外观
论文arXiv 2610.1781615 天前
赋能边缘智能体：LLM智能体执行中边缘-云协作的系统分析
原文链接：Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution（arXiv 2610.17816 · 发布日期：2026-10-13）
Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution
论文提出EC2-Bench，评估长程LLM智能体执行中的边缘-云协作，并在可执行数字工作空间任务上实证分析。
核心要点
- 随着LLM智能体进入长程场景，云端参与贯穿执行轨迹，轨迹级影响仍理解不足。
- EC2-Bench提供可执行数字工作空间任务和统一评估框架，覆盖任务表现、资源使用与隐私暴露。
- 实证显示，协作相较纯边缘或纯云执行，在表现、资源与隐私之间取得更有利平衡。
- 代码与资源已在GitHub公开，支持后续研究边缘智能体与云模型协作。
验证范围与局限：验证范围为EC2-Bench中的可执行数字工作空间长程智能体任务。
英文摘要 (Abstract)
Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models. However, as LLM agents increasingly operate in long-horizon scenarios, cloud participation becomes interleaved throughout execution rather than a one-shot decision for each query. Such interleaved participation introduces trajectory-level effects that remain insufficiently understood. To address this gap, we introduce EC2-Bench, a benchmark for evaluating edge-cloud collaboration in long-horizon agent execution. It provides executable digital-workspace agent tasks and a unified evaluation framework covering task performance, resource usage, and privacy exposure. Using this benchmark, we empirically study diverse forms of edge-cloud collaboration. Our study reveals three findings. First, collaboration achieves a favorable balance among task performance, resource usage, and privacy exposure compared with edge-only and cloud-only execution. Second, as edge models become stronger, collaborative execution approaches cloud-only performance, and performance differences across strategies shrink, while privacy exposure remains strongly strategy-dependent. Third, stronger edge capability shifts preferred collaboration from externally controlled cloud participation and direct cloud execution toward more edge-controlled collaboration. Further analyses identify cloud-invocation decision-making and instruction following as important edge-side abilities for effective collaboration. These findings provide a systematic understanding of edge-cloud collaboration in long-horizon agent execution and insights for designing future edge agents that effectively collaborate with cloud models. Code and resources are available at https://github.com/OpenBMB/EC2-Bench.
作者：Pengcheng Huang, Zimeng Liu, Shixin Ren, Shi Yu, Sijia Yao, Yukun Yan, Chaojun Xiao, Maosong Sun
来源
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