{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:26:59Z","timestamp":1775068019159,"version":"3.50.1"},"reference-count":86,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>The emergence of large language models (LLMs) offers great promise for building domain-specific agents, but adapting them for network management remains challenging. To understand why, we conduct a case study on network management tasks and find that state-of-the-art specialization techniques rely heavily on extensive, high-quality task-specific data to produce precise solutions. However, real-world network queries are often diverse and unpredictable, making such techniques difficult to scale. Motivated by this gap, we propose MeshAgent, a new workflow that improves precision by extracting domain-specific invariants from sample queries and encoding them as constraints. These constraints guide LLM's generation and validation process, narrowing the search space and enabling low-effort adaptation. We evaluate our method across three network management applications and a user study involving industrial network professionals, showing that it complements existing techniques and consistently improves accuracy. We also introduce reliability metrics and demonstrate that our system is more dependable, with the ability to abstain when confidence is low. Overall, our results show that MeshAgent achieves over 95% accuracy, reaching 100% when paired with fine-tuned agents, and improves accuracy by up to 26% compared to baseline methods. The extraction of reusable invariants provides a practical and scalable alternative to traditional LLM specialization, enabling the development of more reliable agents for real-world network management.<\/jats:p>","DOI":"10.1145\/3771567","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T20:07:03Z","timestamp":1764706023000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["MeshAgent: Enabling Reliable Network Management with Large Language Models"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6932-1038","authenticated-orcid":false,"given":"Yajie","family":"Zhou","sequence":"first","affiliation":[{"name":"University of Maryland, College Park, MD, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4154-4525","authenticated-orcid":false,"given":"Kevin","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2776-6917","authenticated-orcid":false,"given":"Sathiya Kumaran","family":"Mani","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, WA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9494-6435","authenticated-orcid":false,"given":"Srikanth","family":"Kandula","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Redmond, WA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9119-1679","authenticated-orcid":false,"given":"Zaoxing","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Maryland, College Park, MD, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","unstructured":"Rohan Anil Andrew M. Dai Orhan Firat Melvin Johnson Dmitry Lepikhin Alexandre Passos Siamak Shakeri Emanuel Taropa Paige Bailey Zhifeng Chen Eric Chu Jonathan H. Clark Laurent El Shafey Yanping Huang Kathy Meier-Hellstern Gaurav Mishra Erica Moreira Mark Omernick Kevin Robinson Sebastian Ruder Yi Tay Kefan Xiao Yuanzhong Xu Yujing Zhang Gustavo Hern\u00e1ndez \u00c1brego Junwhan Ahn Jacob Austin Paul Barham Jan A. Botha James Bradbury Siddhartha Brahma Kevin Brooks Michele Catasta Yong Cheng Colin Cherry Christopher A. Choquette-Choo Aakanksha Chowdhery Cl\u00e9ment Crepy Shachi Dave Mostafa Dehghani Sunipa Dev Jacob Devlin Mark D\u00edaz Nan Du Ethan Dyer Vladimir Feinberg Fangxiaoyu Feng Vlad Fienber Markus Freitag Xavier Garcia Sebastian Gehrmann Lucas Gonzalez and et al. 2023. PaLM 2 Technical Report. CoRR Vol. abs\/2305.10403 (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.10403","DOI":"10.48550\/arXiv.2305.10403"},{"key":"e_1_2_1_2_1","volume-title":"Program Synthesis with Large Language Models. CoRR","author":"Austin Jacob","year":"2021","unstructured":"Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 2021. Program Synthesis with Large Language Models. CoRR, Vol. abs\/2108.07732 (2021). https:\/\/arxiv.org\/abs\/2108.07732"},{"key":"e_1_2_1_3_1","unstructured":"Victor Bahl. 2024. Empowering operators through generative AI technologies with Azure for Operators. https:\/\/azure.microsoft.com\/en-us\/blog\/empowering-operators-through-generative-ai-technologies\u00adwith-azure-for-operators\/."