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Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring
Sep 22, 2026Imad BuljićRoot Cause Analysis Microservices
Polytechnic Faculty, University of Zenica
GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
Aug 10, 2026Yifang Tian, Yaming Liu, Zichun Chong +3Microservices Root Cause Analysis
University of Toronto Toronto, Canada
From Textual Requirements to Microservice Architectures - A Comprehensive Evaluation of LLM-Based Design Synthesis
Jul 30, 2026Danyllo Albuquerque, José Renan, Guillermo Rodríguez +6Microservices Invariant Synthesis
VIRTUS/UFCG Federal University of Campina Grande (UFCG), Paraiba Brazil · Federal University of Campina Grande (UFCG), Paraiba Brazil · Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN), Argentina +2
Safe Remediation as Risk-Constrained Intervention Decision in Microservice Systems
Jul 22, 2026Chengxiao Dai, Zhaokun Yan, Chenjun Lei +2Remediation Microservices
School of Computer Science, University of Sydney, Sydney, Australia · School of Computation, Information and Technology, Technical University of Munich, Munich, Germany · College of Business and Economics, Australian National University, Canberra, Australia +2
FailureAtlas: A Taxonomy of Failure Modes in Multi-Provider LLM Serving Infrastructure
Jul 20, 2026Vishal Pandey, Gopal SinghLatent Failure Patterns Large Language Models Fail
Metriqual London, UK · Metriqual Athens, GR
How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
Jul 15, 2026Athira Gopal, Ashwanth KrishnanRoot Cause Analysis Microservices
QPIAI India
OpsMem: Dual-Memory Reasoning with Cross-Memory Resonance for Failure Diagnosis
Jul 13, 2026Yongqian Sun, Rongchen Gao, Yu Luo +6Microservices Nativemem
Nankai University · Huawei Technologies Co., Ltd. · Tsinghua University
A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis
Jun 28, 2026Yuanhong Cai, Xiaohui Nie, Kanglin Yin +8Microservices
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · Key Laboratory for Satellite Digitalization Technology, Chinese Academy of Sciences, Shanghai, China · Nankai University, Tianjin, China +2
A Topology-Aware, Memory-Centric Architecture that Separates Root-Cause Derivation from Root-Cause Explanation
Jun 18, 2026Momil SeedatMicroservices Root Cause Analysis
Independent Research Prototype
Facial Affect Analysis for Service-Oriented Systems: Advances, Challenges, and Future Visions
Jun 13, 2026Spyridon Georgiou, Aggelos Psiris, Thomas Lagkas +4Facial Expression Recognition Microservices
Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece · Department of Informatics, Faculty of Science, Democritus University of Thrace, Kavala, Greece · Department of Networks and Digital Media, Kingston University, Kingston upon Thames, United Kingdom +1
Anomaly Detection and Root Cause Analysis for Microservice Systems
Jun 8, 2026Luan PhamMicroservices Root Cause Analysis
Viet Nam National University Ho Chi Minh City - University of Technology (HCMUT) · School of Computing Technologies, College of Science, Technology, Engineering and Maths, Royal Melbourne Institute of Technology (RMIT University), Melbourne, Victoria, Australia
ICAN-Deploy: Identity-Stable Canary Deployment for Safety-Critical Embodied Agents
May 27, 2026Xue Qin, Simin Luan, John See +3Agentic Deployments Microservices
Harbin Institute of Technology School of Software Harbin, China
STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms
May 25, 2026Ahmed Abdulaal, Maruf Aytekin, Thilaga kumaran Srinivasan +1Rate Scaling Cloud
Walmart Global Tech Sunnyvale, California, USA
Causely: A Causal Intelligence Layer for Enterprise AI A Benchmark Study on SRE and Reliability Workflows
May 18, 2026Dhairya Dalal, Endre Sara, Ben Yemini +2Root Cause Analysis Microservices
Causely
TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
May 15, 2026Junle Wang, Xingchuang Liao, Wenjun WuRoot Cause Analysis Microservices
School of Artificial Intelligence, Beihang University Beijing, China
STAR: A Stage-attributed Triage and Repair framework for RCA Agents in Microservices
May 15, 2026Junle Wang, Xingchuang Liao, Wenjun WuRoot Cause Analysis Microservices
School of Artificial Intelligence, Beihang University
Detecting Privilege Escalation in Polyglot Microservices via Agentic Program Analysis
May 15, 2026Penghui Li, Hong Yau Chong, Yinzhi Cao +1Program Analysis Microservices
Columbia University · Johns Hopkins University
Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought
May 14, 2026Lingzhe Zhang, Tong Jia, Kangjin Wang +8Microservices Root Cause Analysis
Peking University, Beijing, China · Huawei Theory Lab, China
LLM-Based Robustness Testing of Microservice Applications: An Empirical Study
May 13, 2026Hrushitha Goud Tigulla, Marco VieiraMicroservices Robustness Verification
College of Computing and Informatics University of North Carolina at Charlotte Charlotte, USA
Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
May 12, 2026Yao Fehlis, Benjamin Bengfort, Zhangzhang Si +9Optical Character Recognition Large Language Model Pipelines
KG-First, LLM-Fallback: A Hybrid Microservice for Grounded Skill Search and Explanation
May 2, 2026Ngoc Luyen Le, Marie-Hélène Abel, Bertrand LaforgeSkills Knowledge Graphs
Gamaizer.ia, 93340 Le Raincy, France · Université de technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60319 - 60203 Compiègne Cedex, France · Sorbonne Université, CNRS UMR 7585, LPMHE (Laboratoire de Physique Nucléaire et des Hautes Énergies), 75252 Paris cedex 05, France
Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices
May 1, 2026Xin Liu, Yuhang He, Sichen Zhao +2Root Cause Analysis Microservices
Independent Researcher New York, USA · Independent Researcher Chicago, USA · Northeastern University Boston, USA +2
Think it, Run it: Autonomous ML pipeline generation via self-healing multi-agent AI
Apr 29, 2026Adela Bara, Gabriela Dobrita, Simona-Vasilica OpreaData Generation Pipelines Microservices
Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, no.6 Piața Romană, Bucharest, 010374, Romania
KAYRA: A Microservice Architecture for AI-Assisted Karyotyping with Cloud and On-Premise Deployment
Apr 29, 2026Attila Pintér, Javier Rico, Attila Répai +5Microservices Cloud
Jedlik Innovation Ltd., Budapest, Hungary · Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary · Central Hospital of Southern Pest — National Institute of Hematology and Infectious Diseases, Laboratory of Molecular Genetics, Budapest, Hungary
STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices
Apr 29, 2026Yongliang Ding, Qigong Bi, Peng PuMultiplex Graph Transformers Microservices
East China Normal University Shanghai, China

