English(EN) LongSearchEval: Evaluating Long-Horizon Web Search Capabilities of Large Language Models via a Trajectory-Aware Agent Framework with Failure-Node Localization

新框架LongSearchEval评估LLM网络搜索能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员推出LongSearchEval，一个旨在评估大型语言模型（LLM）长视距网络搜索能力的新框架。该框架利用轨迹感知代理协议来收集和分析真实的交互轨迹，与现有方法相比，能够进行更准确的行为评估和细粒度的错误定位。LongSearchEval被用于在100个任务上评估八个家族的16个模型，产生了1,600个保留运行和超过6,500个自动标记的故障节点，人类验证证实了标签和特征对应性的一致性很高。 AI

影响该框架可能带来更强大的LLM评估，推动代理式网络搜索能力的改进。

排序理由该条目描述了一篇介绍LLM新评估框架的学术论文。[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 →

本文如何被排名

Signal score: 14 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目描述了一篇介绍LLM新评估框架的学术论文。[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) · Jialiang Liu, Shusheng Li, Wenjun Tan · 2026-10-27 04:00

LongSearchEval：通过具有故障节点定位的轨迹感知代理框架评估大型语言模型在长视距网络搜索方面的能力

arXiv:2610.35150v1 Announce Type: new Abstract: Long-horizon web search requires LLM-based agents to integrate information across web pages through extended interactions under task-specific constraints. Evaluating this capability is important not only for measuring final answer q…

本文如何被排名

报道来源 [1]

LongSearchEval：通过具有故障节点定位的轨迹感知代理框架评估大型语言模型在长视距网络搜索方面的能力

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