{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T15:31:58Z","timestamp":1773588718370,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":82,"publisher":"ACM","funder":[{"name":"National Science Foundation","award":["2216964"],"award-info":[{"award-number":["2216964"]}]},{"name":"Defense Sciences Office, DARPA","award":["HR00112520038"],"award-info":[{"award-number":["HR00112520038"]}]},{"name":"Naval Surface Warfare Center","award":["N00164-23-9-G057-01"],"award-info":[{"award-number":["N00164-23-9-G057-01"]}]},{"name":"Stanford University","award":["Stanford Data Analytics for What’s Next (DAWN) Affiliate Program"],"award-info":[{"award-number":["Stanford Data Analytics for What’s Next (DAWN) Affiliate Program"]}]},{"name":"Semiconductor Research Corporation","award":["PRISM Center"],"award-info":[{"award-number":["PRISM Center"]}]},{"name":"National Science Foundation","award":["Graduate Research Fellowships Program (GRFP)"],"award-info":[{"award-number":["Graduate Research Fellowships Program (GRFP)"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,3,22]]},"DOI":"10.1145\/3779212.3790165","type":"proceedings-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T13:55:26Z","timestamp":1773150926000},"page":"798-820","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2268-0074","authenticated-orcid":false,"given":"Rubens","family":"Lacouture","sequence":"first","affiliation":[{"name":"Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9668-902X","authenticated-orcid":false,"given":"Nathan","family":"Zhang","sequence":"additional","affiliation":[{"name":"SambaNova Systems, Inc., Palo Alto, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5809-7031","authenticated-orcid":false,"given":"Ritvik","family":"Sharma","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2782-837X","authenticated-orcid":false,"given":"Marco","family":"Siracusa","sequence":"additional","affiliation":[{"name":"Barcelona Supercomputing Center, Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2267-903X","authenticated-orcid":false,"given":"Fredrik","family":"Kjolstad","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8779-0636","authenticated-orcid":false,"given":"Kunle","family":"Olukotun","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4195-8106","authenticated-orcid":false,"given":"Olivia","family":"Hsu","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA and Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579990.3580020"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3519939.3523442"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3620665.3640366"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3591236"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544559"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/165939.166023"},{"key":"e_1_3_2_1_7_1","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. arXiv:1707.03815 [stat.ML] https:\/\/arxiv.org\/abs\/1707.03815"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). 101-113","author":"Bondhugula Uday","unstructured":"Uday Bondhugula, Albert Hartono, J. Ramanujam, and P. Sadayappan. 2008. A practical automatic polyhedral parallelizer and locality optimizer. In Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). 101-113."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/VLSITechnologyandCir46769.2022.9830509"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSSC.2025.3613245"},{"key":"e_1_3_2_1_11_1","first-page":"578","volume-title":"TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). USENIX Association, Carlsbad, CA, 578-594. https:\/\/www.usenix.org\/conference\/osdi18\/presentation\/chen"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3276493"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358276"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1189"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/1385-7258(72)90034-0"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3524059.3532386"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01407931"},{"key":"e_1_3_2_1_19_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589348"},{"key":"e_1_3_2_1_21_1","volume-title":"The state of sparsity in deep neural networks. arXiv preprint arXiv:1902.09574","author":"Gale Trevor","year":"2019","unstructured":"Trevor Gale, Erich Elsen, and Sara Hooker. 2019. The state of sparsity in deep neural networks. arXiv preprint arXiv:1902.09574 (2019)."},{"key":"e_1_3_2_1_22_1","first-page":"1","article-title":"Sparse GPU Kernels for Deep Learning. IEEE Press","volume":"17","author":"Gale Trevor","year":"2020","unstructured":"Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen. 2020. Sparse GPU Kernels for Deep Learning. IEEE Press, Chapter 17, 1-14.","journal-title":"Chapter"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO56248.2022.00046"},{"key":"e_1_3_2_1_24_1","unstructured":"Google. 