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🧠 AI⚪ NeutralImportance 7/10
The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution
arXiv – CS AI|Junlong Li, Wenshuo Zhao, Jian Zhao, Weihao Zeng, Haoze Wu, Xiaochen Wang, Rui Ge, Yuxuan Cao, Yuzhen Huang, Wei Liu, Junteng Liu, Zhaochen Su, Yiyang Guo, Fan Zhou, Lueyang Zhang, Juan Michelini, Xingyao Wang, Xiang Yue, Shuyan Zhou, Graham Neubig, Junxian He||3 views
🤖AI Summary
Researchers introduce Tool Decathlon (Toolathlon), a comprehensive benchmark for evaluating AI language agents across 32 software applications and 604 tools in realistic, multi-step scenarios. The benchmark reveals significant limitations in current AI models, with the best performer (Claude-4.5-Sonnet) achieving only 38.6% success rate on complex, real-world tasks.
Key Takeaways
- →Tool Decathlon benchmark spans 32 applications from Google Calendar to Kubernetes with 604 tools for testing AI agents.
- →Current SOTA AI models show poor performance on complex tasks, with Claude-4.5-Sonnet achieving only 38.6% success rate.
- →The benchmark includes 108 manually crafted tasks requiring average of 20 tool interactions to complete.
- →Open-source models perform significantly worse, with DeepSeek-V3.2-Exp reaching only 20.1% success rate.
- →The benchmark addresses gaps in existing AI agent evaluation by providing realistic environment states and long-horizon complexity.
#ai-agents#benchmarking#tool-use#language-models#ai-evaluation#multi-step-tasks#claude#deepseek#toolathlon#ai-limitations
Read Original →via arXiv – CS AI
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