{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:15:40Z","timestamp":1783152940242,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["62172454"],"award-info":[{"award-number":["62172454"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023B1515020120"],"award-info":[{"award-number":["2023B1515020120"]}]},{"name":"Guangzhou Basic and Applied Basic Research Scheme","award":["2024A04J3517"],"award-info":[{"award-number":["2024A04J3517"]}]},{"name":"Academic Research Fund Tier 1","award":["FY2024"],"award-info":[{"award-number":["FY2024"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792337","type":"proceedings-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:54:39Z","timestamp":1775771679000},"page":"5334-5345","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Energy-Efficient and Dequantization-Free Quantization of LLMs: A Spiking Neural Network Approach to Salient Value Mitigation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2306-8275","authenticated-orcid":false,"given":"Chenyu","family":"Wang","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6255-4194","authenticated-orcid":false,"given":"Zhanglu","family":"Yan","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0987-9344","authenticated-orcid":false,"given":"Zhi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9943-6020","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4281-2053","authenticated-orcid":false,"given":"Weng-Fai","family":"Wong","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023."},{"key":"e_1_3_2_1_2_1","unstructured":"Albert Q. Jiang Alexandre Sablayrolles Arthur Mensch Chris Bamford Devendra Singh Chaplot Diego de las Casas Florian Bressand Gianna Lengyel Guillaume Lample Lucile Saulnier L\u00e9lio Renard Lavaud Marie-Anne Lachaux Pierre Stock Teven Le Scao Thibaut Lavril Thomas Wang Timoth\u00e9e Lacroix and William El Sayed. Mistral 7b. arXiv preprint arXiv:2310.06825 2023."},{"key":"e_1_3_2_1_3_1","volume-title":"et al. Webllm: A high-performance in-browser llm inference engine. arXiv preprint arXiv:2412.15803","author":"Ruan Charlie F","year":"2024","unstructured":"Charlie F Ruan, Yucheng Qin, Xun Zhou, Ruihang Lai, Hongyi Jin, Yixin Dong, Bohan Hou, Meng-Shiun Yu, Yiyan Zhai, Sudeep Agarwal, et al. Webllm: A high-performance in-browser llm inference engine. arXiv preprint arXiv:2412.15803, 2024."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599931"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.naacl-long.632"},{"key":"e_1_3_2_1_6_1","volume-title":"Transformer-lite: High-efficiency deployment of large language models on mobile phone gpus","author":"Li Luchang","year":"2024","unstructured":"Luchang Li, Sheng Qian, Jie Lu, Lunxi Yuan, Rui Wang, and Qin Xie. Transformer-lite: High-efficiency deployment of large language models on mobile phone gpus, 2024."},{"key":"e_1_3_2_1_7_1","volume-title":"The Thirteenth International Conference on Learning Representations","author":"An Yongqi","year":"2025","unstructured":"Yongqi An, Xu Zhao, Tao Yu, Ming Tang, and Jinqiao Wang. Systematic outliers in large language models. In The Thirteenth International Conference on Learning Representations, 2025."},{"key":"e_1_3_2_1_8_1","first-page":"38087","volume-title":"International conference on machine learning","author":"Xiao Guangxuan","year":"2023","unstructured":"Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International conference on machine learning, pages 38087--38099. PMLR, 2023."},{"key":"e_1_3_2_1_9_1","first-page":"196","volume-title":"Atom: Low-bit quantization for efficient and accurate llm serving","author":"Zhao Yilong","year":"2024","unstructured":"Yilong Zhao, Chien-Yu Lin, Kan Zhu, Zihao Ye, Lequn Chen, Size Zheng, Luis Ceze, Arvind Krishnamurthy, Tianqi Chen, and Baris Kasikci. Atom: Low-bit quantization for efficient and accurate llm serving. In P. Gibbons, G. Pekhimenko, and C. De Sa, editors, Proceedings of Machine Learning and Systems, volume 6, pages 196--209, 2024."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3714983.3714987"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-021-00184-y"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2023.1209795"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681186"},{"key":"e_1_3_2_1_14_1","volume-title":"The Thirteenth International Conference on Learning Representations","author":"Wei Wenjie","year":"2025","unstructured":"Wenjie Wei, Malu Zhang, Zijian Zhou, Ammar Belatreche, Yimeng Shan, Yu Liang, Honglin Cao, Jieyuan Zhang, and Yang Yang. QP-SNN: Quantized and pruned spiking neural networks. In The Thirteenth International Conference on Learning Representations, 2025."},{"key":"e_1_3_2_1_15_1","volume-title":"NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks, page 734--740","author":"Li Haomin","year":"2025","unstructured":"Haomin Li, Fangxin Liu, Zewen Sun, Zongwu Wang, Shiyuan Huang, Ning Yang, and Li Jiang. NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks, page 734--740. Association for Computing Machinery, New York, NY, USA, 2025."