},{"key":"e_1_2_1_4_1","unstructured":"Yuntao Bai Saurav Kadavath Sandipan Kundu Amanda Askell Jackson Kernion Andy Jones Anna Chen Anna Goldie Azalia Mirhoseini Cameron McKinnon et al. 2022. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 (2022)."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3591300"},{"key":"e_1_2_1_6_1","volume-title":"Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs. arXiv preprint arXiv:2406.01943","author":"Brown Nik Bear","year":"2024","unstructured":"Nik Bear Brown. 2024. Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs. arXiv preprint arXiv:2406.01943 (2024)."},{"key":"e_1_2_1_7_1","unstructured":"Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.12712"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.16749"},{"key":"e_1_2_1_10_1","volume-title":"CodeT: Code Generation with Generated Tests. CoRR","author":"Chen Bei","year":"2022","unstructured":"Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022. CodeT: Code Generation with Generated Tests. CoRR, Vol. abs\/2207.10397 (2022)."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2304.05128"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627703.3629553"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","unstructured":"Aakanksha Chowdhery Sharan Narang Jacob Devlin Maarten Bosma Gaurav Mishra Adam Roberts Paul Barham Hyung Won Chung Charles Sutton Sebastian Gehrmann Parker Schuh Kensen Shi Sasha Tsvyashchenko Joshua Maynez Abhishek Rao Parker Barnes Yi Tay Noam Shazeer Vinodkumar Prabhakaran Emily Reif Nan Du Ben Hutchinson Reiner Pope James Bradbury Jacob Austin Michael Isard Guy Gur-Ari Pengcheng Yin Toju Duke Anselm Levskaya Sanjay Ghemawat Sunipa Dev Henryk Michalewski Xavier Garcia Vedant Misra Kevin Robinson Liam Fedus Denny Zhou Daphne Ippolito David Luan Hyeontaek Lim Barret Zoph Alexander Spiridonov Ryan Sepassi David Dohan Shivani Agrawal Mark Omernick Andrew M. Dai Thanumalayan Sankaranarayana Pillai Marie Pellat Aitor Lewkowycz Erica Moreira Rewon Child Oleksandr Polozov Katherine Lee Zongwei Zhou Xuezhi Wang Brennan Saeta Mark Diaz Orhan Firat Michele Catasta Jason Wei Kathy Meier-Hellstern Douglas Eck Jeff Dean Slav Petrov and Noah Fiedel. 2022. PaLM: Scaling Language Modeling with Pathways. CoRR Vol. abs\/2204.02311 (2022). https:\/\/doi.org\/10.48550\/arXiv.2204.02311","DOI":"10.48550\/arXiv.2204.02311"},{"key":"e_1_2_1_14_1","volume-title":"Training Verifiers to Solve Math Word Problems. CoRR","author":"Cobbe Karl","year":"2021","unstructured":"Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. Training Verifiers to Solve Math Word Problems. CoRR, Vol. abs\/2110.14168 (2021). https:\/\/arxiv.org\/abs\/2110.14168"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1571941.1572114"},{"key":"e_1_2_1_16_1","volume-title":"Building guardrails for large language models. arXiv preprint arXiv:2402.01822","author":"Dong Yi","year":"2024","unstructured":"Yi Dong, Ronghui Mu, Gaojie Jin, Yi Qi, Jinwei Hu, Xingyu Zhao, Jie Meng, Wenjie Ruan, and Xiaowei Huang. 2024. Building guardrails for large language models. arXiv preprint arXiv:2402.01822 (2024)."},{"key":"e_1_2_1_17_1","volume-title":"Don't Hallucinate","author":"Feng Shangbin","year":"2024","unstructured":"Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, and Yulia Tsvetkov. 2024. Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration. arXiv preprint arXiv:2402.00367 (2024)."},{"key":"e_1_2_1_18_1","unstructured":"FireMon. [n.d.]. One Simple Misconfiguration: 2.9 Billion Users Down. https:\/\/www.firemon.com\/blog\/one-simple-misconfiguration-2-9-billion-users-down\/?t. Accessed: 2025-01-30."