@inproceedings{xi-etal-2026-agentgym2,
title = "{A}gent{G}ym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments",
author = "Xi, Zhiheng and
Yang, Dingwen and
Liu, Jiaqi and
Huang, Jixuan and
Guo, Honglin and
Huang, Baodai and
Chen, Tinggang and
Zhang, Qi and
Lu, Zhonghang and
Liu, Chenyu and
Sun, Jiajun and
Zhang, Jiazheng and
Zhu, Dingwei and
Guo, Xin and
Wang, Junzhe and
Zhang, Zhihao and
Yang, Yuming and
Ye, Junjie and
Gao, Minghe and
Liu, Dongrui and
Ji, Jiaming and
Li, Guohao and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2058/",
pages = "44451--44479",
ISBN = "979-8-89176-390-6",
abstract = "Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic evaluations. However, most existing benchmarks evaluate agents in simplified, idealized settings. They typically rely on pre-packaged tool interfaces, overlook critical steps, and assume inputs are clean and fully specified. Consequently, they understate the difficulty of real deployments, where uncertainty and noise are ubiquitous and agents must proactively explore the environment to uncover new tools. To bridge this gap, we present AgentGym2, a new evaluation framework with task instances grounded in real-world end-to-end working demands. Beyond reasoning and planning, it measures agents' ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information. Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2, revealing a substantial gap between the capability of current agents and the demands of real-world applications."
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<abstract>Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic evaluations. However, most existing benchmarks evaluate agents in simplified, idealized settings. They typically rely on pre-packaged tool interfaces, overlook critical steps, and assume inputs are clean and fully specified. Consequently, they understate the difficulty of real deployments, where uncertainty and noise are ubiquitous and agents must proactively explore the environment to uncover new tools. To bridge this gap, we present AgentGym2, a new evaluation framework with task instances grounded in real-world end-to-end working demands. Beyond reasoning and planning, it measures agents’ ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information. Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2, revealing a substantial gap between the capability of current agents and the demands of real-world applications.</abstract>
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%0 Conference Proceedings
%T AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
%A Xi, Zhiheng
%A Yang, Dingwen
%A Liu, Jiaqi
%A Huang, Jixuan
%A Guo, Honglin
%A Huang, Baodai
%A Chen, Tinggang
%A Zhang, Qi
%A Lu, Zhonghang
%A Liu, Chenyu
%A Sun, Jiajun
%A Zhang, Jiazheng
%A Zhu, Dingwei
%A Guo, Xin
%A Wang, Junzhe
%A Zhang, Zhihao
%A Yang, Yuming
%A Ye, Junjie
%A Gao, Minghe
%A Liu, Dongrui
%A Ji, Jiaming
%A Li, Guohao
%A Gui, Tao
%A Huang, Xuanjing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xi-etal-2026-agentgym2
%X Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic evaluations. However, most existing benchmarks evaluate agents in simplified, idealized settings. They typically rely on pre-packaged tool interfaces, overlook critical steps, and assume inputs are clean and fully specified. Consequently, they understate the difficulty of real deployments, where uncertainty and noise are ubiquitous and agents must proactively explore the environment to uncover new tools. To bridge this gap, we present AgentGym2, a new evaluation framework with task instances grounded in real-world end-to-end working demands. Beyond reasoning and planning, it measures agents’ ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information. Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2, revealing a substantial gap between the capability of current agents and the demands of real-world applications.
%U https://aclanthology.org/2026.acl-long.2058/
%P 44451-44479
Markdown (Informal)
[AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments](https://aclanthology.org/2026.acl-long.2058/) (Xi et al., ACL 2026)
ACL
- Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, and Xuanjing Huang. 2026. AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 44451–44479, San Diego, California, United States. Association for Computational Linguistics.