面壁智能 EC²-Bench 研究端云智能体协作
When should the cloud execute for the edge agent, and when should it just advise?
面壁智能与清华用 EC²-Bench 在 128 个可执行任务上评测四种端云协作策略，同时衡量任务完成度、资源成本与隐私暴露。端侧 Qwen3.5-9B 加云端 GLM-5.3 时，最佳协作得分 60.46，约为纯云端 82.70 的 73%，成本仅 $8.73，约为纯云端 $56.42 的 15%。弱端侧适合由外部路由器让云端执行（2B 模型 39.97 对 15.86），强端侧适合云端只给建议（9B 模型 60.46）。
正文
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: https://t.co/ESc7nA1Aeu 🌐 Project: https://t.co/J1A3ZjtgCj 💻 Code: https://t.co/xjAuYnCDT1
#EdgeCloudCollaboration #AIAgent #LLM #OnDeviceAI #AgentBenchmark #EC2Bench #OpenBMB #Tsinghua