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

arXiv: https://arxiv.org/abs/2507.18190 · HTML: https://mlsanity.com/paper/2507.18190/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs](https://mlsanity.com/paper/2406.01297/) (2024)
- [MASAI: Modular Architecture for Software-engineering AI Agents](https://mlsanity.com/paper/2406.11638/) (2024)
- [RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data](https://mlsanity.com/paper/2412.17015/) (2024)
- [Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute](https://mlsanity.com/paper/2503.23803/) (2025)
- [Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications](https://mlsanity.com/paper/2409.05314/) (2024)
- [ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs](https://mlsanity.com/paper/2403.01971/) (2024)
- [An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks](https://mlsanity.com/paper/2105.03092/) (2021)
- [Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQA](https://mlsanity.com/paper/2505.20971/) (2025)
- [Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data](https://mlsanity.com/paper/2505.07877/) (2025)

## Built on by (most cited first, 2 of 2)

- [Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks](https://mlsanity.com/paper/2507.21974/) (2025)
- [Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis](https://mlsanity.com/paper/2609.02805/) (2026)

## Abstract (arXiv)

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.