{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T15:05:33Z","timestamp":1783955133474,"version":"3.55.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGOPS Oper. Syst. Rev."],"published-print":{"date-parts":[[2026,7,13]]},"abstract":"<jats:p>Computer systems are increasingly using AI techniques, as evidenced by increasing numbers of publications and several reported deployments in production systems. While early work in this area focuses on relatively small ML models and statistical techniques that replace heuristics, the field is experiencing a paradigm shift towards using agentic techniques to automatically discover new systems algorithms and designs, driven by a rapid increase in model capabilities.<\/jats:p>\n                  <jats:p>We argue that these recent advances in AI capabilities \u2013 specifically, models that reason about the world and perform complex sequences of actions through agentic interactions \u2013 not only suggest that the OS community should employ AI more widely in its research, but also that it should look at AI for Systems through a new lens. We show that solving systems problems such as resource scheduling and code optimization requires reasoning that is fundamentally as hard as any reasoning problem a human might have to solve. By establishing a strict success criterion for what it means to ''fully solve'' these tasks across open-ended, real-world edge cases, we demonstrate that these problems are ''AGIcomplete''. We argue that this makes systems problems a promising target for AGI research, and that both the AI and systems community should investigate this area more.<\/jats:p>","DOI":"10.1145\/3830422.3830425","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:20:01Z","timestamp":1783952401000},"page":"21-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI for Systems is \"AGI-Complete\""],"prefix":"10.1145","volume":"60","author":[{"given":"Martin","family":"Maas","sequence":"first","affiliation":[{"name":"Google DeepMind, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milad","family":"Hashemi","sequence":"additional","affiliation":[{"name":"Google DeepMind, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn","family":"McKinley","sequence":"additional","affiliation":[{"name":"Google, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parthasarathy","family":"Ranganathan","sequence":"additional","affiliation":[{"name":"Google, Mountain View, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"232","volume-title":"Proceedings of Machine Learning and Systems, D. Song, M. Carbin, and T. Chen (Eds.)","volume":"5","author":"Barbalho Hugo","year":"2023","unstructured":"Hugo Barbalho, Patricia Kovaleski, Beibin Li, Luke Marshall, Marco Molinaro, Abhisek Pan, Eli Cortez, Matheus Leao, Harsh Patwari, Zuzu Tang, Larissa Rozales Gon\u00e7alves, David Dion, Thomas Moscibroda, and Ishai Menache. 2023. Virtual Machine Allocation with Lifetime Predictions. In Proceedings of Machine Learning and Systems, D. Song, M. Carbin, and T. Chen (Eds.), Vol. 5. Curan, 232-253. https:\/\/proceedings.mlsys.org\/paper_files\/paper\/2023\/file\/ 48eb2c79643df5cb7d125945238bd7d0-Paper-mlsys2023.pdf"},{"key":"e_1_2_1_2_1","unstructured":"Audrey Cheng Shu Liu Melissa Pan Zhifei Li Shubham Agarwal Mert Cemri Bowen Wang Alexander Krentsel Tian Xia Jongseok Park Shuo Yang Jeff Chen Lakshya Agrawal Ashwin Naren Shulu Li Ruiying Ma Aditya Desai Jiarong Xing Koushik Sen Matei Zaharia and Ion Stoica. 2025. Let the Barbarians In: How AI Can Accelerate Systems Performance Research. arXiv:2512.14806 [cs.SE] https:\/\/arxiv. org\/abs\/2512.14806"},{"key":"e_1_2_1_3_1","unstructured":"Audrey Cheng Shu Liu Melissa Pan Zhifei Li Bowen Wang Alex Krentsel Tian Xia Mert Cemri Jongseok Park Shuo Yang Jeff Chen Lakshya Agrawal Aditya Desai Jiarong Xing Koushik Sen Matei Zaharia and Ion Stoica. 2025. Barbarians at the Gate: How AI is Upending Systems Research. arXiv:2510.06189 [cs.AI] https:\/\/arxiv. org\/abs\/2510.06189"},{"key":"e_1_2_1_4_1","volume-title":"Linux kernel engineer introduces Sashiko code review system. The Register. https:\/\/www.theregister.com\/2026\/03\/20\/ linux-kernel-engineer-introduces-sashiko-code-review-system\/ Accessed","author":"Claburn Thomas","year":"2026","unstructured":"Thomas Claburn. 2026. Linux kernel engineer introduces Sashiko code review system. The Register. https:\/\/www.theregister.com\/2026\/03\/20\/ linux-kernel-engineer-introduces-sashiko-code-review-system\/ Accessed: May 15, 2026."},{"key":"e_1_2_1_5_1","volume-title":"Advances in Neural Information Processing Systems","author":"Cohn David","year":"1996","unstructured":"David Cohn and Satinder Singh. 