新的GLOW框架预测代理工作流性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了GLOW，一个旨在预测代理工作流（AWs）性能的新框架。该方法结合了用于结构建模的图神经网络（GNNs）和用于语义理解的大型语言模型（LLMs），旨在降低评估大量候选AWs相关的计算成本和延迟。在FLORA-Bench基准上的实验表明，GLOW在准确性和排名效用方面优于现有方法。当集成到自动AW生成框架中时，GLOW在保持高性能的同时显著缩短了优化时间。 AI

影响该框架可以显著降低开发和优化代理工作流的计算成本，从而加速其在复杂任务解决场景中的应用。

排序理由该集群包含一篇研究论文，详细介绍了代理工作流性能预测的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Wei Guan, Jian Cao, Jinyu Cai, Qiqi Cai, Jianqi Gao, See-Kiong Ng · 2026-09-07 04:00

GLOW：面向代理工作流性能预测的图语言联合编码

arXiv:2512.15751v2 Announce Type: replace-cross Abstract: Agentic Workflows (AWs) have emerged as a promising paradigm for solving complex tasks. However, automatically generating high-quality AWs remains expensive because AW optimization requires evaluating a large number of can…

本文如何被排名

报道来源 [1]

GLOW：面向代理工作流性能预测的图语言联合编码

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