{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T07:55:19Z","timestamp":1776930919136,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,16]]},"DOI":"10.1145\/3712285.3759862","type":"proceedings-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T16:04:47Z","timestamp":1762963487000},"page":"1267-1283","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Phoenix: A Refactored I\/O Stack for GPU Direct Storage without Phony Buffers"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4279-9395","authenticated-orcid":false,"given":"Jianqin","family":"Yan","sequence":"first","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0821-0646","authenticated-orcid":false,"given":"Shi","family":"Qiu","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3971-3123","authenticated-orcid":false,"given":"Yina","family":"Lv","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6501-5659","authenticated-orcid":false,"given":"Yifan","family":"Hu","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7141-5903","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2673-5868","authenticated-orcid":false,"given":"Zhirong","family":"Shen","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5780-7900","authenticated-orcid":false,"given":"Xin","family":"Yao","sequence":"additional","affiliation":[{"name":"Huawei Theory Lab, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0233-5838","authenticated-orcid":false,"given":"Renhai","family":"Chen","sequence":"additional","affiliation":[{"name":"Huawei Theory Lab, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-2789","authenticated-orcid":false,"given":"Jiwu","family":"Shu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0283-7050","authenticated-orcid":false,"given":"Gong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Theory Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6450-8485","authenticated-orcid":false,"given":"Yiming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China and Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,15]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"AMD. 2022. Xilinx Runtime library (XRT). https:\/\/xilinx.github.io\/XRT\/master\/html\/p2p.html."},{"key":"e_1_3_3_2_3_2","unstructured":"AMD. 2025. DirectGMA. https:\/\/www.bitflow.com\/technology\/directgma\/."},{"key":"e_1_3_3_2_4_2","unstructured":"AMD. 2025. RoCm. https:\/\/github.com\/RoCm\/ROCK-Kernel-Driver\/blob\/master\/drivers\/gpu\/drm\/amd\/amdkfd\/kfd_peerdirect.c."},{"key":"e_1_3_3_2_5_2","unstructured":"Chenxin An Shansan Gong Ming Zhong Xingjian Zhao Mukai Li Jun Zhang Lingpeng Kong and Xipeng Qiu. 2023. L-eval: Instituting standardized evaluation for long context language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2307.11088 (2023) 26\u00a0pages."},{"key":"e_1_3_3_2_6_2","first-page":"387","volume-title":"19th USENIX Conference on File and Storage Technologies (FAST 21)","author":"Bae Jonghyun","year":"2021","unstructured":"Jonghyun Bae, Jongsung Lee, Yunho Jin, Sam Son, Shine Kim, Hakbeom Jang, Tae\u00a0Jun Ham, and Jae\u00a0W. Lee. 2021. FlashNeuron: SSD-Enabled Large-Batch Training of Very Deep Neural Networks. In 19th USENIX Conference on File and Storage Technologies (FAST 21). USENIX Association, 387\u2013401. https:\/\/www.usenix.org\/conference\/fast21\/presentation\/bae"},{"key":"e_1_3_3_2_7_2","first-page":"387","volume-title":"19th USENIX Conference on File and Storage Technologies (FAST 21)","author":"Bae Jonghyun","year":"2021","unstructured":"Jonghyun Bae, Jongsung Lee, Yunho Jin, Sam Son, Shine Kim, Hakbeom Jang, Tae\u00a0Jun Ham, and Jae\u00a0W. Lee. 2021. FlashNeuron: SSD-Enabled Large-Batch Training of Very Deep Neural Networks. In 19th USENIX Conference on File and Storage Technologies (FAST 21). USENIX Association, 387\u2013401. https:\/\/www.usenix.org\/conference\/fast21\/presentation\/bae"},{"key":"e_1_3_3_2_8_2","unstructured":"Shai Bergman Tanya Brokhman Tzachi Cohen and Mark Silberstein. 2017. {SPIN}: Seamless Operating System Integration of {Peer-to-Peer} {DMA} Between {SSDs} and {GPUs}. 167\u2013179. https:\/\/www.usenix.org\/conference\/atc17\/technical-sessions\/presentation\/bergman"},{"key":"e_1_3_3_2_9_2","unstructured":"BlazingSQL. 