Benchmark RadarAI BENCHMARK PROFILE
NetConfArena
Evaluates LLM agents in closed-loop network configuration across 480 task instances from 96 protocol-focused templates, scoring based on hidden executable test cases.
- Released
- 2026-08-24
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
Provides a realistic, executable benchmark for network configuration agents, revealing failure patterns and guiding improvements in agent reliability and planning.
Motivation
Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poorly understood.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.