AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation

• URL: http://arxiv.org/abs/2603.03761v1
• Date: Wed, 04 Mar 2026 06:17:51 GMT
• Title: AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
• Authors: Yunxiao Shi, Wujiang Xu, Tingwei Chen, Haoning Shang, Ling Yang, Yunfeng Wan, Zhuo Cao, Xing Zi, Dimitris N. Metaxas, Min Xu,
• Abstract summary: AgentSelect is a benchmark that reframes agent selection as narrative query-to-agent recommendation.<n>It converts heterogeneous evaluation artifacts into unified, positive-only interaction data.<n>AgentSelect provides the first unified data and evaluation infrastructure for agent recommendation.
• Score: 39.61543921719145
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: LLM agents are rapidly becoming the practical interface for task automation, yet the ecosystem lacks a principled way to choose among an exploding space of deployable configurations. Existing LLM leaderboards and tool/agent benchmarks evaluate components in isolation and remain fragmented across tasks, metrics, and candidate pools, leaving a critical research gap: there is little query-conditioned supervision for learning to recommend end-to-end agent configurations that couple a backbone model with a toolkit. We address this gap with AgentSelect, a benchmark that reframes agent selection as narrative query-to-agent recommendation over capability profiles and systematically converts heterogeneous evaluation artifacts into unified, positive-only interaction data. AgentSelectcomprises 111,179 queries, 107,721 deployable agents, and 251,103 interaction records aggregated from 40+ sources, spanning LLM-only, toolkit-only, and compositional agents. Our analyses reveal a regime shift from dense head reuse to long-tail, near one-off supervision, where popularity-based CF/GNN methods become fragile and content-aware capability matching is essential. We further show that Part~III synthesized compositional interactions are learnable, induce capability-sensitive behavior under controlled counterfactual edits, and improve coverage over realistic compositions; models trained on AgentSelect also transfer to a public agent marketplace (MuleRun), yielding consistent gains on an unseen catalog. Overall, AgentSelect provides the first unified data and evaluation infrastructure for agent recommendation, which establishes a reproducible foundation to study and accelerate the emerging agent ecosystem.

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