As reinforcement learning continues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly challenging. Existing approaches rely on rule-based verifiers or LLM-as-a-Judge models, which struggle to generalize beyond narrow domains. Agent-as-a-Judge addresses this limitation by actively interacting with environments and tools to acquire verifiable evidence, yet its capabilities remain underexplored. We introduce a benchmark AJ-Bench to systematically evaluate Agent-as-a-Judge across three domains-search, data systems, and graphical user interfaces-comprising 155 tasks and 516 annotated trajectories. The benchmark comprehensively assesses judge agents' abilities in information acquisition, state verification, and process verification. Experiments demonstrate consistent performance gains over LLM-as-a-Judge baselines, while also revealing substantial open challenges in agent-based verification. Our data and code are available at https://aj-bench.github.io/.
@article{arxiv.2604.18240,
title = {AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation},
author = {Wentao Shi and Yu Wang and Yuyang Zhao and Yuxin Chen and Fuli Feng and Xueyuan Hao and Xi Su and Qi Gu and Hui Su and Xunliang Cai and Xiangnan He},
journal= {arXiv preprint arXiv:2604.18240},
year = {2026}
}
Comments
Accepted to ACL 2026 Findings. 43 pages total, 5 figures