2025. BlazingSQL: A GPU-accelerated SQL engine for data science on rapids.ai. https:\/\/github.com\/BlazingDB\/blazingsql."},{"key":"e_1_3_3_2_10_2","first-page":"661","volume-title":"2019 USENIX Annual Technical Conference (USENIX ATC 19)","author":"Brokhman Tanya","year":"2019","unstructured":"Tanya Brokhman, Pavel Lifshits, and Mark Silberstein. 2019. { GAIA} : An { OS} page cache for heterogeneous systems. In 2019 USENIX Annual Technical Conference (USENIX ATC 19). 661\u2013674."},{"key":"e_1_3_3_2_11_2","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared\u00a0D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et\u00a0al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877\u20131901."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3620666.3651353"},{"key":"e_1_3_3_2_13_2","unstructured":"Karl Cobbe Vineet Kosaraju Mohammad Bavarian Mark Chen Heewoo Jun Lukasz Kaiser Matthias Plappert Jerry Tworek Jacob Hilton Reiichiro Nakano et\u00a0al. 2021. Training verifiers to solve math word problems. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2110.14168 (2021)."},{"key":"e_1_3_3_2_14_2","unstructured":"Intel Corporation. 2020. Intel Optane DC SSD Series 400 GB. https:\/\/www.intel.com\/content\/www\/us\/en\/products\/sku\/201860\/intel-optane-ssd-dc-p5800x-series-800gb-2-5in-pcie-x4-3d-xpoint\/specifications.html."},{"key":"e_1_3_3_2_15_2","first-page":"929","volume-title":"19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)","author":"Eisenman Assaf","year":"2022","unstructured":"Assaf Eisenman, Kiran\u00a0Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, and Murali Annavaram. 2022. { Check-N-Run} : A checkpointing system for training deep learning recommendation models. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). 929\u2013943."},{"key":"e_1_3_3_2_16_2","first-page":"135","volume-title":"18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)","author":"Fu Yao","year":"2024","unstructured":"Yao Fu, Leyang Xue, Yeqi Huang, Andrei-Octavian Brabete, Dmitrii Ustiugov, Yuvraj Patel, and Luo Mai. 2024. { ServerlessLLM} :{ Low-Latency} serverless inference for large language models. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24). 135\u2013153."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3689031.3696072"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00289"},{"key":"e_1_3_3_2_19_2","unstructured":"IBM. 2024. IBM Storage Scale. https:\/\/www.ibm.com\/docs\/en\/storage-scale\/5.2.1?topic=installing-gpudirect-storage-storage-scale."},{"key":"e_1_3_3_2_20_2","unstructured":"Keras. 2025. Keras. https:\/\/keras.io\/."},{"key":"e_1_3_3_2_21_2","unstructured":"The\u00a0Linux Kernel. 2025. io-uring. https:\/\/man7.org\/linux\/man-pages\/man7\/io_uring.7.html."},{"key":"e_1_3_3_2_22_2","unstructured":"The\u00a0Linux Kernel. 2025. Linux Filesystems API summary. https:\/\/www.kernel.org\/doc\/html\/latest\/filesystems\/api-summary.html#c.bio_iov_iter_get_pages."},{"key":"e_1_3_3_2_23_2","unstructured":"The\u00a0Linux Kernel. 2025. PCI Peer-to-Peer DMA support. https:\/\/docs.kernel.org\/driver-api\/pci\/p2pdma.html."},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613165"},{"key":"e_1_3_3_2_25_2","unstructured":"Aixin Liu Bei Feng Bin Wang Bingxuan Wang Bo Liu Chenggang Zhao Chengqi Dengr Chong Ruan Damai Dai Daya Guo et\u00a0al. 2024. Deepseek-v2: A strong economical and efficient mixture-of-experts language model. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2405.04434 (2024)."},{"key":"e_1_3_3_2_26_2","series-title":"Proceedings of Machine Learning Research","first-page":"22137","volume-title":"Proceedings of the 40th International Conference on Machine Learning","volume":"202","author":"Liu Zichang","year":"2023","unstructured":"Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, and Beidi Chen. 2023. Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. In Proceedings of the 40th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a0202), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 22137\u201322176. https:\/\/proceedings.mlr.press\/v202\/liu23am.html"},{"key":"e_1_3_3_2_27_2","unstructured":"The Linux\u00a0Kernel Memory. 