{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:22:35Z","timestamp":1781713355504,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["241711KYSB20200023"],"award-info":[{"award-number":["241711KYSB20200023"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202445"],"award-info":[{"award-number":["62202445"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China-Research Grants Council (RGC) Joint Research Scheme","award":["62321166652"],"award-info":[{"award-number":["62321166652"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,8]]},"DOI":"10.1145\/3701716.3715225","type":"proceedings-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T16:20:01Z","timestamp":1748017201000},"page":"422-431","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9288-4787","authenticated-orcid":false,"given":"Changhua","family":"Pei","sequence":"first","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China and Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8534-4909","authenticated-orcid":false,"given":"Zexin","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China, and ByteDance, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7578-0492","authenticated-orcid":false,"given":"Fengrui","family":"Liu","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3529-5879","authenticated-orcid":false,"given":"Zeyan","family":"Li","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1178-5506","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7959-2157","authenticated-orcid":false,"given":"Xiao","family":"He","sequence":"additional","affiliation":[{"name":"ByteDance, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8449-0223","authenticated-orcid":false,"given":"Rong","family":"Kang","sequence":"additional","affiliation":[{"name":"ByteDance, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2250-5528","authenticated-orcid":false,"given":"Tieying","family":"Zhang","sequence":"additional","affiliation":[{"name":"ByteDance, San Jose, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-892X","authenticated-orcid":false,"given":"Jianjun","family":"Chen","sequence":"additional","affiliation":[{"name":"ByteDance, San Jose, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6253-9808","authenticated-orcid":false,"given":"Jianhui","family":"Li","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4964-1135","authenticated-orcid":false,"given":"Gaogang","family":"Xie","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5113-838X","authenticated-orcid":false,"given":"Dan","family":"Pei","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00031"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583274"},{"key":"e_1_3_2_2_3_1","volume-title":"MicroFI: Non-Intrusive and Prioritized Request-Level Fault Injection for Microservice Applications","author":"Chen Hongyang","year":"2024","unstructured":"Hongyang Chen, Pengfei Chen, Guangba Yu, Xiaoyun Li, Zilong He, and Huxing Zhang. 2024a. MicroFI: Non-Intrusive and Prioritized Request-Level Fault Injection for Microservice Applications. IEEE Transactions on Dependable and Secure Computing (2024)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627703.3629553"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3613864"},{"key":"e_1_3_2_2_6_1","volume-title":"Zijuan Lin, Liyang Zhou, et al.","author":"Hong Sirui","year":"2023","unstructured":"Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al. 2023. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352 (2023)."},{"key":"e_1_3_2_2_7_1","first-page":"31158","article-title":"Root cause analysis of failures in microservices through causal discovery","volume":"35","author":"Ikram Azam","year":"2022","unstructured":"Azam Ikram, Sarthak Chakraborty, Subrata Mitra, Shiv Saini, Saurabh Bagchi, and Murat Kocaoglu. 2022a. Root cause analysis of failures in microservices through causal discovery. Advances in Neural Information Processing Systems, Vol. 35 (2022), 31158--31170.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_8_1","first-page":"31158","article-title":"Root cause analysis of failures in microservices through causal discovery","volume":"35","author":"Ikram Azam","year":"2022","unstructured":"Azam Ikram, Sarthak Chakraborty, Subrata Mitra, Shiv Saini, Saurabh Bagchi, and Murat Kocaoglu. 2022b. Root cause analysis of failures in microservices through causal discovery. Advances in Neural Information Processing Systems, Vol. 35 (2022), 31158--31170.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_9_1","volume-title":"Sung Ju Hwang, and Jong C Park","author":"Jeong Soyeong","year":"2024","unstructured":"Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C Park. 2024. Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity. arXiv preprint arXiv:2403.14403 (2024)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583338"},{"key":"e_1_3_2_2_11_1","unstructured":"k8sgpt ai. 2023. k8sgpt. https:\/\/github.com\/k8sgpt-ai\/k8sgpt."},{"key":"e_1_3_2_2_12_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Kocaoglu Murat","year":"2019","unstructured":"Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, and Elias Bareinboim. 2019. Characterization and learning of causal graphs with latent variables from soft interventions. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_13_1","unstructured":"Labring. 2023. FastGPT. https:\/\/github.com\/labring\/FastGPT."},{"key":"e_1_3_2_2_14_1","volume-title":"Chain of code: Reasoning with a language model-augmented code emulator. arXiv preprint arXiv:2312.04474","author":"Li Chengshu","year":"2023","unstructured":"Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, and Brian Ichter. 