Benchmarking LLM Judges for Mobile Agent Evaluation
Mobile agent benchmarks increasingly rely on LLM-based judges to evaluate task completion, yet the reliability of these judges on mobile agent trajectories remains largely unexamined. We introduce MobileJudgeBench, a benchmark for systematically evaluating LLM-as-judge methods on mobile agent trajectories. Our benchmark comprises 931 human-annotated trajectories spanning 6 mobile agent benchmarks, 4 agent models, and 68 apps. We evaluate 6 judge methods (five adapted from SPA-Bench, A3 with two modes, AndroidArena, and AgentRewardBench, plus a simple baseline we design) across multiple LLM backends. Our experiments reveal three key findings. First, a simple baseline judge with sampled scr...
| Authors | Ziqiang Wang, Li Gu, Zhixiang Chi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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