@inproceedings{shi-etal-2026-aj,
title = "{AJ}-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation",
author = "Shi, Wentao and
Wang, Yu and
Zhao, Yuyang and
Chen, Yuxin and
Feng, Fuli and
Hao, Xueyuan and
Su, Xi and
GU, Qi and
Su, Hui and
Cai, Xunliang and
He, Xiangnan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1269/",
pages = "25371--25413",
ISBN = "979-8-89176-395-1",
abstract = "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/."
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%0 Conference Proceedings
%T AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation
%A Shi, Wentao
%A Wang, Yu
%A Zhao, Yuyang
%A Chen, Yuxin
%A Feng, Fuli
%A Hao, Xueyuan
%A Su, Xi
%A GU, Qi
%A Su, Hui
%A Cai, Xunliang
%A He, Xiangnan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shi-etal-2026-aj
%X 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/.
%U https://aclanthology.org/2026.findings-acl.1269/
%P 25371-25413
Markdown (Informal)
[AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation](https://aclanthology.org/2026.findings-acl.1269/) (Shi et al., Findings 2026)
ACL
- Wentao Shi, Yu Wang, Yuyang Zhao, Yuxin Chen, Fuli Feng, Xueyuan Hao, Xi Su, Qi GU, Hui Su, Xunliang Cai, and Xiangnan He. 2026. AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25371–25413, San Diego, California, United States. Association for Computational Linguistics.