1996. Predicting Lifetimes in Dynamically Allocated Memory. In Advances in Neural Information Processing Systems, M.C. Mozer, M. Jordan, and T. Petsche (Eds.), Vol. 9. MIT Press. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/1996\/file\/ a9078e8653368c9c291ae2f8b74012e7-Paper.pdf"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3624062.3624172"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2168836.2168847"},{"key":"e_1_2_1_8_1","volume-title":"This Cadence AI Super-Agent is World\u2019s First to Automate Chip Design. Cambrian-AI Research. https:\/\/cambrian-ai.com\/this-cadence-ai-super-agent-is-worldsfirst- to-automate-chip-design\/ Accessed","author":"Freund Karl","year":"2026","unstructured":"Karl Freund. 2026. This Cadence AI Super-Agent is World\u2019s First to Automate Chip Design. Cambrian-AI Research. https:\/\/cambrian-ai.com\/this-cadence-ai-super-agent-is-worldsfirst- to-automate-chip-design\/ Accessed: May 15, 2026."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3445814.3446700"},{"key":"e_1_2_1_10_1","volume-title":"12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Gog Ionel","year":"2016","unstructured":"Ionel Gog, Malte Schwarzkopf, Adam Gleave, Robert N. M.Watson, and Steven Hand. 2016. Firmament: Fast, Centralized Cluster Scheduling at Scale. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 99- 115. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/ presentation\/gog"},{"key":"e_1_2_1_11_1","unstructured":"Google Cloud. 2024. Artificial intelligence (AI) vs. machine learning (ML). https:\/\/cloud.google.com\/learn\/artificial-intelligence-vsmachine- learning"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599933"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"1928","author":"Hashemi Milad","year":"2018","unstructured":"Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, and Parthasarathy Ranganathan. 2018. Learning Memory Access Patterns. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80), Jennifer Dy and Andreas Krause (Eds.). PMLR, 1919-1928. https:\/\/proceedings.mlr.press\/v80\/hashemi18a. html"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3461535.3461545"},{"key":"e_1_2_1_15_1","first-page":"295","volume-title":"Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation","author":"Hindman Benjamin","year":"2011","unstructured":"Benjamin Hindman, Andy Konwinski, Matei Zaharia, Ali Ghodsi, Anthony D. Joseph, Randy Katz, Scott Shenker, and Ion Stoica. 2011. Mesos: a platform for fine-grained resource sharing in the data center. In Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation (Boston, MA) (NSDI'11). USENIX Association, USA, 295-308."},{"key":"e_1_2_1_16_1","first-page":"257","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Hunter A.H.","year":"2021","unstructured":"A.H. Hunter, Chris Kennelly, Paul Turner, Darryl Gove, Tipp Moseley, and Parthasarathy Ranganathan. 2021. Beyond malloc efficiency to fleet efficiency: a hugepage-aware memory allocator. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). USENIX Association, 257-273. https:\/\/www.usenix.org\/conference\/ osdi21\/presentation\/hunter"},{"key":"e_1_2_1_17_1","unstructured":"Carlos E. Jimenez John Yang AlexanderWettig Shunyu Yao Kexin Pei Ofir Press and Karthik Narasimhan. 2024. SWE-bench: Can Language Models Resolve Real-World GitHub Issues? arXiv:2310.06770 [cs.CL] https:\/\/arxiv.org\/abs\/2310.06770"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2001.903263"},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","unstructured":"John Jumper Richard Evans Alexander Pritzel Tim Green Michael Figurnov Olaf Ronneberger Kathryn Tunyasuvunakool Russ Bates Augustin \u017d\u00eddek Anna Potapenko et al. 2021. Highly accurate protein structure prediction with AlphaFold. nature 596 7873 (2021) 583-589.","DOI":"10.1038\/s41586-021-03819-2"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196909"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772751"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/279227.279229"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2004.1281665"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","unstructured":"Hugh Leather and Chris Cummins. 2020. Machine Learning in Compilers: Past Present and Future. In 2020 Forum for Specification and Design Languages (FDL). 1-8. doi:10.1109\/FDL50818.2020.9232934","DOI":"10.1109\/FDL50818.2020.9232934"},{"key":"e_1_2_1_25_1","volume-title":"Agustin Dal Lago, et al","author":"Li Yujia","year":"2022","unstructured":"Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, R\u00e9mi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. 