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICONS62911.2024.00047"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.971937"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747906"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.638474"},{"key":"e_1_3_2_1_20_1","volume-title":"Wonyong Sung, and Jungwook Choi. Enhancing computation efficiency in large language models through weight and activation quantization. arXiv preprint arXiv:2311.05161","author":"Lee Janghwan","year":"2023","unstructured":"Janghwan Lee, Minsoo Kim, Seungcheol Baek, Seok Joong Hwang, Wonyong Sung, and Jungwook Choi. Enhancing computation efficiency in large language models through weight and activation quantization. arXiv preprint arXiv:2311.05161, 2023."},{"key":"e_1_3_2_1_21_1","volume-title":"International Conference on Learning Representations","author":"Merity Stephen","year":"2017","unstructured":"Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. In International Conference on Learning Representations, 2017."},{"key":"e_1_3_2_1_22_1","volume-title":"Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1--67","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1--67, 2020."},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS '23","author":"Bondarenko Yelysei","year":"2023","unstructured":"Yelysei Bondarenko, Markus Nagel, and Tijmen Blankevoort. Quantizable transformers: removing outliers by helping attention heads do nothing. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS '23, Red Hook, NY, USA, 2023. Curran Associates Inc."},{"key":"e_1_3_2_1_24_1","volume-title":"Addressing activation outliers in llms: A systematic review of post-training quantization techniques","author":"Czak\u00f3 Patrik","year":"2025","unstructured":"Patrik Czak\u00f3, G\u00e1bor Kert\u00e9sz, and S\u00e1ndor Sz\u00e9n\u00e1si. Addressing activation outliers in llms: A systematic review of post-training quantization techniques. IEEE Access, 2025."},{"key":"e_1_3_2_1_25_1","volume-title":"8-bit matrix multiplication for transformers at scale. Advances in neural information processing systems, 35:30318--30332","author":"Dettmers Tim","year":"2022","unstructured":"Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. Gpt3.int8 (): 8-bit matrix multiplication for transformers at scale. Advances in neural information processing systems, 35:30318--30332, 2022."},{"key":"e_1_3_2_1_26_1","volume-title":"Standard deviation-based quantization for deep neural networks. arXiv preprint arXiv:2202.12422","author":"Ardakani Amir","year":"2022","unstructured":"Amir Ardakani, Arash Ardakani, Brett Meyer, James J Clark, and Warren J Gross. Standard deviation-based quantization for deep neural networks. arXiv preprint arXiv:2202.12422, 2022."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_36"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476408"},{"key":"e_1_3_2_1_29_1","volume-title":"Quality Press","author":"Iglewicz Boris","year":"1993","unstructured":"Boris Iglewicz and David C Hoaglin. Volume 16: how to detect and handle outliers. Quality Press, 1993."},{"key":"e_1_3_2_1_30_1","volume-title":"The Twelfth International Conference on Learning Representations","author":"Shao Wenqi","year":"2024","unstructured":"Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Omnidirectionally calibrated quantization for large language models. In The Twelfth International Conference on Learning Representations, 2024."},{"key":"e_1_3_2_1_31_1","first-page":"1526","volume-title":"Asian Conference on Machine Learning","author":"Yang Jiaming","year":"2024","unstructured":"Jiaming Yang, Chenwei Tang, Caiyang Yu, and Jiancheng Lv. Gwq: Group-wise quantization framework for neural networks. In Asian Conference on Machine Learning, pages 1526--1541. PMLR, 2024."},{"key":"e_1_3_2_1_32_1","volume-title":"Bf-imna: A bit fluid in-memory neural architecture for neural network acceleration. arXiv preprint arXiv:2411.01417","author":"Rakka Mariam","year":"2024","unstructured":"Mariam Rakka, Rachid Karami, Ahmed M Eltawil, Mohammed E Fouda, and Fadi Kurdahi. Bf-imna: A bit fluid in-memory neural architecture for neural network acceleration. arXiv preprint arXiv:2411.01417, 2024."},{"key":"e_1_3_2_1_33_1","volume-title":"The Thirteenth International Conference on Learning Representations","author":"Xing Xingrun","year":"2025","unstructured":"Xingrun Xing, Boyan Gao, Zheng Liu, David A. Clifton, Shitao Xiao, Wanpeng Zhang, Li Du, Zheng Zhang, Guoqi Li, and Jiajun Zhang. SpikeLLM: Scaling up spiking neural network to large language models via saliency-based spiking. In The Thirteenth International Conference on Learning Representations, 2025."},{"key":"e_1_3_2_1_34_1","first-page":"66357","article-title":"Towards extremely low-bit large language models","volume":"37","author":"Xu Yuzhuang","year":"2024","unstructured":"Yuzhuang Xu, Xu Han, Zonghan Yang, Shuo Wang, Qingfu Zhu, Zhiyuan Liu, Weidong Liu, and Wanxiang Che. Onebit: Towards extremely low-bit large language models. Advances in Neural Information Processing Systems, 37:66357--66382, 2024.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.122"},{"key":"e_1_3_2_1_36_1","volume-title":"Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:2210.17323","author":"Frantar Elias","year":"2022","unstructured":"Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:2210.17323, 2022."},{"key":"e_1_3_2_1_37_1","volume-title":"Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068","author":"Zhang Susan","year":"2022","unstructured":"Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068, 2022."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6239"},{"key":"e_1_3_2_1_39_1","volume-title":"Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457","author":"Clark Peter","year":"2018","unstructured":"Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018."