2025. Physical Memory Model. https:\/\/docs.kernel.org\/mm\/memory-model.html."},{"key":"e_1_3_3_2_28_2","unstructured":"Microsoft. 2025. DirectStorage. https:\/\/github.com\/microsoft\/DirectStorage."},{"key":"e_1_3_3_2_29_2","first-page":"203","volume-title":"19th USENIX Conference on File and Storage Technologies (FAST 21)","author":"Mohan Jayashree","year":"2021","unstructured":"Jayashree Mohan, Amar Phanishayee, and Vijay Chidambaram. 2021. { CheckFreq} : Frequent,{ Fine-Grained}{ DNN} Checkpointing. In 19th USENIX Conference on File and Storage Technologies (FAST 21). 203\u2013216."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA56546.2023.10070949"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA52012.2021.00020"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3409963.3410491"},{"key":"e_1_3_3_2_33_2","unstructured":"NetApp. 2024. NetApp ONTAP. https:\/\/www.netapp.com\/data-storage\/ontap\/."},{"key":"e_1_3_3_2_34_2","unstructured":"NVIDIA. 2024. nvidia-fs. https:\/\/github.com\/NVIDIA\/gds-nvidia-fs."},{"key":"e_1_3_3_2_35_2","unstructured":"NVIDIA. 2025. CUDA Stream. https:\/\/docs.nvidia.com\/cuda\/cuda-runtime-api\/group__CUDART__STREAM.html."},{"key":"e_1_3_3_2_36_2","unstructured":"NVIDIA. 2025. cudaLaunchHostFunc. https:\/\/docs.nvidia.com\/cuda\/cuda-runtime-api\/group__CUDART__EXECUTION.html#group__CUDART__EXECUTION_1g05841eaa5f90f27124241baafb3e856f."},{"key":"e_1_3_3_2_37_2","unstructured":"NVIDIA. 2025. cudaMemcpy. https:\/\/developer.download.nvidia.com\/compute\/DevZone\/docs\/html\/C\/doc\/html\/group__CUDART__MEMORY_g48efa06b81cc031b2aa6fdc2e9930741.html."},{"key":"e_1_3_3_2_38_2","unstructured":"NVIDIA. 2025. cuDF - GPU DataFrames. https:\/\/github.com\/rapidsai\/cudf."},{"key":"e_1_3_3_2_39_2","unstructured":"NVIDIA. 2025. cuFile API Reference Guide. https:\/\/docs.nvidia.com\/gpudirect-storage\/api-reference-guide\/index.html."},{"key":"e_1_3_3_2_40_2","unstructured":"NVIDIA. 2025. Developing a Linux Kernel Module using GPUDirect RDMA. https:\/\/docs.nvidia.com\/cuda\/gpudirect-rdma\/index.html#kernel-api."},{"key":"e_1_3_3_2_41_2","unstructured":"NVIDIA. 2025. GDRCopy. https:\/\/github.com\/NVIDIA\/gdrcopy."},{"key":"e_1_3_3_2_42_2","unstructured":"NVIDIA. 2025. GPUDirect Storage Configuration Parameters. https:\/\/docs.nvidia.com\/gpudirect-storage\/configuration-guide\/index.html#gds-configuration-parameters."},{"key":"e_1_3_3_2_43_2","unstructured":"NVIDIA. 2025. Magnum IO. https:\/\/github.com\/NVIDIA\/MagnumIO."},{"key":"e_1_3_3_2_44_2","unstructured":"NVIDIA. 2025. NVIDIA GPU PCI Bar Size. https:\/\/docs.nvidia.com\/cuda\/gpudirect-rdma\/#pci-bar-sizes."},{"key":"e_1_3_3_2_45_2","unstructured":"NVIDIA. 2025. NVIDIA L40S. https:\/\/www.nvidia.com\/en-sg\/data-center\/l40s\/."},{"key":"e_1_3_3_2_46_2","unstructured":"NVIDIA. 2025. nvidia-peermem. https:\/\/github.com\/Mellanox\/nv_peer_memory."},{"key":"e_1_3_3_2_47_2","unstructured":"NVIDIA. 2025. System Management Interface SMI. https:\/\/developer.nvidia.com\/system-management-interface."},{"key":"e_1_3_3_2_48_2","unstructured":"Richard\u00a0Yuanzhe Pang Alicia Parrish Nitish Joshi Nikita Nangia Jason Phang Angelica Chen Vishakh Padmakumar Johnny Ma Jana Thompson He He et\u00a0al. 2021. QuALITY: Question answering with long input texts yes! arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2112.08608 (2021)."},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"publisher","unstructured":"Jeongmin\u00a0Brian Park Vikram\u00a0Sharma Mailthody Zaid Qureshi and Wen-mei Hwu. 2024. Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses. Proc. VLDB Endow. 17 6 (May 2024) 1227\u20131240. 10.14778\/3648160.3648166","DOI":"10.14778\/3648160.3648166"},{"key":"e_1_3_3_2_50_2","unstructured":"Jeongmin\u00a0Brian Park Kun Wu Vikram\u00a0Sharma Mailthody Zaid Quresh Scott Mahlke and Wen-mei Hwu. 2024. LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2407.15264 (2024)."},{"key":"e_1_3_3_2_51_2","unstructured":"Pytorch. 2025. PyTorch documentation. https:\/\/pytorch.org\/docs\/stable\/data.html#torch.utils.data.DataLoader."