},{"key":"e_1_2_1_19_1","volume-title":"Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997","author":"Gao Yunfan","year":"2023","unstructured":"Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023)."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-013-0282-8"},{"key":"e_1_2_1_21_1","unstructured":"Google. [n.d.]. MALT example models. https:\/\/github.com\/google\/malt-example-models Retrieved on 2023-06."},{"key":"e_1_2_1_22_1","unstructured":"Google. 2024. Gemini AI for developers. https:\/\/ai.google.dev\/."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230555"},{"key":"e_1_2_1_24_1","volume-title":"Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al.","author":"Gupta Aman","year":"2024","unstructured":"Aman Gupta, Anup Shirgaonkar, Angels de Luis Balaguer, Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al., 2024. RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture. arXiv preprint arXiv:2401.08406 (2024)."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626111.3628176"},{"key":"e_1_2_1_26_1","volume-title":"Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study. arXiv preprint arXiv:2505.02502","author":"Hou Xinyi","year":"2025","unstructured":"Xinyi Hou, Jiahao Han, Yanjie Zhao, and Haoyu Wang. 2025. Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study. arXiv preprint arXiv:2505.02502 (2025)."},{"key":"e_1_2_1_27_1","volume-title":"LoRA: Low-Rank Adaptation of Large Language Models. In The Tenth International Conference on Learning Representations (ICLR).","author":"Hu Edward J.","year":"2022","unstructured":"Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In The Tenth International Conference on Learning Representations (ICLR)."},{"key":"e_1_2_1_28_1","volume-title":"Large language models for networking: Applications, enabling techniques, and challenges","author":"Huang Yudong","year":"2024","unstructured":"Yudong Huang, Hongyang Du, Xinyuan Zhang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shuo Wang, and Tao Huang. 2024. Large language models for networking: Applications, enabling techniques, and challenges. IEEE Network (2024)."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1298306.1298349"},{"key":"e_1_2_1_30_1","first-page":"625","volume-title":"2021 USENIX Annual Technical Conference (USENIX ATC 21)","author":"Jacobs Arthur S","year":"2021","unstructured":"Arthur S Jacobs, Ricardo J Pfitscher, Rafael H Ribeiro, Ronaldo A Ferreira, Lisandro Z Granville, Walter Willinger, and Sanjay G Rao. 2021. Hey, lumi! using natural language for {intent-based} network management. In 2021 USENIX Annual Technical Conference (USENIX ATC 21). 625-639."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626111.3628191"},{"key":"e_1_2_1_32_1","volume-title":"Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip HS Torr, Fahad Shahbaz Khan, and Salman Khan.","author":"Kumar Komal","year":"2025","unstructured":"Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip HS Torr, Fahad Shahbaz Khan, and Salman Khan. 2025. Llm post-training: A deep dive into reasoning large language models. arXiv preprint arXiv:2502.21321 (2025)."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2069216.2069227"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2014.03.007"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2022.3215919"},{"key":"e_1_2_1_36_1","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis Patrick","year":"2020","unstructured":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fcttler, Mike Lewis, Wen-tau Yih, Tim Rockt\u00e4schel, et al., 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9459-9474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","unstructured":"Yujia Li David H. Choi Junyoung Chung Nate Kushman Julian Schrittwieser R\u00e9mi Leblond Tom Eccles James Keeling Felix Gimeno Agustin Dal Lago Thomas Hubert Peter Choy Cyprien de Masson d'Autume Igor Babuschkin Xinyun Chen Po-Sen Huang Johannes Welbl Sven Gowal Alexey Cherepanov James Molloy Daniel J. Mankowitz Esme Sutherland Robson Pushmeet Kohli Nando de Freitas Koray Kavukcuoglu and Oriol Vinyals. 