},{"key":"e_1_3_2_1_40_1","volume-title":"Boolq: Exploring the surprising difficulty of natural yes\/no questions. arXiv preprint arXiv:1905.10044","author":"Clark Christopher","year":"2019","unstructured":"Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising difficulty of natural yes\/no questions. arXiv preprint arXiv:1905.10044, 2019."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474381"},{"key":"e_1_3_2_1_42_1","volume-title":"Reconsidering the energy efficiency of spiking neural networks. arXiv preprint arXiv:2409.08290","author":"Yan Zhanglu","year":"2024","unstructured":"Zhanglu Yan, Zhenyu Bai, and Weng-Fai Wong. Reconsidering the energy efficiency of spiking neural networks. arXiv preprint arXiv:2409.08290, 2024."},{"key":"e_1_3_2_1_43_1","volume-title":"Otters: An energy-efficient spikingtransformer via optical time-to-first-spike encoding","author":"Yan Zhanglu","year":"2025","unstructured":"Zhanglu Yan, Jiayi Mao, Qianhui Liu, Fanfan Li, Gang Pan, Tao Luo, Bowen Zhu, and Weng-Fai Wong. Otters: An energy-efficient spikingtransformer via optical time-to-first-spike encoding, 2025."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASYNC.2018.00018"},{"key":"e_1_3_2_1_45_1","volume-title":"et al. Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip","author":"Akopyan Filipp","year":"2015","unstructured":"Filipp Akopyan, Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza, John Arthur, Paul Merolla, Nabil Imam, Yutaka Nakamura, Pallab Datta, Gi-Joon Nam, et al. Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip. IEEE transactions on computer-aided design of integrated circuits and systems, 34(10):1537--1557, 2015."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42614.2022.9731612"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/HCS55958.2022.9895479"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534111"},{"key":"e_1_3_2_1_49_1","volume-title":"Comprehensive snn compression using admm optimization and activity regularization","author":"Deng Lei","year":"2021","unstructured":"Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, and Yuan Xie. Comprehensive snn compression using admm optimization and activity regularization. IEEE transactions on neural networks and learning systems, 34(6):2791--2805, 2021."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00662"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003162810-13"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.045"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT57864.2024.10619223"},{"key":"e_1_3_2_1_54_1","volume-title":"Cdquant: Greedy coordinate descent for accurate llm quantization","author":"Nair Pranav Ajit","year":"2024","unstructured":"Pranav Ajit Nair and Arun Sai Suggala. Cdquant: Greedy coordinate descent for accurate llm quantization, 2024."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.1001"},{"key":"e_1_3_2_1_56_1","volume-title":"Duquant: Distributing outliers via dual transformation makes stronger quantized llms","author":"Lin Haokun","year":"2024","unstructured":"Haokun Lin, Haobo Xu, Yichen Wu, Jingzhi Cui, Yingtao Zhang, Linzhan Mou, Linqi Song, Zhenan Sun, and Ying Wei. Duquant: Distributing outliers via dual transformation makes stronger quantized llms, 2024."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.102"},{"key":"e_1_3_2_1_58_1","volume-title":"Mitigating the impact of outlier channels for language model quantization with activation regularization. arXiv preprint arXiv:2404.03605","author":"Nrusimha Aniruddha","year":"2024","unstructured":"Aniruddha Nrusimha, Mayank Mishra, Naigang Wang, Dan Alistarh, Rameswar Panda, and Yoon Kim. Mitigating the impact of outlier channels for language model quantization with activation regularization. arXiv preprint arXiv:2404.03605, 2024."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1677-2"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04362-w"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2782-y"},{"key":"e_1_3_2_1_63_1","volume-title":"Lasnn: Layer-wise ann-to-snn distillation for effective and efficient training in deep spiking neural networks. Neurocomputing, page 131351","author":"Hong Di","year":"2025","unstructured":"Di Hong, Yu Qi, and Yueming Wang. Lasnn: Layer-wise ann-to-snn distillation for effective and efficient training in deep spiking neural networks. Neurocomputing, page 131351, 2025."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28975"},{"key":"e_1_3_2_1_65_1","volume-title":"Spikegpt: Generative pre-trained language model with spiking neural networks. arXiv preprint arXiv:2302.13939","author":"Zhu Rui-Jie","year":"2023","unstructured":"Rui-Jie Zhu, Qihang Zhao, Guoqi Li, and Jason K Eshraghian. Spikegpt: Generative pre-trained language model with spiking neural networks. arXiv preprint arXiv:2302.13939, 2023."}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774904.3792337","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:20:57Z","timestamp":1783149657000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792337"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":65,"alternative-id":["10.1145\/3774904.3792337","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792337","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}