# A Network Arena for Benchmarking AI Agents on Network Troubleshooting

arXiv: https://arxiv.org/abs/2512.16381 · HTML: https://mlsanity.com/paper/2512.16381/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2025.

## Built on by (most cited first, 9 of 9)

- [SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting](https://mlsanity.com/paper/2605.04530/) (2026)
- [Evaluating Agentic Configuration Repair for Computer Networks](https://mlsanity.com/paper/2606.06212/) (2026)
- [DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows](https://mlsanity.com/paper/2608.26546/) (2026)
- [FaulT-Bench: Towards Benchmarking Network Troubleshooting LLM Agents under Unreliable User Tickets](https://mlsanity.com/paper/2608.27021/) (2026)
- [Evidence-Grounded Ensemble Diagnosis of 802.11 Packet Captures: A Multi-Stage Pipeline with Deterministic Reliability Scoring](https://mlsanity.com/paper/2606.06871/) (2026)
- [Let AI Agents Translate Networks, Not Reason About Them](https://mlsanity.com/paper/2607.22947/) (2026)
- [CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations](https://mlsanity.com/paper/2608.12002/) (2026)
- [Safety Signals to Verify NetOps Agents with Action-Level Granularity](https://mlsanity.com/paper/2609.14422/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Agentic systems, powered by Large Language Models (LLMs), assist network engineers with network configuration synthesis and network troubleshooting tasks. For network troubleshooting, progress is hindered by the absence of standardized and accessible benchmarks for evaluating LLM agents in dynamic network settings at low operational effort. We present NIKA, the largest public benchmark to date for LLM-driven network incident diagnosis and troubleshooting. NIKA targets both domain experts and especially AI researchers alike, providing zero-effort replay of real-world network scenarios, and establishing well-defined agent-network interfaces for quick agent prototyping. NIKA comprises hundreds of curated network incidents, spanning five network scenarios, from data centers to ISP networks, and covers 54 representative network issues. Lastly, NIKA is modular and extensible by design, offering APIs to facilitate the integration of new network scenarios and failure cases. We evaluate state-of-the-art LLM agents on NIKA and find that while larger models succeed more often in detecting network issues, they still struggle to localize faults and identify root causes. NIKA is open-source and available to the community: https://github.com/sands-lab/nika.