{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:14:30Z","timestamp":1774318470866,"version":"3.50.1"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032212887","type":"print"},{"value":"9783032212894","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-21289-4_18","type":"book-chapter","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:05:57Z","timestamp":1774314357000},"page":"272-288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FACTUM: Mechanistic Detection of\u00a0Citation Hallucination in\u00a0Long-Form RAG"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1046-5416","authenticated-orcid":false,"given":"Maxime","family":"Dassen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1337-4919","authenticated-orcid":false,"given":"Rebecca","family":"Kotula","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5628-1003","authenticated-orcid":false,"given":"Kenton","family":"Murray","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5970-880X","authenticated-orcid":false,"given":"Andrew","family":"Yates","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7347-7086","authenticated-orcid":false,"given":"Dawn","family":"Lawrie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2357-9807","authenticated-orcid":false,"given":"Efsun","family":"Kayi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3866-3013","authenticated-orcid":false,"given":"James","family":"Mayfield","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8107-4383","authenticated-orcid":false,"given":"Kevin","family":"Duh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal, A., Suzgun, M., Mackey, L., Kalai, A.: Do language models know when they\u2019re hallucinating references? In: Graham, Y., Purver, M. (eds.) Findings of the Association for Computational Linguistics: EACL 2024, pp. 912\u2013928. Association for Computational Linguistics, St. Julian\u2019s (2024). https:\/\/aclanthology.org\/2024.findings-eacl.62\/","DOI":"10.18653\/v1\/2024.findings-eacl.62"},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"Ayala, O., Bechard, P.: Reducing hallucination in structured outputs via retrieval-augmented generation. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pp. 228\u2013238. Association for Computational Linguistics (2024). https:\/\/doi.org\/10.18653\/v1\/2024.naacl-industry.19, http:\/\/dx.doi.org\/10.18653\/v1\/2024.naacl-industry.19","DOI":"10.18653\/v1\/2024.naacl-industry.19"},{"key":"18_CR3","unstructured":"Barbero, F., et al.: Why do LLMs attend to the first token? (2025). https:\/\/arxiv.org\/abs\/2504.02732"},{"key":"18_CR4","unstructured":"Barbero, F., et al.: Transformers need glasses! Information over-squashing in language tasks (2024)"},{"key":"18_CR5","unstructured":"Belrose, N., et al.: Eliciting latent predictions from transformers with the tuned lens (2023). https:\/\/arxiv.org\/abs\/2303.08112"},{"key":"18_CR6","doi-asserted-by":"publisher","unstructured":"Liu, N.F., et al.: Lost in the Middle: how language models use long contexts. Trans. Assoc. Comput. Linguist. 12, 157\u2013173 (2024). https:\/\/doi.org\/10.1162\/tacl_a_00638","DOI":"10.1162\/tacl_a_00638"},{"key":"18_CR7","doi-asserted-by":"publisher","unstructured":"Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., Wei, F.: Knowledge neurons in pretrained transformers. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8493\u20138502. Association for Computational Linguistics, Dublin (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.581, https:\/\/aclanthology.org\/2022.acl-long.581\/","DOI":"10.18653\/v1\/2022.acl-long.581"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Ding, Y., et al.: Citations and trust in LLM generated responses (2025)","DOI":"10.1609\/aaai.v39i22.34550"},{"key":"18_CR9","unstructured":"Elhage, N., et al.: A mathematical framework for transformer circuits. Transformer Circuits Thread (2021). https:\/\/transformer-circuits.pub\/2021\/framework\/index.html"},{"key":"18_CR10","doi-asserted-by":"publisher","unstructured":"Es, S., James, J., Espinosa\u00a0Anke, L., Schockaert, S.: RAGAs: automated evaluation of retrieval augmented generation. In: Aletras, N., De\u00a0Clercq, O. (eds.) Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pp. 150\u2013158. Association for Computational Linguistics, St. Julians (2024). https:\/\/doi.org\/10.18653\/v1\/2024.eacl-demo.16, https:\/\/aclanthology.org\/2024.eacl-demo.16\/","DOI":"10.18653\/v1\/2024.eacl-demo.16"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Gal\u00e1n-Sales, F.J., Reina-Jim\u00e9nez, P., Carranza-Garc\u00eda, M., Luna-Romera, J.M.: An approach to enhance time series forecasting by fast Fourier transform. In: International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 259\u2013268. Springer (2023)","DOI":"10.1007\/978-3-031-42529-5_25"},{"key":"18_CR12","doi-asserted-by":"publisher","unstructured":"Geva, M., Schuster, R., Berant, J., Levy, O.: Transformer feed-forward layers are key-value memories. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.T. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 5484\u20135495. Association for Computational Linguistics, Online and Punta Cana (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.446, https:\/\/aclanthology.org\/2021.emnlp-main.446\/","DOI":"10.18653\/v1\/2021.emnlp-main.446"},{"key":"18_CR13","unstructured":"Gu, X., et al.: When attention sink emerges in language models: an empirical view (2024)"},{"key":"18_CR14","unstructured":"Huang, P., et al.: ParamMute: suppressing knowledge-critical FFNs for faithful retrieval-augmented generation (2025). https:\/\/arxiv.org\/abs\/2502.15543"},{"key":"18_CR15","unstructured":"Huang, Y., et al.: RePPL: recalibrating perplexity by uncertainty in semantic propagation and language generation for explainable QA hallucination detection (2025). https:\/\/arxiv.org\/abs\/2505.15386"},{"key":"18_CR16","doi-asserted-by":"publisher","unstructured":"Hussain, M.S., Zaki, M.J., Subramanian, D.: The information pathways hypothesis: transformers are dynamic self-ensembles. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, pp. 810\u2013821. Association for Computing Machinery, New York (2023).https:\/\/doi.org\/10.1145\/3580305.3599520","DOI":"10.1145\/3580305.3599520"},{"key":"18_CR17","doi-asserted-by":"publisher","unstructured":"Ji, Z., et al.: LLM internal states reveal hallucination risk faced with a query. In: Belinkov, Y., Kim, N., Jumelet, J., Mohebbi, H., Mueller, A., Chen, H. (eds.) Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 88\u2013104. Association for Computational Linguistics, Miami (2024). https:\/\/doi.org\/10.18653\/v1\/2024.blackboxnlp-1.6, https:\/\/aclanthology.org\/2024.blackboxnlp-1.6\/","DOI":"10.18653\/v1\/2024.blackboxnlp-1.6"},{"key":"18_CR18","doi-asserted-by":"publisher","unstructured":"Kadavath, S., et al.: Language models (mostly) know what they know. CoRR abs\/2207.05221 (2022). https:\/\/doi.org\/10.48550\/arXiv.2207.05221","DOI":"10.48550\/arXiv.2207.05221"},{"key":"18_CR19","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf"},{"key":"18_CR20","unstructured":"\u00c1d\u00e1m Kov\u00e1cs, Recski, G.: LettuceDetect: a hallucination detection framework for RAG applications (2025). https:\/\/arxiv.org\/abs\/2502.17125"},{"key":"18_CR21","unstructured":"Lawrie, D., et al.: Overview of the TREC 2024 neuclir track. arXiv preprint arXiv:2509.14355 (2025)"},{"key":"18_CR22","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"18_CR23","doi-asserted-by":"publisher","unstructured":"Li, J., Yang, B., Dou, Z.Y., Wang, X., Lyu, M.R., Tu, Z.: Information aggregation for multi-head attention with routing-by-agreement. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 3566\u20133575. Association for Computational Linguistics, Minneapolis (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1359, https:\/\/aclanthology.org\/N19-1359\/","DOI":"10.18653\/v1\/N19-1359"},{"key":"18_CR24","unstructured":"Li, T., Zhang, G., Do, Q.D., Yue, X., Chen, W.: Long-context LLMs struggle with long in-context learning. Trans. Mach. Learn. Res. (2025). https:\/\/openreview.net\/forum?id=Cw2xlg0e46"},{"key":"18_CR25","doi-asserted-by":"publisher","unstructured":"Li, X., Li, S., Song, S., Yang, J., Ma, J., Yu, J.: PMet: precise model editing in a transformer (2024). https:\/\/doi.org\/10.1609\/aaai.v38i17.29818, https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/29818","DOI":"10.1609\/aaai.v38i17.29818"},{"key":"18_CR26","unstructured":"Li, X., et al.: RAG-DDR: optimizing retrieval-augmented generation using differentiable data rewards. In: The Thirteenth International Conference on Learning Representations (2025). https:\/\/openreview.net\/forum?id=Pnktu2PBXD"},{"key":"18_CR27","unstructured":"Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 21464\u201321475. