error attribution

CI/CD pipeline failure diagnosis and repair have long suffered from high complexity and low automation. This paper introduces LogSage—the first end-to-end, LLM-driven framework for root-cause analysis (RCA) and automated repair. It features a novel two-stage LLM architecture: (1) Stage I employs intelligent log preprocessing to precisely localize failures; (2) Stage II integrates retrieval-augmented generation (RAG) with tool calling to generate executable, validated fixes. LogSage is the first industrial-grade solution validated on over one million production CI/CD pipelines. It achieves 98% RCA accuracy—12 percentage points higher than state-of-the-art baselines—and end-to-end repair accuracy exceeding 88%. Deployed at scale, it supported 1.07 million CI/CD executions in its first year, processing over 3,000 tasks daily.

Naval systems frequently exhibit anomalous behaviors due to wear, misuse, or component failures—challenges that hinder timely detection and precise remediation. To address this, we propose a predictive-diagnostic closed-loop framework that tightly integrates the existing failure prediction system PREVENT with a newly designed responsive troubleshooting module, REACT. Methodologically, the framework synergizes multi-source time-series anomaly detection with domain-knowledge-driven fault-isolation process modeling, enabling end-to-end automation—from anomaly alerting and root-cause localization to actionable remediation recommendations. Evaluated on operational shipboard systems deployed by Fincantieri, the framework reduces mean time to fault localization by 42%, significantly improves operational response efficiency, and demonstrates strong generalizability across diverse industrial domains.

The root cause analysis (RCA) community suffers from a critical shortage of large-scale, open-source, multimodal benchmark datasets, hindering rigorous method evaluation and advancement. To address this, we introduce LEMMA-RCA—the first cross-domain, multimodal, open-source RCA dataset tailored for IT/OT systems, encompassing realistic failure scenarios from microservices, water supply, and wastewater treatment. It comprises hundreds of system entities and fine-grained causal relationship annotations. Uniquely integrating four heterogeneous modalities—time-series metrics, logs, topology graphs, and alerts—it supports both offline/online and unimodal/multimodal RCA evaluation. Built via distributed monitoring, multi-source alignment, controllable fault injection, and causal graph annotation, the dataset ensures high fidelity and representativeness. Extensive evaluation across eight baseline methods demonstrates that multimodal joint modeling improves average F1-score by 23.6%. LEMMA-RCA is publicly released, establishing a new community benchmark for RCA research.

In industrial quality inspection, anomaly detection suffers from poor robustness due to high noise levels and sparse defective samples. To address this, we propose Iterative Refinement of Pseudo-labels (IRP), a self-supervised method that alternately evaluates sample credibility and removes misleading instances under feature-space consistency constraints—effectively purifying the training set dynamically without human annotations and generating high-fidelity self-supervised signals. IRP introduces the novel paradigm of “iterative data refinement,” significantly enhancing model robustness against label noise and cross-domain generalization capability. Evaluated on KSDD2 and MVTec AD benchmarks, IRP consistently outperforms existing unsupervised and self-supervised methods. Notably, under high-noise conditions, it achieves substantial improvements in detection accuracy and reduces false positive rates by over 25%.

Inaccurate root cause analysis (RCA) in distributed systems stems from incomplete fault reports on platforms like GitHub and JIRA, lacking sufficient code-level diagnostic context. Method: This paper proposes a code-knowledge-enhanced RCA framework that automatically extracts relevant code snippets via static analysis, reconstructs exception propagation paths and call contexts, and dynamically injects code-level diagnostic signals—including call chains, function signatures, and exception-handling logic—into large language model (LLM) inference to align problem descriptions with source-code semantics and enable collaborative reasoning. The method integrates execution-path reconstruction, multi-example prompt engineering, and generative LLM inference, ensuring cross-system and cross-model generalizability. Results: Evaluated on five real-world distributed-system datasets, the framework improves root cause localization accuracy by 28.3% and root cause summary quality by 22.0%, while maintaining robust performance across multiple mainstream LLMs.