},{"key":"e_1_3_3_2_52_2","first-page":"155","volume-title":"23rd USENIX Conference on File and Storage Technologies (FAST 25)","author":"Qin Ruoyu","year":"2025","unstructured":"Ruoyu Qin, Zheming Li, Weiran He, Jialei Cui, Feng Ren, Mingxing Zhang, Yongwei Wu, Weimin Zheng, and Xinran Xu. 2025. Mooncake: Trading More Storage for Less Computation \u2014 A KVCache-centric Architecture for Serving LLM Chatbot. In 23rd USENIX Conference on File and Storage Technologies (FAST 25). USENIX Association, Santa Clara, CA, 155\u2013170. https:\/\/www.usenix.org\/conference\/fast25\/presentation\/qin"},{"key":"e_1_3_3_2_53_2","first-page":"221","volume-title":"23rd USENIX Conference on File and Storage Technologies (FAST 25)","author":"Qiu Shi","year":"2025","unstructured":"Shi Qiu, Weinan Liu, Yifan Hu, Jianqin Yan, Zhirong Shen, Xin Yao, Renhai Chen, Gong Zhang, and Yiming Zhang. 2025. { GeminiFS} : A Companion File System for { GPUs}. In 23rd USENIX Conference on File and Storage Technologies (FAST 25). 221\u2013236."},{"key":"e_1_3_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3575693.3575748"},{"key":"e_1_3_3_2_55_2","unstructured":"ShareGPT. 2025. ShareGPT. https:\/\/huggingface.co\/datasets\/anon8231489123\/ShareGPT_Vicuna_unfiltered."},{"key":"e_1_3_3_2_56_2","first-page":"31094","volume-title":"International Conference on Machine Learning","author":"Sheng Ying","year":"2023","unstructured":"Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher R\u00e9, Ion Stoica, and Ce Zhang. 2023. Flexgen: High-throughput generative inference of large language models with a single gpu. In International Conference on Machine Learning. PMLR, 31094\u201331116."},{"key":"e_1_3_3_2_57_2","unstructured":"Pure Storage. 2024. Pure Storage Delivers All-Flash for Every Storage Need. https:\/\/www.purestorage.com\/company\/newsroom\/press-releases\/pure-delivers-all-flash-for-every-storage-need.html."},{"key":"e_1_3_3_2_58_2","unstructured":"syoyo. 2025. safetensors-cpp. https:\/\/github.com\/syoyo\/safetensors-cpp.git."},{"key":"e_1_3_3_2_59_2","unstructured":"ThinkParQ. 2022. BeeGFS. https:\/\/doc.beegfs.io\/7.3.0\/advanced_topics\/gds_support.html\/."},{"key":"e_1_3_3_2_60_2","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar et\u00a0al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2302.13971 (2023)."},{"key":"e_1_3_3_2_61_2","unstructured":"vllm project. 2025. vLLM. https:\/\/github.com\/vllm-project\/vllm."},{"key":"e_1_3_3_2_62_2","unstructured":"WekaFS. 2020. WekaFS Architecture White Paper. https:\/\/www.weka.io\/wp-content\/uploads\/files\/2020\/08\/weka-architecture-white-paper.pdf."},{"key":"e_1_3_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/CloudCom.2017.14"},{"key":"e_1_3_3_2_64_2","unstructured":"YRCloudFile. 2024. YRCloudFile Delivers Unmatched Performance Scalability Simplicity and Flexibility for AI and HPC Workloads Without Compromise. https:\/\/www.yanrongyun.com\/."},{"key":"e_1_3_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/PACT.2015.43"},{"key":"e_1_3_3_2_66_2","unstructured":"Susan Zhang Stephen Roller Naman Goyal Mikel Artetxe Moya Chen Shuohui Chen Christopher Dewan Mona Diab Xian Li Xi\u00a0Victoria Lin et\u00a0al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2205.01068 (2022)."}],"event":{"name":"SC '25: The International Conference for High Performance Computing, Networking, Storage and Analysis","location":"St. Louis MO USA","acronym":"SC '25","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing"]},"container-title":["Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3712285.3759862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T18:52:40Z","timestamp":1773255160000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3712285.3759862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,15]]},"references-count":65,"alternative-id":["10.1145\/3712285.3759862","10.1145\/3712285"],"URL":"https:\/\/doi.org\/10.1145\/3712285.3759862","relation":{},"subject":[],"published":{"date-parts":[[2025,11,15]]},"assertion":[{"value":"2025-11-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}