2022. Competition-Level Code Generation with AlphaCode. CoRR Vol. abs\/2203.07814 (2022). https:\/\/doi.org\/10.48550\/arXiv.2203.07814","DOI":"10.48550\/arXiv.2203.07814"},{"key":"e_1_2_1_38_1","volume-title":"Data-efficient Fine-tuning for LLM-based Recommendation. arXiv preprint arXiv:2401.17197","author":"Lin Xinyu","year":"2024","unstructured":"Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, and Tat-Seng Chua. 2024. Data-efficient Fine-tuning for LLM-based Recommendation. arXiv preprint arXiv:2401.17197 (2024)."},{"key":"e_1_2_1_39_1","unstructured":"Aixin Liu Bei Feng Bing Xue Bingxuan Wang Bochao Wu Chengda Lu Chenggang Zhao Chengqi Deng Chenyu Zhang Chong Ruan et al. 2024a. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437 (2024)."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3613905.3650756"},{"key":"e_1_2_1_41_1","volume-title":"Lost in the Middle: How Language Models Use Long Contexts. CoRR","author":"Liu Nelson F.","year":"2023","unstructured":"Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. Lost in the Middle: How Language Models Use Long Contexts. CoRR, Vol. abs\/2307.03172 (2023)."},{"key":"e_1_2_1_42_1","unstructured":"LlamaIndex. 2024. Building Performant RAG Applications for Production. https:\/\/docs.llamaindex.ai\/en\/stable\/optimizing\/production_rag\/."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2025.3526606"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626111.3628198"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626111.3628183"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.173"},{"key":"e_1_2_1_47_1","unstructured":"Microsoft. [n.d.]. Starter Resource Graph query samples. https:\/\/learn.microsoft.com\/en-us\/azure\/governance\/resource-graph\/samples\/starter Retrieved on 2023-12."},{"key":"e_1_2_1_48_1","volume-title":"Azure Resource Graph documentation. https:\/\/learn.microsoft.com\/en-us\/azure\/governance\/resource-graph\/ Retrieved","year":"2023","unstructured":"Microsoft. 2023. Azure Resource Graph documentation. https:\/\/learn.microsoft.com\/en-us\/azure\/governance\/resource-graph\/ Retrieved December 2023."},{"key":"e_1_2_1_49_1","unstructured":"Microsoft. 2024. Azure OpenAI Service. https:\/\/azure.microsoft.com\/en-us\/products\/ai-services\/openai-service."},{"key":"e_1_2_1_50_1","volume-title":"USENIX Symposium on Networked Systems Design and Implementation (NSDI). https:\/\/www.usenix.org\/conference\/nsdi20\/presentation\/mogul","author":"Mogul Jeffrey C.","year":"2020","unstructured":"Jeffrey C. Mogul, Drago Goricanec, Martin Pool, Anees Shaikh, Douglas Turk, Bikash Koley, and Xiaoxue Zhao. 2020. Experiences with Modeling Network Topologies at Multiple Levels of Abstraction. In USENIX Symposium on Networked Systems Design and Implementation (NSDI). https:\/\/www.usenix.org\/conference\/nsdi20\/presentation\/mogul"},{"key":"e_1_2_1_51_1","unstructured":"OpenAI. [n.d.]. Introducing OpenAI o1-preview. https:\/\/openai.com\/index\/introducing-openai-o1-preview\/."},{"key":"e_1_2_1_52_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. CoRR Vol. abs\/2303.08774 (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.08774"},{"key":"e_1_2_1_53_1","unstructured":"OpenAI. 2024. Hello GPT-4o. https:\/\/openai.com\/index\/hello-gpt-4o\/."},{"key":"e_1_2_1_54_1","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS).","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. In Annual Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_1_55_1","volume-title":"Learning from few examples: A summary of approaches to few-shot learning. arXiv preprint arXiv:2203.04291","author":"Parnami Archit","year":"2022","unstructured":"Archit Parnami and Minwoo Lee. 2022. Learning from few examples: A summary of approaches to few-shot learning. arXiv preprint arXiv:2203.04291 (2022)."