---
title: "AgentGym2 — paper by Xi et al. (2026) — summary and ideas"
description: "Most agent benchmarks test LLM agents under idealized conditions: tools are handed over ready-made, intermediate steps… 59 references."
authors: ["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", "Xuanjing Huang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.2058/"
doi: "10.18653/v1/2026.acl-long.2058"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xi-2026-agentgym2-benchmarking-large-language-model/"
---
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# AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

**Authors:** [Zhiheng Xi](https://md.research.elamur.ai/free/en/authors/zhiheng-xi/), Dingwen Yang, [Jiaqi Liu](https://md.research.elamur.ai/free/en/authors/jiaqi-liu/), Jixuan Huang, [Honglin Guo](https://md.research.elamur.ai/free/en/authors/honglin-guo/), Baodai Huang, Tinggang Chen, [Qi Zhang](https://md.research.elamur.ai/free/en/authors/qi-zhang/), Zhonghang Lu, Chenyu Liu, Jiajun Sun, [Jiazheng Zhang](https://md.research.elamur.ai/free/en/authors/jiazheng-zhang/), Dingwei Zhu, Xin Guo, [Junzhe Wang](https://md.research.elamur.ai/free/en/authors/junzhe-wang/), [Zhihao Zhang](https://md.research.elamur.ai/free/en/authors/zhihao-zhang/), [Yuming Yang](https://md.research.elamur.ai/free/en/authors/yuming-yang/), [Junjie Ye](https://md.research.elamur.ai/free/en/authors/junjie-ye/), Minghe Gao, [Dongrui Liu](https://md.research.elamur.ai/free/en/authors/dongrui-liu/), [Jiaming Ji](https://md.research.elamur.ai/free/en/authors/jiaming-ji/), [Guohao Li](https://md.research.elamur.ai/free/en/authors/guohao-li/), [Tao Gui](https://md.research.elamur.ai/free/en/authors/tao-gui/), [Qi Zhang](https://md.research.elamur.ai/free/en/authors/qi-zhang/) and [Xuanjing Huang](https://md.research.elamur.ai/free/en/authors/xuanjing-huang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.2058/) · [PDF](https://aclanthology.org/2026.acl-long.2058.pdf) · [DOI 10.18653/v1/2026.acl-long.2058](https://doi.org/10.18653/v1/2026.acl-long.2058) · [OpenAlex](https://openalex.org/W7166890094)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most agent benchmarks test LLM agents under idealized conditions: tools are handed over ready-made, intermediate steps are skipped, and inputs are tidy and complete. AgentGym2 aims to close this gap by evaluating agents in realistic, "de-idealized" settings. It covers three scenarios: complex tool use, end-to-end data analysis, and deep search. Tasks span more than 27 domains and are derived from genuine user needs collected on GitHub, Reddit and Kaggle. These needs were turned into test instances through automatic synthesis, query augmentation and human annotation, then checked by a cascade of model-based and human review.

Instead of task-specific tools, agents receive a generic but composable toolbox. This forces them to discover tools on their own, combine them for new tasks, and cope with noisy or vague queries. Across 15 proprietary and open-source models, even the strongest systems succeed on fewer than half of the tasks. The result shows that current agents are still far from the reliability needed in production.

## Key points
- The toolbox organizes more than 27 actions into five kinds of capability: browsing the web, retrieving information, handling files, understanding multimodal inputs, and executing code. A modular, decoupled design allows isolated, parallel runs.
- GPT-5 scores about 44% and Claude Sonnet 4.5 about 37%. Post-trained Nex-N1-32B and Nex-N1-671B beat their base models by roughly 9.16% and 10.53%. Larger models and more compute help, but allowing more interaction turns does not.
- Adding memory-bias noise to deep search queries cuts accuracy by 7.4% for GPT-5 and 6.4% for Claude-4.5-Sonnet.
- Ablations confirm the impact of each non-ideal factor. For example, GPT-5 scores 61.67 when hidden information is supplied directly, versus 49.60 in the de-idealized setting.
- The most common failures are incorrect analysis (24.0%) and insufficient exploration (22.8%). Code and data are released on GitHub (hotdog-zz/Agentgym2) and Hugging Face (hotdogzz/Agentgym2).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 315 works · 43,267 citations
- [Fudan University](https://md.research.elamur.ai/free/en/universities/fudan-university/) — University, Shanghai, China · 202 works · 22,509 citations
- [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) — University, Shanghai, China · 212 works · 19,544 citations
- [Zhejiang University](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) — University, Hangzhou, China · 224 works · 13,587 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

## Cite

Cite as: 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. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xi2026agentgym2,
  title = {{AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments}},
  author = {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},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.2058},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2058}
}
```

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*Updated 2 November 2026*