2022. Competition-level code generation with alphacode. Science 378, 6624 (2022), 1092-1097."},{"key":"e_1_2_1_26_1","volume-title":"Aditya Patil, Don Schwarz, Satish Chandra, Chris Kennelly, Milad Hashemi, and Parthasarathy Ranganathan.","author":"Lin Hannah","year":"2025","unstructured":"Hannah Lin, Martin Maas, Maximilian Roquemore, Arman Hasanzadeh, Fred Lewis, Yusuf Simonson, Tzu-Wei Yang, Amir Yazdanbakhsh, Deniz Altinb\u00fcken, Florin Papa, Maggie Nolan Edmonds, Aditya Patil, Don Schwarz, Satish Chandra, Chris Kennelly, Milad Hashemi, and Parthasarathy Ranganathan. 2025. ECO: An LLMDriven Efficient Code Optimizer for Warehouse Scale Computers. arXiv:2503.15669 [cs.SE] https:\/\/arxiv.org\/abs\/2503.15669"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525517"},{"key":"e_1_2_1_28_1","unstructured":"Mingjie Liu Teodor-Dumitru Ene Robert Kirby Chris Cheng Nathaniel Pinckney Rongjian Liang Jonah Alben Himyanshu Anand Sanmitra Banerjee Ismet Bayraktaroglu Bonita Bhaskaran Bryan Catanzaro Arjun Chaudhuri Sharon Clay Bill Dally Laura Dang Parikshit Deshpande Siddhanth Dhodhi Sameer Halepete Eric Hill Jiashang Hu Sumit Jain Ankit Jindal Brucek Khailany George Kokai Kishor Kunal Xiaowei Li Charley Lind Hao Liu Stuart Oberman Sujeet Omar Ghasem Pasandi Sreedhar Pratty Jonathan Raiman Ambar Sarkar Zhengjiang Shao Hanfei Sun Pratik P Suthar Varun Tej Walker Turner Kaizhe Xu and Haoxing Ren. 2024. ChipNeMo: Domain-Adapted LLMs for Chip Design. arXiv:2311.00176 [cs.CL] https:\/\/arxiv.org\/abs\/2311.00176"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2020.3012883"},{"key":"e_1_2_1_30_1","volume-title":"25th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS).","author":"Maas Martin","year":"2020","unstructured":"Martin Maas, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, and Colin Raffel. 2020. Learningbased Memory Allocation for C++ Server Workloads. In 25th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341302.3342080"},{"key":"e_1_2_1_32_1","volume-title":"Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, et al.","author":"Mirhoseini Azalia","year":"2021","unstructured":"Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, et al. 2021. A graph placement methodology for fast chip design. Nature 594, 7862 (2021), 207-212."},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski et al. 2015. Human-level control through deep reinforcement learning. nature 518 7540 (2015) 529-533.","DOI":"10.1038\/nature14236"},{"key":"e_1_2_1_34_1","unstructured":"Meredith Ringel Morris Jascha Sohl-dickstein Noah Fiedel Tris Warkentin Allan Dafoe Aleksandra Faust Clement Farabet and Shane Legg. 2024. Levels of AGI for Operationalizing Progress on the Path to AGI. arXiv:2311.02462 [cs.AI] https:\/\/arxiv.org\/abs\/2311.02462"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3591195.3595275"},{"key":"e_1_2_1_36_1","unstructured":"Alexander Novikov Ng\u00e2n V\u02dc u Marvin Eisenberger Emilien Dupont Po-Sen Huang Adam Zsolt Wagner Sergey Shirobokov Borislav Kozlovskii Francisco J. R. Ruiz Abbas Mehrabian M. Pawan Kumar Abigail See Swarat Chaudhuri George Holland Alex Davies Sebastian Nowozin Pushmeet Kohli and Matej Balog. 2025. AlphaEvolve: A coding agent for scientific and algorithmic discovery. arXiv:2506.13131 [cs.AI] https:\/\/arxiv.org\/abs\/2506.13131"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3527853"},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the 2014 USENIX Conference on USENIX Annual Technical Conference (Philadelphia, PA) (USENIX ATC'14). USENIX Association, USA, 305-320","author":"Ongaro Diego","year":"2014","unstructured":"Diego Ongaro and John Ousterhout. 2014. In search of an understandable consensus algorithm. In Proceedings of the 2014 USENIX Conference on USENIX Annual Technical Conference (Philadelphia, PA) (USENIX ATC'14). USENIX Association, USA, 305-320."},{"key":"e_1_2_1_39_1","unstructured":"Marcelo Orenes-Vera Margaret Martonosi and David Wentzlaff. 2023. Using LLMs to Facilitate Formal Verification of RTL. arXiv:2309.09437 [cs.AR] https:\/\/arxiv.org\/abs\/2309.09437"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3715324"},{"key":"e_1_2_1_41_1","unstructured":"Szabolcs Rozsnyai and Karthi Thyagarajan. 2024. Farewell to overprovisioning: How to unlock cost-effective elasticity with Spanner. https:\/\/cloud.google.com\/blog\/products\/databases\/how-spannerminimizes- the-need-to-overprovision"},{"key":"e_1_2_1_42_1","volume-title":"Learning Performance-Improving Code Edits. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum? id=ix7rLVHXyY","author":"Shypula Alexander G","year":"2024","unstructured":"Alexander G Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, and Amir Yazdanbakhsh. 2024. Learning Performance-Improving Code Edits. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum? id=ix7rLVHXyY"},{"key":"e_1_2_1_43_1","volume-title":"Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al.","author":"Silver David","year":"2016","unstructured":"David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. nature 529, 7587 (2016), 484-489."},{"key":"e_1_2_1_44_1","first-page":"529","volume-title":"Learning Relaxed Belady for Content Distribution Network Caching. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20)","author":"Song Zhenyu","year":"2020","unstructured":"Zhenyu Song, Daniel S. Berger, Kai Li, andWyatt Lloyd. 2020. Learning Relaxed Belady for Content Distribution Network Caching. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). USENIX Association, Santa Clara, CA, 529-544. https: \/\/www.usenix.org\/conference\/nsdi20\/presentation\/song"},{"key":"e_1_2_1_45_1","first-page":"1149","volume-title":"HALP: Heuristic Aided Learned Preference Eviction Policy for YouTube Content Delivery Network. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)","author":"Song Zhenyu","year":"2023","unstructured":"Zhenyu Song, Kevin Chen, ikhil Sarda, Deniz Alt?nb\u00fcken, Eugene Brevdo, Jimmy Coleman, Xiao Ju, Pawel Jurczyk, Richard Schooler, and Ramki Gummadi. 2023. HALP: Heuristic Aided Learned Preference Eviction Policy for YouTube Content Delivery Network. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). USENIX Association, Boston, MA, 1149-1163. https: \/\/www.usenix.org\/conference\/nsdi23\/presentation\/song-zhenyu"},{"key":"e_1_2_1_46_1","unstructured":"Stockfish. 2023. Stockfish - Open Source Chess Engine. https:\/\/ stockfishchess.org\/"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3694715.3695971"},{"key":"e_1_2_1_48_1","volume-title":"Solving olympiad geometry without human demonstrations. Nature 625, 7995","author":"Trinh Trieu H","year":"2024","unstructured":"Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. 2024. Solving olympiad geometry without human demonstrations. Nature 625, 7995 (2024), 476-482."},{"key":"e_1_2_1_49_1","volume-title":"Campbell","author":"Verma Abhishek","year":"2011","unstructured":"Abhishek Verma, Ludmila Cherkasova, and Roy H. Campbell. 2011. ARIA: automatic resource inference and allocation for mapreduce"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2741948.2741964"},{"key":"e_1_2_1_51_1","volume-title":"Proceedings (Cat. No.99CH36339)","volume":"2","author":"Vintan L.N.","year":"1999","unstructured":"L.N. Vintan and M. Iridon. 1999. Towards a high performance neural branch predictor. In IJCNN\u201999. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), Vol. 2. 868-873 vol.2. doi:10. 1109\/IJCNN.1999.831066"},{"key":"e_1_2_1_52_1","volume-title":"CacheSack: Admission Optimization for Google Datacenter Flash Caches. In 2022 USENIX Annual Technical Conference (USENIX ATC 22)","author":"Yang Tzu-Wei","year":"2022","unstructured":"Tzu-Wei Yang, Seth Pollen, Mustafa Uysal, Arif Merchant, and Homer Wolfmeister. 2022. CacheSack: Admission Optimization for Google Datacenter Flash Caches. In 2022 USENIX Annual Technical Conference (USENIX ATC 22). USENIX Association, Carlsbad, CA, 1021- 1036. https:\/\/www.usenix.org\/conference\/atc22\/presentation\/yangtzu- wei"},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica (Eds.)","volume":"3","author":"Zhou Giulio","year":"2021","unstructured":"Giulio Zhou and Martin Maas. 2021. Learning on Distributed Traces for Data Center Storage Systems. In Proceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica (Eds.), Vol. 3. 350-364. https:\/\/proceedings.mlsys.org\/paper_files\/paper\/2021\/file\/ efe0df3ea4a53a04614ad79e7a8a57de-Paper.pdf"}],"container-title":["ACM SIGOPS Operating Systems Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3830422.3830425","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:20:43Z","timestamp":1783952443000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3830422.3830425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,7,13]]}},"alternative-id":["10.1145\/3830422.3830425"],"URL":"https:\/\/doi.org\/10.1145\/3830422.3830425","relation":{},"ISSN":["0163-5980"],"issn-type":[{"value":"0163-5980","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,13]]},"assertion":[{"value":"2026-07-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}