hotdogzz/Agentgym2

🌟 Introduction

Existing agent benchmarks often evaluate models in simplified, idealized settings: tool interfaces are pre-packaged, intermediate steps are assumed solved, and inputs are relatively clean and well-specified. In contrast, AgentGym2 evaluates language agents under de-idealized, real-world conditions, where tools must often be discovered, pipelines must be composed from scratch, and information can be noisy, ambiguous, or underspecified.

AgentGym2 is built on realistic digital environments and provides agents with a basic yet composable toolbox instead of pre-selected, task-specific tools. This design encourages agents to proactively explore the environment, discover additional tools when needed, and execute complete end-to-end workflows under uncertainty.

📦 AgentGym2 Benchmark

AgentGym2 spans 437 tasks across 27 domains with ZH/EN requests. It covers three core scenarios:

• Complex Tool Use: agents must discover and compose tools in realistic web environments.
• Data Analysis: agents must explore raw data, understand cleaning or transformation requirements, and complete end-to-end analysis.
• Deep Search: agents must retrieve, synthesize, and verify information across multiple sources while handling ambiguous or noisy user queries.

Each query may be paired with multimodal attachments, including documents, images, audio, or video. All tools are encapsulated into a unified interface and can be invoked in parallel when appropriate, supporting scalable and reproducible evaluation.

Statistics	Number
Total queries	437
- Complex Tool Use	182
- Data Analysis	57
- Deep Search	198
Language	ZH / EN
Total attachments	652
Attachment modalities	Text, Image, Audio, Video
Basic tool types	27

🔖 Citation

@misc{xi2026agentgym2benchmarkinglargelanguage,
      title={AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments}, 
      author={Zhiheng Xi and Dingwen Yang and Jiaqi Liu and Jixuan Huang and Honglin Guo and Baodai Huang and Tinggang Chen and Qi Zhang and Zhonghang Lu and Chenyu Liu and Jiajun Sun and Jiazheng Zhang and Dingwei Zhu and Xin Guo and Junzhe Wang and Zhihao Zhang and Yuming Yang and Junjie Ye and Minghe Gao and Dongrui Liu and Jiaming Ji and Guohao Li and Tao Gui and Qi Zhang and Xuanjing Huang},
      year={2026},
      eprint={2607.05174},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2607.05174}, 
}