CTBench: Benchmarking AI Agents for Telecom Troubleshooting
CTBench, a newly launched public benchmark detailed in arXiv:2608.12002, assesses the effectiveness of AI agents in managing real-world telecom network operations. It emphasizes root cause analysis and path restoration—essential tasks for network engineers who need to identify faults, optimize configurations, and minimize expenses under tight constraints. Current evaluations do not accurately reflect real network dynamics or evaluate agents in environments with partial observability involving various vendors, devices, protocols, and interfaces. By creating expert-designed tasks enriched with detailed metadata, including golden evidence steps, CTBench fills this void. It employs expert-based metrics to assess both the final outcomes and the diagnostic rationale. Experiments reveal that even advanced agents encounter challenges with these tasks, highlighting ample opportunities for enhancement. The benchmark aims to drive advancements in the automation of network operations and maintenance.
Key facts
- CTBench is a public benchmark for AI agents in telecom troubleshooting.
- It focuses on root cause analysis and path restoration.
- Tasks are constructed by experts and annotated with golden evidence steps.
- Metrics evaluate both final answers and diagnostic evidence.
- Experiments show state-of-the-art agents struggle with the tasks.
- The benchmark addresses limitations of existing evaluations.
- It models partially observable telecom environments with diverse vendors, devices, protocols, and interfaces.
- The paper is available on arXiv with ID 2608.12002.
Entities
Institutions
- arXiv