arXiv AI
Aug 5
GENESIS: Towards Explainable Causal Discovery
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.
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arXiv AI
Jun 9
Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents
arXiv:2606. 08590v1 Announce Type: cross Abstract: Kubernetes incidents are diagnosed reliably only when a root-cause system's reported gains come from incident evidence rather than scenario-specific shortcuts.
arXiv AI
Jul 16
How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
arXiv:2607. 13548v1 Announce Type: new Abstract: Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces, a problem that has proved resistant to both classical and LLM-based approaches.
Hugging Face Trending Papers
Jul 15
How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
Identifying root causes in production microservice failures requires reasoning over large-scale, multimodal telemetry spanning metrics, logs, and traces, a problem that has proved resistant to both classical and LLM-based approaches. The OpenRCA dataset exemplifies these challenges: it is large-scale, multimodal, and lacks detailed domain knowledge, and yields consistently low accuracy across all existing methods.
arXiv AI
1d ago
Amortised Post-Hoc Explanation with Exact Preservation for Dynamic Graph Anomaly Detectors
arXiv:2608. 15559v1 Announce Type: cross Abstract: Anomaly detection in dynamic graphs underpins financial fraud analysis, intrusion detection, and platform integrity, where automated decisions require human-interpretable justifications.
arXiv AI
Jun 26
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
arXiv:2606. 27154v1 Announce Type: new Abstract: Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use.
arXiv AI
5d ago
Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection
arXiv:2608. 12441v1 Announce Type: cross Abstract: Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst receives a score but no reason.
arXiv Machine Learning
Aug 4
TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference
arXiv:2608. 01975v1 Announce Type: cross Abstract: Large language model (LLM) inference has evolved from an offline workload into a continuously operated software service, yet root-cause analysis remains difficult because a single request spans the inference engine, Python/C++ backend, host CUDA APIs, GPU kernels, and distributed communication.
arXiv Machine Learning
Jun 5
Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data
arXiv:2412. 11800v4 Announce Type: replace Abstract: Extracting anomaly causality facilitates diagnostics once monitoring systems detect system faults.
arXiv Machine Learning
Jun 16
SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles
arXiv:2606. 15559v1 Announce Type: cross Abstract: The transition toward software-defined vehicles concentrates an increasing share of vehicle functionality into distributed software services, where failures propagate through service dependencies and the surface symptom is often several causal hops away from the underlying defect.
arXiv Machine Learning
Jun 5
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
arXiv:2606. 05636v1 Announce Type: new Abstract: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare.
arXiv AI
Aug 11
GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
arXiv:2608. 08968v1 Announce Type: cross Abstract: Microservice root cause analysis (RCA) requires correlating failures across heterogeneous telemetry within complex service dependency graphs.