OpenBMB 发布 EC²-Bench 研究：端云协作策略随端侧能力翻转

When should the cloud execute for the edge agent, and when should it just advise?

We present a systematic study of edge-cloud collaboration in long-horizon agent execution. Using EC²-Bench, we evaluate four collaboration strategies across 128 executable tasks, jointly measuring task completion, resource cost, and privacy exposure.
Here are three key findings:

1️⃣ Small cloud budget, big gains.(cost-efficiency). With Qwen3.5-9B on-device and GLM-5.3 in the cloud, edge-only scores 39.12; the best collaboration reaches 60.46 — about 73% of cloud-only (82.70) — at only $8.73, roughly 15% of cloud-only's $56.42.
2️⃣ Performance converges, privacy doesn't. On Qwen3.7-Plus, best collaboration hits 76.84 vs 82.70 cloud-only; strategy gaps shrink to just 3.67 completion points, yet privacy exposure spans 66.23 percentage points — and the best-performing strategy leaks the most.
3️⃣ The mode flips with edge capability. Weak edges do best with an external router that lets the cloud execute (2B: Per-step Routing 39.97 vs self-delegation 15.86); stronger edges do best when the cloud only advises (9B Cloud Advisory 60.46).
Further experiments highlight two abilities that matter for effective self-directed collaboration: knowing when to ask for help, and actually acting on the cloud's advice.

📄 Paper: http://arxiv.org/abs/2610.17816
🌐 Project: http://passionate11.github.io/ec2-bench-project-page/
💻 Code: http://github.com/OpenBMB/EC2-Bench

#EdgeCloudCollaboration #AIAgent #LLM #OnDeviceAI #AgentBenchmark #EC2Bench #OpenBMB #Tsinghua