2021. MLIR Sparsifier. https:\/\/developers.google.com\/mlir-sparsifier"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/355791.355796"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_3_2_1_27_1","volume-title":"Dally","author":"Han Song","year":"2015","unstructured":"Song Han, Jeff Pool, John Tran, and William J. Dally. 2015. Learning both Weights and Connections for Efficient Neural Networks. arXiv:1506.02626 [cs.NE] https:\/\/arxiv.org\/abs\/1506.02626"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358275"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485505"},{"key":"e_1_3_2_1_30_1","first-page":"1","article-title":"Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks","volume":"22","author":"Hoefler Torsten","year":"2021","unstructured":"Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste. 2021. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research, Vol. 22, 241 (2021), 1-124.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696443.3708918"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3582016.3582051"},{"key":"e_1_3_2_1_33_1","unstructured":"Olivia Weiya Hsu. 2025. Programming Systems for Sparse Accelerators. Ph.D. Dissertation. Stanford University."},{"key":"e_1_3_2_1_34_1","volume-title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs. arXiv preprint arXiv:2005.00687","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. arXiv preprint arXiv:2005.00687 (2020)."},{"key":"e_1_3_2_1_35_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_1_36_1","volume-title":"Tensor Algebra Compilation with Workspaces. International Symposium on Code Generation and Optimization (February","author":"Kjolstad Fredrik","year":"2019","unstructured":"Fredrik Kjolstad, Peter Ahrens, Shoaib Kamil, and Saman Amarasinghe. 2019. Tensor Algebra Compilation with Workspaces. International Symposium on Code Generation and Optimization (February 2019)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133901"},{"key":"e_1_3_2_1_38_1","unstructured":"Fredrik Berg Kj\u00f8lstad. 2020. Sparse tensor algebra compilation. Ph.D. Dissertation. Massachusetts Institute of Technology."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3192366.3192379"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","unstructured":"Kalhan Koul Olivia Hsu Yuchen Mei Sai Gautham Ravipati Maxwell Strange Jackson Melchert Alex Carsello Taeyoung Kong Po-Han Chen Huifeng Ke Keyi Zhang Qiaoyi Liu Gedeon Nyengele Zhouhua Xie Akhilesh Balasingam Jayashree Adivarahan Ritvik Sharma Christopher Torng Joel S. Emer Fredrik Kjolstad Mark Horowitz and Priyanka Raina. 2025. Onyx: A 12-nm Programmable Accelerator for Dense and Sparse Applications. IEEE Journal of Solid-State Circuits (2025) 1-13. doi:10.1109\/JSSC.2025.3604724","DOI":"10.1109\/JSSC.2025.3604724"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534933"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/VLSITechnologyandCir46783.2024.10631383"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3591268"},{"key":"e_1_3_2_1_44_1","unstructured":"Rubens Lacouture Olivia Hsu Kunle Olukotun and Fredrik Kjolstad. [n.d.]. Challenges with Hardware-Software Co-design for Sparse Machine Learning on Streaming Dataflow. ([n.d.])."},{"key":"e_1_3_2_1_45_1","volume-title":"LLM-Guided Autoscheduling for Large-Scale Sparse Machine Learning. In NeurIPS 2025 Workshop on Machine Learning for Systems (ML for Systems). https:\/\/openreview.net\/forum?id=7H9qWe8lLO","author":"Lacouture Rubens","year":"2025","unstructured":"Rubens Lacouture, Genghan Zhang, Konstantin Hossfeld, Tian Zhao, and Kunle Olukotun. 2025. LLM-Guided Autoscheduling for Large-Scale Sparse Machine Learning. In NeurIPS 2025 Workshop on Machine Learning for Systems (ML for Systems). https:\/\/openreview.net\/forum?id=7H9qWe8lLO"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649816"},{"key":"e_1_3_2_1_47_1","unstructured":"LLVM. [n.d.]. Torch-MLIR. https:\/\/github.com\/llvm\/torch-mlir"},{"key":"e_1_3_2_1_48_1","volume-title":"F. Nisa Bostanc?, Ataberk Olgun, A. Giray Ya\u011flik\u00e7i, and Onur Mutlu.","author":"Luo Haocong","year":"2023","unstructured":"Haocong Luo, Yahya Can Tu\u011frul, F. Nisa Bostanc?, Ataberk Olgun, A. Giray Ya\u011flik\u00e7i, and Onur Mutlu. 2023. Ramulator 2.0: A Modern, Modular, and Extensible DRAM Simulator."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2024.3390542"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3613424.3623791"},{"key":"e_1_3_2_1_51_1","unstructured":"Andrew Ng et al. 2011. Sparse autoencoder. CS294A Lecture notes Vol. 72 2011 (2011) 1-19."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480048"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453483.3454083"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080255"},{"key":"e_1_3_2_1_55_1","unstructured":"NVIDIA. [n.d.]