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

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. To catalyze research in this domain, We herein present TN-RCA530, the inaugural real-world, publicly accessible benchmark for root cause analysis (RCA) of telecommunication network alarms, comprising 530 fault scenarios constructed from expert-validated Knowledge Graphs(KGs). Our evaluation reveals that even state-of-the-art Large Language Models (LLMs) perform poorly on this task, with the best models achieving an F1-score below 70%, highlighting its significant difficulty.To address this challenge, we then propose Auto-RCA, a novel agentic system that automates the iterative refinement of a code-based solution. The core innovation of Auto-RCA lies beyond simple self-correction; it employs an iterative "evaluate-analyze-repair" loop that systematically identifies common patterns across all failure cases to generate contrastive feedback. This feedback guides the LLM to fix systemic logical flaws rather than isolated errors. Experiments show that this agentic framework dramatically boosts problem-solving performance, elevating the final solution's F1-score on TN-RCA530 from a baseline of 58.99% (achieved by Gemini-2.5-Pro