXiv:2505.07686 (2025)"},{"key":"34_CR2","unstructured":"DeepSeek-AI, et al.: DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning. arXiv:2501.12948 (2025)"},{"key":"34_CR3","unstructured":"Izacard, G., et al.: Unsupervised dense information retrieval with contrastive learning. Trans. Mach. Learn. Res. (2022)"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, T., Liu, Y., Chen, X.: GRPO-LEAD: a difficulty-aware reinforcement learning approach for concise mathematical reasoning in language models. arXiv:2504.09696 (2025)","DOI":"10.18653\/v1\/2025.emnlp-main.287"},{"key":"34_CR5","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"key":"34_CR6","unstructured":"Jiang, A.Q., et al.: Mistral 7B. arXiv:2310.06825 (2023)"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: ProRank: prompt warmup via reinforcement learning for small language models reranking. arXiv:2506.03487 (2025)","DOI":"10.18653\/v1\/2026.findings-acl.51"},{"key":"34_CR8","unstructured":"Ma, X., Zhang, X., Pradeep, R., Lin, J.: Zero-shot listwise document reranking with a large language model. arXiv:2305.02156 (2023)"},{"key":"34_CR9","unstructured":"Nguyen, T., et al.: MS MARCO: a human generated machine reading comprehension dataset. arXiv:1611.09268 (2016)"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Nogueira, R., Jiang, Z., Pradeep, R., Lin, J.: Document ranking with a pretrained sequence-to-sequence model. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 708\u2013718. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.63"},{"key":"34_CR11","unstructured":"OpenAI: Introducing structured outputs in the API. OpenAI Blog, 6 August 2024"},{"key":"34_CR12","unstructured":"Pradeep, R., Sharifymoghaddam, S., Lin, J.: RankVicuna: zero-shot listwise document reranking with open-source large language models. arXiv:2309.15088 (2023)"},{"key":"34_CR13","unstructured":"Pradeep, R., Sharifymoghaddam, S., Lin, J.: RankZephyr: effective and robust zero-shot listwise reranking is a breeze! arXiv:2312.02724 (2023)"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Rasley, J., Rajbhandari, S., Ruwase, O., He, Y.: DeepSpeed: system optimizations enable training deep learning models with over 100 billion parameters. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3505\u20133506. ACM (2020)","DOI":"10.1145\/3394486.3406703"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Reddy, R.G., et al.: FIRST: faster improved listwise reranking with single token decoding. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 8642\u20138652. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.491"},{"key":"34_CR16","unstructured":"Shao, Z., et al.: DeepSeekMath: pushing the limits of mathematical reasoning in open language models. arXiv:2402.03300 (2024)"},{"key":"34_CR17","unstructured":"Thakur, N., Reimers, N., R\u00fcckl\u00e9, A., Srivastava, A., Gurevych, I.: BEIR: a heterogeneous benchmark for zero-shot evaluation of information retrieval models. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2021)"},{"key":"34_CR18","unstructured":"Tunstall, L., et al.: Zephyr: direct distillation of LM alignment. arXiv:2310.16944 (2023)"},{"key":"34_CR19","unstructured":"vLLM Team: Structured outputs. vLLM documentation, v0.8.5 (2025)"},{"key":"34_CR20","unstructured":"Cai, Y., et al.: Training-free group relative policy optimization. arXiv:2510.08191 (2025)"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Lyu, Q., et al.: Calibrating large language models with sample consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence (2025)","DOI":"10.1609\/aaai.v39i18.34120"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Zhuang, H., et al.: RankT5: fine-tuning T5 for text ranking with ranking losses. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2308\u20132313. ACM (2023)","DOI":"10.1145\/3539618.3592047"},{"key":"34_CR23","unstructured":"Jain, N., et al.: NEFTune: noisy embeddings improve instruction finetuning. arXiv:2310.05914 (2023)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3432-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T07:41:41Z","timestamp":1783928501000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3432-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"ISBN":["9789819234318","9789819234325"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3432-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]},"assertion":[{"value":"14 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}