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AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
arXiv – CS AI|Yunxiao Shi, Wujiang Xu, Tingwei Chen, Haoning Shang, Ling Yang, Yunfeng Wan, Zhuo Cao, Xing Zi, Dimitris N. Metaxas, Min Xu|
🤖AI Summary
Researchers introduce AgentSelect, a comprehensive benchmark for recommending AI agent configurations based on narrative queries. The benchmark aggregates over 111,000 queries and 107,000 deployable agents from 40+ sources to address the critical gap in selecting optimal LLM agent setups for specific tasks.
Key Takeaways
- →AgentSelect provides the first unified benchmark for AI agent recommendation with 111,179 queries and 107,721 deployable agents.
- →The research reveals a shift from popular agent reuse to highly specialized, one-off agent configurations.
- →Traditional recommendation methods become fragile in this ecosystem, requiring capability-aware matching instead.
- →Models trained on AgentSelect successfully transfer to real-world agent marketplaces like MuleRun.
- →The benchmark establishes reproducible infrastructure to accelerate development of the AI agent ecosystem.
#ai-agents#llm#benchmark#agent-selection#automation#machine-learning#research#recommendation-systems
Read Original →via arXiv – CS AI
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