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Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring

Imad Buljić

Polytechnic Faculty, University of Zenica

Root Cause Analysis Microservices Graph Representations Graph Neural Networks Ablation

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Graph neural networks dominate recent work on microservice root-cause analysis, yet recent results question whether the graph contributes. Those results compare whole pipelines, so when a flat model wins one cannot tell whether structure is useless or redundant. We run the comparison they imply on RCAEval: three learned arms with identical features, optimiser, validation split, early-stopping rule and scoring head, in which a single term separates the graph arms. Across two RCAEval benchmarks, two topology sources and four regimes we find no reliable graph-specific effect: in-distribution the graph model leads the conventional flat model by 0.003 Avg@5 (p = 0.844, n = 6 disjoint folds). Auditing the pipeline surfaced two benchmark properties that condition any result on it. RCAEval injects faults into only five services per system while exposing 12 to 70 in telemetry, and the headline metric is Avg@5: a ranker reading no telemetry at all places the true culprit in the top five on 99.7 percent of held-out incidents, scoring Avg@5 0.488. That prior, not the uniform-random 0.137, is the honest in-distribution floor, and it collapses to 0.192 across systems. The second property is a non-uniform column schema that silently zeroes telemetry for most RE1 cases. We reproduce a published baseline, BARO, RCAEval's own reference implementation; on the one system with a clean schema it reaches similar aggregate accuracy to our heuristic, within 0.004, under a different scoring rule. The audit motivated a new model. PSC-GRCA separates a candidate score into a system prior, telemetry evidence and a centred graph residual, and reaches mean Avg@5 0.915 against 0.864 for the flat baseline, while its ablations locate most of the gain in the prior term rather than the graph. We close with a twelve-item checklist for graph-versus-flat ablation studies, distilled from sixty-two recorded defects.

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