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/f5496252609c43eb8a3d147ab9b9c006-Paper.pdf"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Lou, Y., Caruana, R., Gehrke, J., Hooker, G.: Accurate intelligible models with pairwise interactions (2013)","DOI":"10.1145\/2487575.2487579"},{"key":"18_CR29","unstructured":"Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C.D., Ho, D.E.: Hallucination-free? Assessing the reliability of leading ai legal research tools (2025)"},{"key":"18_CR30","unstructured":"Mak, B., Flanigan, J.: Residual matrix transformers: scaling the size of the residual stream. In: Forty-second International Conference on Machine Learning (2025). https:\/\/openreview.net\/forum?id=RR05ym7DNF"},{"key":"18_CR31","unstructured":"Malinin, A., Gales, M.: Uncertainty estimation in autoregressive structured prediction (2021). https:\/\/openreview.net\/forum?id=jN5y-zb5Q7m"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Manakul, P., Liusie, A., Gales, M.: SelfcheckGPT: Zero-resource black-box hallucination detection for generative large language models (2023). https:\/\/openreview.net\/forum?id=RwzFNbJ3Ez","DOI":"10.18653\/v1\/2023.emnlp-main.557"},{"key":"18_CR33","doi-asserted-by":"publisher","unstructured":"Mayfield, J., et al.: On the evaluation of machine-generated reports. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, pp. 1904\u20131915. Association for Computing Machinery, New York (2024). https:\/\/doi.org\/10.1145\/3626772.3657846","DOI":"10.1145\/3626772.3657846"},{"key":"18_CR34","unstructured":"Meng, K., Bau, D., Andonian, A., Belinkov, Y.: Locating and editing factual associations in GPT. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 17359\u201317372. Curran Associates, Inc. (2022). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/6f1d43d5a82a37e89b0665b33bf3a182-Paper-Conference.pdf"},{"key":"18_CR35","unstructured":"Meng, K., Sharma, A.S., Andonian, A.J., Belinkov, Y., Bau, D.: Mass-editing memory in a transformer. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=MkbcAHIYgyS"},{"key":"18_CR36","unstructured":"Miroyan, M., et al.: Search arena: analyzing search-augmented LLMs (2025). https:\/\/arxiv.org\/abs\/2506.05334"},{"key":"18_CR37","doi-asserted-by":"crossref","unstructured":"Ni, S., Bi, K., Guo, J., Yu, L., Bi, B., Cheng, X.: Towards fully exploiting LLM internal states to enhance knowledge boundary perception (2025). https:\/\/arxiv.org\/abs\/2502.11677","DOI":"10.18653\/v1\/2025.acl-long.1184"},{"key":"18_CR38","doi-asserted-by":"publisher","unstructured":"Niu, C., et al.: RAGTruth: a hallucination corpus for developing trustworthy retrieval-augmented language models. In: Ku, L.W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 10862\u201310878. Association for Computational Linguistics, Bangkok (2024). https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.585, https:\/\/aclanthology.org\/2024.acl-long.585\/","DOI":"10.18653\/v1\/2024.acl-long.585"},{"key":"18_CR39","unstructured":"Orgad, H., et al.: LLMs know more than they show: on the intrinsic representation of LLM hallucinations (2025). https:\/\/arxiv.org\/abs\/2410.02707"},{"key":"18_CR40","unstructured":"Ravi, S.S., Mielczarek, B., Kannappan, A., Kiela, D., Qian, R.: Lynx: an open source hallucination evaluation model (2024). https:\/\/arxiv.org\/abs\/2407.08488"},{"key":"18_CR41","doi-asserted-by":"publisher","unstructured":"Saad-Falcon, J., Khattab, O., Potts, C., Zaharia, M.: ARES: an automated evaluation framework for retrieval-augmented generation systems (2024). https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.20, https:\/\/aclanthology.org\/2024.naacl-long.20\/","DOI":"10.18653\/v1\/2024.naacl-long.20"},{"key":"18_CR42","doi-asserted-by":"crossref","unstructured":"Shai, A.S., Marzen, S.E., Teixeira, L., Oldenziel, A.G., Riechers, P.M.: Transformers represent belief state geometry in their residual stream. In: Globerson, A., et al. (eds.) Advances in Neural Information Processing Systems, vol.