English(EN) Joint Workflow and Prompt Optimization for User Behavior Simulation

SWORD框架通过自主提示和工作流发现优化用户行为仿真

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

开发了一个名为SWORD的新框架，通过联合优化多智能体工作流拓扑和自然语言提示来改进用户行为仿真。该方法仅由标量任务指标指导，可自主发现领域相关信号和先验知识，无需领域初始化或特定任务工程。SWORD在现有基线之上实现了统计学上的显著改进，以更少的数据和更低的API成本实现了更高的准确性，同时还建立了文本梯度作为无监督特征重要性发现的机制。 AI

影响该框架可以显著提高各种应用中用户行为建模的效率和准确性，减少对手动工程的需求。

排序理由该条目是一篇学术论文，详细介绍了一个新框架及其实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Nipun B Nair (Monash University), Tongtong Wu (Monash University), Hongzhi Yin (The University of Queensland), Hui Li (Xiamen University), Weiqing Wang (Monash University) · 2026-10-07 04:00

面向用户行为仿真的联合工作流与提示优化

arXiv:2610.07663v1 Announce Type: cross Abstract: User behavior simulation is the computational modeling of user interactions within information systems through the use of simulated agents in place of live users. It supports system testing and evaluation, decision-making and fore…

本文如何被排名

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面向用户行为仿真的联合工作流与提示优化

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