TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks

Keyu Wu; Qianjin Yu; M. Mei; Ran Liu; Cheng Wang; K. Max Zhang; Yanping Bao

doi:10.48550/arxiv.2507.18190

July 24, 2025Open Access

TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks

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Key Points

Self-improving frameworks enhance root cause analysis, addressing challenges in telecommunication networks.
The construction of realistic benchmarks is crucial for effective AI-driven solutions in RCA.
Graph-based reasoning is essential for accurate analysis, driving the need for advanced AI methods.
AI's involvement in root cause analysis can significantly improve network reliability and performance.

Implication

Benchmark construction improves root cause analysis in telecommunications, suggesting AI's role is vital.

Abstract

Root Cause Analysis (RCA) in telecommunication networks is a critical task, yet it presents a formidable challenge for Artificial Intelligence (AI) due to its complex, graph-based reasoning requirements and the scarcity of realistic benchmarks.

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Cite This Study

Wu et al. (2025) studied this question.

synapsesocial.com/papers/68e6a0f4718ef0a556b34086 https://doi.org/10.48550/arxiv.2507.18190

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TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks

Key Points

Implication

Abstract

Discussion

Cite This Study

TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks

Key Points

Implication

Abstract

Discussion

Cite This Study

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