English(EN) AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation

新的AgentSelect基准测试解决了LLM代理推荐的挑战

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员推出了AgentSelect，这是一个新的基准测试，旨在满足日益增长的为任务自动化推荐LLM代理的有效方法的需求。该基准测试整合了来自40多个来源的111,000多个查询和107,000个代理，为评估代理推荐系统提供了一个统一的数据集。初步分析表明，传统的协同过滤和图神经网络方法在处理代理监督的长尾性质方面存在困难，这凸显了内容感知能力匹配的重要性。 AI

影响为新兴的LLM代理生态系统建立了一个基础性的评估框架，有可能加速其开发和采用。

排序理由该集群描述了一篇介绍LLM代理基准测试的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 17 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇介绍LLM代理基准测试的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yunxiao Shi, Wujiang Xu, Tingwei Chen, Haoning Shang, Ling Yang, Yunfeng Wan, Zhuo Cao, Xing Zi, Dimitris N. Metaxas, Min Xu · 2026-11-06 05:00

AgentSelect：叙事查询到代理推荐的基准测试

arXiv:2603.03761v2 Announce Type: replace 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 benc…

本文如何被排名

报道来源 [1]

AgentSelect：叙事查询到代理推荐的基准测试

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