This study addresses the challenge of effectively monitoring early-stage agent systems, where structural flaws often obscure task-level errors. The authors propose a three-dimensional (quality, suitability, efficiency) and three-granularity (intra-run, inter-run, structural) monitoring and triaging framework tailored for low-maturity agent systems. They introduce a novel system maturity staging model based on the coefficient of variation and monitoring granularity, integrated with a severity classification adapted from FMEA to guide human review. The resulting transferable monitoring architecture supports document-driven, multi-stage workflows, enhanced by a synthetic testbed with controlled error injection. Experimental results demonstrate that structural defects significantly mask task-level signals; 97% of issues can be automatically traced, with only 2% requiring human intervention, and each granularity level precisely identifies its corresponding defect type (coefficients of variation: 0.02, 1.25, and 0.00, respectively).

Predictive maintenance in manufacturing often relies on spurious correlations, hindering identification of true causal mechanisms underlying equipment failures and leading to misdiagnosis and inefficient interventions. To address this, we propose a causal machine learning–based decision framework that leverages a pre-trained causal foundation model as a “what-if” reasoning engine to systematically identify root causes and quantify their causal effects on Overall Equipment Effectiveness (OEE). Our method integrates causal inference modeling with intervention-effect estimation on semi-synthetic data, enabling interpretable and actionable ranking and recommendation of maintenance strategies. Experiments demonstrate that, compared to conventional predictive models, our framework significantly improves the accuracy of identifying effective interventions, increases average OEE by 12.3%, and reduces unnecessary downtime by 37.6%. This represents a critical transition from failure prediction to causally grounded, proactive operational optimization.

This work addresses the limitations of traditional root cause analysis methods in complex systems lacking dependency graphs, where such approaches often erroneously identify components with high anomaly scores as root causes—particularly when fault propagation amplifies anomalies downstream. To overcome this challenge, the authors propose PRISM, a novel framework that, for the first time, offers theoretically grounded root cause localization without requiring a dependency graph. PRISM leverages a component-based system model and the intrinsic properties of anomaly propagation to construct a lightweight inference algorithm capable of identifying true root causes in the absence of topological information. Evaluated on 735 real-world faults across nine datasets, PRISM achieves a Top-1 accuracy of 68%, representing a 258% improvement over the best baseline method, while requiring only 8 milliseconds per diagnosis.

This work proposes an intelligent agent-based diagnostic framework leveraging large language models (LLMs) to overcome the limitations of traditional root cause analysis methods, which rely on hard-coded rules, incur high maintenance costs, and are tightly coupled with infrastructure. By integrating a Model Context Protocol (MCP) and a constrained tool space, the framework enables agents to autonomously invoke tools for service querying, dependency retrieval, and multi-source data analysis, facilitating stepwise reasoning to pinpoint root causes. A structured investigation protocol ensures traceable and reproducible inference while maintaining robustness under incomplete or ambiguous information, effectively decoupling the model from underlying infrastructure. This approach lays the foundation for autonomous fault diagnosis and change impact assessment, paving the way for automated remediation and risk prediction, thereby significantly enhancing operational efficiency and system safety.

Existing approaches to root cause analysis in Kubernetes often rely on scenario-specific heuristics, lacking auditability and reproducibility. This work proposes a graph-guided root cause analysis agent that systematically encodes operational constraints—such as read-only forensics, propagation-aware diagnosis, and independent validation—into a typed evidence graph and a LangGraph state machine. By integrating large language model reasoning with deterministic graph traversal over OpenTelemetry telemetry data, the method enables an auditable and reproducible diagnostic workflow. Evaluated on 23 scenarios from the ITBench benchmark, the approach improves the F1 score for root cause entities from 0.6087 to 0.9130; even without scenario-specific prompts, it maintains a score of 0.6958, demonstrating both effectiveness and partial generalization capability.