\u00a037, pp. 75012\u201375034. Curran Associates, Inc. (2024). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/8936fa1691764912d9519e1b5673ea66-Paper-Conference.pdf","DOI":"10.52202\/079017-2387"},{"key":"18_CR43","doi-asserted-by":"crossref","unstructured":"Shakespeare, W.: The Sonnets. Thomas Thorpe, London (1609)","DOI":"10.1093\/oseo\/instance.00006245"},{"key":"18_CR44","doi-asserted-by":"publisher","unstructured":"Song, J., et al.: RAG-HAT: a hallucination-aware tuning pipeline for LLM in retrieval-augmented generation. In: Dernoncourt, F., Preo\u0163iuc-Pietro, D., Shimorina, A. (eds.) Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pp. 1548\u20131558. Association for Computational Linguistics, Miami (2024). https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-industry.113, https:\/\/aclanthology.org\/2024.emnlp-industry.113\/","DOI":"10.18653\/v1\/2024.emnlp-industry.113"},{"key":"18_CR45","doi-asserted-by":"crossref","unstructured":"Sriramanan, G., Bharti, S., Sadasivan, V.S., Saha, S., Kattakinda, P., Feizi, S.: LLM-check: investigating detection of hallucinations in large language models. In: Globerson, A., et al. (eds.) Advances in Neural Information Processing Systems, vol.\u00a037, pp. 34188\u201334216. Curran Associates, Inc. (2024). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/3c1e1fdf305195cd620c118aaa9717ad-Paper-Conference.pdf","DOI":"10.52202\/079017-1077"},{"key":"18_CR46","doi-asserted-by":"crossref","unstructured":"Su, W., et al.: Unsupervised real-time hallucination detection based on the internal states of large language models (2024)","DOI":"10.18653\/v1\/2024.findings-acl.854"},{"key":"18_CR47","unstructured":"Sun, Z., et al.: ReDeEP: detecting hallucination in retrieval-augmented generation via mechanistic interpretability (2025)"},{"key":"18_CR48","unstructured":"Tan, Y., He, S., Liao, H., Zhao, J., Liu, K.: Dynamic parametric retrieval augmented generation for test-time knowledge enhancement (2025). https:\/\/arxiv.org\/abs\/2503.23895"},{"key":"18_CR49","unstructured":"Teller, V.: Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. MIT Press One Rogers Street, Cambridge 02142-1209, USA journals-info\u00a0... (2000)"},{"key":"18_CR50","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"18_CR51","doi-asserted-by":"publisher","unstructured":"Wallat, J., Heuss, M., Rijke, M.D., Anand, A.: Correctness is not faithfulness in retrieval augmented generation attributions. In: Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR), ICTIR 2025, pp. 22\u201332. Association for Computing Machinery, New York (2025). https:\/\/doi.org\/10.1145\/3731120.3744592","DOI":"10.1145\/3731120.3744592"},{"key":"18_CR52","unstructured":"Wu, Y., et al.: RAGTruth: a hallucination corpus for developing trustworthy retrieval-augmented language models (2024)"},{"key":"18_CR53","doi-asserted-by":"publisher","unstructured":"Xu, M., Gan, Q., Zhu, Z., Qin, H.: Logprobs know uncertainty: fighting LLM hallucinations. In: Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering, FSE Companion 2025, pp. 1242\u20131243. Association for Computing Machinery, New York (2025). https:\/\/doi.org\/10.1145\/3696630.3731433","DOI":"10.1145\/3696630.3731433"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-21289-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:06:04Z","timestamp":1774314364000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21289-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032212887","9783032212894"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21289-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"25 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delft","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","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":"29 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2026.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}