},{"key":"e_1_2_1_56_1","volume-title":"Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang.","author":"Parvez Md Rizwan","year":"2021","unstructured":"Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Retrieval augmented code generation and summarization. arXiv preprint arXiv:2108.11601 (2021)."},{"key":"e_1_2_1_57_1","unstructured":"Sasha Ratkovic. 2017. What is Intent-Based Networking? https:\/\/blogs.juniper.net\/en-us\/enterprise-cloud-and-transformation\/what-is-intent-based-networking."},{"key":"e_1_2_1_58_1","volume-title":"A Survey of Hallucination in Large Foundation Models. CoRR","author":"Rawte Vipula","year":"2023","unstructured":"Vipula Rawte, Amit P. Sheth, and Amitava Das. 2023. A Survey of Hallucination in Large Foundation Models. CoRR, Vol. abs\/2309.05922 (2023)."},{"key":"e_1_2_1_59_1","volume-title":"Innovation Insight: Intent-Based Networking Systems. https:\/\/www.gartner.com\/en\/documents\/3599617.","author":"Research Gartner","year":"2017","unstructured":"Gartner Research. 2017. Innovation Insight: Intent-Based Networking Systems. https:\/\/www.gartner.com\/en\/documents\/3599617."},{"key":"e_1_2_1_60_1","volume-title":"Exploring LLM-based Agents for Root Cause Analysis. arXiv preprint arXiv:2403.04123","author":"Roy Devjeet","year":"2024","unstructured":"Devjeet Roy, Xuchao Zhang, Rashi Bhave, Chetan Bansal, Pedro Las-Casas, Rodrigo Fonseca, and Saravan Rajmohan. 2024. Exploring LLM-based Agents for Root Cause Analysis. arXiv preprint arXiv:2403.04123 (2024)."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626111.3628205"},{"key":"e_1_2_1_62_1","unstructured":"Prashanth Shenoy. 2017. Journey to an Intent-based Network. https:\/\/blogs.cisco.com\/networking\/journey-to-an-intent-based-network."},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). https:\/\/aclanthology.org\/2022","author":"Shi Freda","unstructured":"Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I. Wang. 2022. Natural Language to Code Translation with Execution. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). https:\/\/aclanthology.org\/2022.emnlp-main.231"},{"key":"e_1_2_1_64_1","volume-title":"Reflexion: Language Agents with Verbal Reinforcement Learning. CoRR","author":"Shinn Noah","year":"2023","unstructured":"Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023. Reflexion: Language Agents with Verbal Reinforcement Learning. CoRR, Vol. abs\/2303.11366 (2023)."},{"key":"e_1_2_1_65_1","unstructured":"SIFF. [n.d.]. Configuration Outages That We Should Be Learning From. https:\/\/www.siff.io\/configuration-outages-that-we-should-be-learning-from\/?t. Accessed: 2025-01-30."},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","unstructured":"Karan Singhal Shekoofeh Azizi Tao Tu S. Sara Mahdavi Jason Wei Hyung Won Chung Nathan Scales Ajay Kumar Tanwani Heather Cole-Lewis Stephen Pfohl Perry Payne Martin Seneviratne Paul Gamble Chris Kelly Nathaneal Sch\u00e4rli Aakanksha Chowdhery Philip Andrew Mansfield Blaise Ag\u00fcera y Arcas Dale R. Webster Gregory S. Corrado Yossi Matias Katherine Chou Juraj Gottweis Nenad Tomasev Yun Liu Alvin Rajkomar Joelle K. Barral Christopher Semturs Alan Karthikesalingam and Vivek Natarajan. 2022. Large Language Models Encode Clinical Knowledge. CoRR Vol. abs\/2212.13138 (2022). https:\/\/doi.org\/10.48550\/arXiv.2212.13138","DOI":"10.48550\/arXiv.2212.13138"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102538"},{"key":"e_1_2_1_68_1","unstructured":"Gemini Team Rohan Anil Sebastian Borgeaud Yonghui Wu Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M Dai Anja Hauth et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)."},{"key":"e_1_2_1_69_1","volume-title":"Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al.","author":"Team Gemini","year":"2024","unstructured":"Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al., 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530 (2024)."