| title | Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution |
|---|---|
| authors | Pengcheng Huang, Zhenghao Liu, Shixin Ren, Shi Yu, Sijia Yao, Yukun Yan, Chaojun Xiao, Maosong Sun |
| date | 2026-10-13 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.17816 |
| affiliation | Northeastern University |
| upvotes | 15 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{huang2026empowering,
title={Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution},
author={Pengcheng Huang and Zhenghao Liu and Shixin Ren and Shi Yu and Sijia Yao and Yukun Yan and Chaojun Xiao and Maosong Sun},
year={2026},
eprint={2610.17816},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.17816}
}