2023. Chain of code: Reasoning with a language model-augmented code emulator. arXiv preprint arXiv:2312.04474 (2023)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549092"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27772"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE5003.2020.00014"},{"key":"e_1_3_2_2_18_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Misiakos Panagiotis","year":"2024","unstructured":"Panagiotis Misiakos, Chris Wendler, and Markus P\u00fcschel. 2024. Learning DAGs from data with few root causes. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_19_1","volume-title":"Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning. arXiv preprint arXiv:2305.12295","author":"Pan Liangming","year":"2023","unstructured":"Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang. 2023. Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning. arXiv preprint arXiv:2305.12295 (2023)."},{"key":"e_1_3_2_2_20_1","volume-title":"Toolllm: Facilitating large language models to master 16000 real-world apis. arXiv preprint arXiv:2307.16789","author":"Qin Yujia","year":"2023","unstructured":"Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. 2023. Toolllm: Facilitating large language models to master 16000 real-world apis. arXiv preprint arXiv:2307.16789 (2023)."},{"key":"e_1_3_2_2_21_1","unstructured":"Alec Radford. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_3_2_2_22_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9."},{"key":"e_1_3_2_2_23_1","unstructured":"robusta dev. 2024. holmesgpt. https:\/\/github.com\/robusta-dev\/holmesgpt."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3382494.3410684"},{"key":"e_1_3_2_2_25_1","volume-title":"Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom.","author":"Schick Timo","year":"2024","unstructured":"Timo Schick, Jane Dwivedi-Yu, Roberto Dess`i, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2024. Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3603269.3604823"},{"key":"e_1_3_2_2_27_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Shinn Noah","year":"2024","unstructured":"Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645665"},{"key":"e_1_3_2_2_29_1","volume-title":"Tranad: Deep transformer networks for anomaly detection in multivariate time series data. arXiv preprint arXiv:2201.07284","author":"Tuli Shreshth","year":"2022","unstructured":"Shreshth Tuli, Giuliano Casale, and Nicholas R Jennings. 2022. Tranad: Deep transformer networks for anomaly detection in multivariate time series data. arXiv preprint arXiv:2201.07284 (2022)."},{"key":"e_1_3_2_2_30_1","volume-title":"Large Language Models Can Provide Accurate and Interpretable Incident Triage. In 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 523--534","author":"Wang Zexin","year":"2024","unstructured":"Zexin Wang, Jianhui Li, Minghua Ma, Ze Li, Yu Kang, Chaoyun Zhang, Chetan Bansal, Murali Chintalapati, Saravan Rajmohan, Qingwei Lin, et al. 2024a. Large Language Models Can Provide Accurate and Interpretable Incident Triage. In 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 523--534."},{"key":"e_1_3_2_2_31_1","volume-title":"Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models. arXiv preprint arXiv:2310.16340","author":"Wang Zefan","year":"2023","unstructured":"Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong, Lunting Fan, Lingfei Wu, and Qingsong Wen. 2023. Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models. arXiv preprint arXiv:2310.16340 (2023)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645710"},{"key":"e_1_3_2_2_33_1","volume-title":"Denny Zhou, et al.","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, Vol. 35 (2022), 24824--24837."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS47738.2020.9110353"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185996"},{"key":"e_1_3_2_2_36_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_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3663529.3663827"},{"key":"e_1_3_2_2_38_1","volume-title":"SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces. In 2024 IEEE 35st International Symposium on Software Reliability Engineering (ISSRE).","author":"Yao Zhenhe","year":"2024","unstructured":"Zhenhe Yao, Haowei Ye, Changhua Pei, Guang Cheng, Guangpei Wang, Zhiwei Liu, Hongwei Chen, Hang Cui, Zeyan Li, Jianhui Li, et al. 2024b. SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces. In 2024 IEEE 35st International Symposium on Software Reliability Engineering (ISSRE)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449905"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3616249"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Wei Zhang Hongcheng Guo Jian Yang Yi Zhang Chaoran Yan Zhoujin Tian Hangyuan Ji Zhoujun Li Tongliang Li Tieqiao Zheng et al. 2024. mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture. arXiv preprint arXiv:2404.12135 (2024).","DOI":"10.18653\/v1\/2024.findings-emnlp.232"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645442"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.14778\/3675034.3675043"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Companion Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715225","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701716.3715225","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T03:04:33Z","timestamp":1759892673000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":43,"alternative-id":["10.1145\/3701716.3715225","10.1145\/3701716"],"URL":"https:\/\/doi.org\/10.1145\/3701716.3715225","relation":{},"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"2025-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}