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About this paper
Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang and 17 more
Fudan NLP Lab introduced AgentGym2, a new benchmark to evaluate large language model agents in de-idealized real-world environments across complex tool usage, data analysis, and deep search scenarios. Evaluations reveal that even state-of-the-art models struggle significantly, with GPT-5 achieving an average score of 46.15%, highlighting critical limitations in handling real-world complexities.
Key insights
- Current state-of-the-art LLM agents face substantial challenges in de-idealized real-world settings, indicating a significant gap between idealized benchmark performance and practical deployment needs.
- Agentic post-training improves model performance, suggesting that specialized training for agentic behaviors is crucial for bridging the gap with proprietary systems.
- Data wrangling (cleaning and exploration) and robustness to noisy information are identified as major bottlenecks for current agents, leading to significant performance degradation.
Summary by alphaXiv.
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