. TensorRT. https:\/\/github.com\/NVIDIA\/TensorRT"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2485922.2485935"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3140659.3080256"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS49936.2021.00034"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3689752"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480047"},{"key":"e_1_3_2_1_61_1","volume-title":"XLA : Compiling Machine Learning for Peak Performance.","author":"Sabne Amit","year":"2020","unstructured":"Amit Sabne. 2020. XLA : Compiling Machine Learning for Peak Performance."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3428226"},{"key":"e_1_3_2_1_63_1","volume-title":"Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, et al.","author":"Adiyoso Setio Arnaud Arindra","year":"2017","unstructured":"Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel, Moira SN Berens, Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, et al., 2017. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Medical image analysis, Vol. 42 (2017), 1-13."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3613424.3614284"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parco.2016.02.004"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649820"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/2813885.2738003"},{"key":"e_1_3_2_1_68_1","volume-title":"Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 (2019)."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"e_1_3_2_1_70_1","volume-title":"Learning structured sparsity in deep neural networks. Advances in neural information processing systems","author":"Wen Wei","year":"2016","unstructured":"Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2016. Learning structured sparsity in deep neural networks. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2022.3189976"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447786.3456247"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2205.05826"},{"key":"e_1_3_2_1_74_1","volume-title":"SpDISTAL: Compiling Distributed Sparse Tensor Computations. arXiv preprint arXiv:2207.13901","author":"Yadav Rohan","year":"2022","unstructured":"Rohan Yadav, Alex Aiken, and Fredrik Kjolstad. 2022. SpDISTAL: Compiling Distributed Sparse Tensor Computations. arXiv preprint arXiv:2207.13901 (2022)."},{"key":"e_1_3_2_1_75_1","volume-title":"Scorch: A Library for Sparse Deep Learning. arXiv preprint arXiv:2405.16883","author":"Yan Bobby","year":"2024","unstructured":"Bobby Yan, Alexander J Root, Trevor Gale, David Broman, and Fredrik Kjolstad. 2024. Scorch: A Library for Sparse Deep Learning. arXiv preprint arXiv:2405.16883 (2024)."},{"key":"e_1_3_2_1_76_1","unstructured":"Zhilin Yang William W. Cohen and Ruslan Salakhutdinov. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. arXiv:1603.08861 [cs.LG] https:\/\/arxiv.org\/abs\/1603.08861"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","unstructured":"Zihao Ye Ruihang Lai Junru Shao Tianqi Chen and Luis Ceze. 2022. SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning. doi:10.48550\/ARXIV.2207.04606","DOI":"10.48550\/ARXIV.2207.04606"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1086\/jar.33.4.3629752"},{"key":"e_1_3_2_1_79_1","volume-title":"Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al.","author":"Zaheer Manzil","year":"2020","unstructured":"Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al., 2020. Big bird: Transformers for longer sequences. Advances in neural information processing systems, Vol. 33 (2020), 17283-17297."},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3656426"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA59077.2024.00046"},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3559009.3569685"}],"event":{"name":"ASPLOS '26: 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems","location":"Pittsburgh PA USA","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems","SIGPLAN ACM Special Interest Group on Programming Languages","SIGARCH ACM Special Interest Group on Computer Architecture","SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2"],"original-title":[],"deposited":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T14:08:50Z","timestamp":1773583730000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3779212.3790165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,22]]},"references-count":82,"alternative-id":["10.1145\/3779212.3790165","10.1145\/3779212"],"URL":"https:\/\/doi.org\/10.1145\/3779212.3790165","relation":{},"subject":[],"published":{"date-parts":[[2026,3,22]]},"assertion":[{"value":"2026-03-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}