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2302.13971"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551841"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3656296"},{"key":"e_1_2_1_73_1","volume-title":"21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)","author":"Wang Haopei","year":"2024","unstructured":"Haopei Wang, Anubhavnidhi Abhashkumar, Changyu Lin, Tianrong Zhang, Xiaoming Gu, Ning Ma, Chang Wu, Songlin Liu, Wei Zhou, Yongbin Dong, et al., 2024a. {NetAssistant}: Dialogue Based Network Diagnosis in Data Center Networks. In 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24). 2011-2024."},{"key":"e_1_2_1_74_1","volume-title":"The Tenth International Conference on Learning Representations (ICLR).","author":"Wei Jason","unstructured":"Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022a. Finetuned Language Models are Zero-Shot Learners. In The Tenth International Conference on Learning Representations (ICLR)."},{"key":"e_1_2_1_75_1","volume-title":"Quoc V. Le, and Denny Zhou.","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022b. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems (NeurIPS). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3651890.3672268"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626111.3628189"},{"key":"e_1_2_1_78_1","volume-title":"Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. arXiv preprint arXiv:2306.13063","author":"Xiong Miao","year":"2023","unstructured":"Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2023. Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. arXiv preprint arXiv:2306.13063 (2023)."},{"key":"e_1_2_1_79_1","volume-title":"Kankanhalli","author":"Xu Ziwei","year":"2024","unstructured":"Ziwei Xu, Sanjay Jain, and Mohan S. Kankanhalli. 2024a. Hallucination is Inevitable: An Innate Limitation of Large Language Models. CoRR, Vol. abs\/2401.11817 (2024)."},{"key":"e_1_2_1_80_1","volume-title":"LLM Jailbreak Attack versus Defense Techniques-A Comprehensive Study. arXiv preprint arXiv:2402.13457","author":"Xu Zihao","year":"2024","unstructured":"Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li, and Stjepan Picek. 2024b. LLM Jailbreak Attack versus Defense Techniques-A Comprehensive Study. arXiv preprint arXiv:2402.13457 (2024)."},{"key":"e_1_2_1_81_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yao Shunyu","year":"2024","unstructured":"Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_2_1_82_1","volume-title":"React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629","author":"Yao Shunyu","year":"2022","unstructured":"Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629 (2022)."},{"key":"e_1_2_1_83_1","volume-title":"Language agent tree search unifies reasoning acting and planning in language models. arXiv preprint arXiv:2310.04406","author":"Zhou Andy","year":"2023","unstructured":"Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. 2023a. Language agent tree search unifies reasoning acting and planning in language models. arXiv preprint arXiv:2310.04406 (2023)."},{"key":"e_1_2_1_84_1","volume-title":"NetPress: Dynamically Generated LLM Benchmarks for Network Applications. arXiv preprint arXiv:2506.03231","author":"Zhou Yajie","year":"2025","unstructured":"Yajie Zhou, Jiajun Ruan, Eric S Wang, Sadjad Fouladi, Francis Y Yan, Kevin Hsieh, and Zaoxing Liu. 2025. NetPress: Dynamically Generated LLM Benchmarks for Network Applications. arXiv preprint arXiv:2506.03231 (2025)."},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387514.3406214"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626111.3628212"}],"container-title":["Proceedings of the ACM on Measurement and Analysis of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3771567","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:25:47Z","timestamp":1764782747000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3771567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":86,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1145\/3771567"],"URL":"https:\/\/doi.org\/10.1145\/3771567","relation":{},"ISSN":["2476-1249"],"issn-type":[{"value":"2476-1249","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"2025-12-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}