| --- |
| license: mit |
| pretty_name: EC²-Bench (EC2-Bench) |
| language: |
| - en |
| - zh |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-generation |
| tags: |
| - agents |
| - llm-agents |
| - benchmark |
| - edge-cloud-collaboration |
| - edge-ai |
| - on-device |
| - llm-routing |
| - privacy |
| - openclaw |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/tasks.jsonl |
| --- |
| |
| # EC²-Bench (EC2-Bench) |
|
|
| **Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution** |
|
|
| [📄 Paper (arXiv 2610.17816)](https://arxiv.org/abs/2610.17816) · [💻 Code (GitHub)](https://github.com/OpenBMB/EC2-Bench) · [🌐 Project Page](https://passionate11.github.io/ec2-bench-project-page/) |
|
|
| EC2-Bench is a benchmark for edge-cloud collaboration in long-horizon LLM agents, jointly measuring task performance, resource usage and privacy exposure. |
|
|
|  |
|
|
| Edge-side models keep data local and cost little, but are weak at demanding planning and tool use. Cloud-hosted frontier models are far more capable, but expensive, and they see everything they are sent. In long-horizon agent execution, cloud participation is no longer a one-shot routing decision per query: it is **interleaved throughout the trajectory**, so every cloud call shapes later steps, accumulates cost, and decides how much workspace information leaves the device. |
|
|
| ## What is in this dataset |
|
|
| | Path | What it is | |
| | --- | --- | |
| | `data/tasks.jsonl` | Index of all **128 tasks** (shown in the viewer above): prompt, expected behavior, grading criteria, category, source benchmark, language, and the number of annotated sensitivity units | |
| | `workspace/ACE_Bench.tar.gz` | All task workspaces in one tarball (files, mock services, and privacy ground truth under `gt/sensitive_items.json`) | |
| | `workspace/ACE_Bench/` | The same workspaces, unpacked, for browsing | |
| | `Images/acebench-openclaw-v1.0.tar.gz` | Prebuilt Docker image (`docker save` tarball) bundling the OpenClaw harness and per-task mock services | |
|
|
| The executable task definitions (with inline `grade()` verifiers) and the evaluation code live in the [GitHub repo](https://github.com/OpenBMB/EC2-Bench). Task IDs keep the benchmark's earlier working name (`ACE_Bench`). |
|
|
| **At a glance** |
|
|
| - **128 executable digital-workspace tasks** in 6 categories (office & daily tasks, information search, safety & security, development & operations, data analysis, automation), in English and Chinese. |
| - **100 tasks with fine-grained privacy annotations**: 3,385 sensitivity units covering personal and organizational information. |
| - Tasks are adapted from Claw-Eval, WildClawBench, QwenClawBench, LiveClawBench, PinchBench and ClawBench, plus 7 newly curated tasks, and standardized to one OpenClaw runtime. |
|
|
| ## Evaluation axes |
|
|
| - **Task performance**: weighted completion score (rule-based checks, environment-state auditing, rubric-based LLM judge) and Pass³. |
| - **Resource usage**: cloud input / cache-read / output tokens with USD cost, plus edge-side FLOPs. |
| - **Privacy exposure**: fraction of annotated sensitivity units that become visible to the cloud anywhere along the trajectory (lower is better). |
|
|
| Four collaboration strategies are studied: **Query Routing**, **Per-step Routing**, **Edge-controlled Delegation** and **Edge-controlled Advisory**, against edge-only and cloud-only baselines. |
|
|
| ## Quick start |
|
|
| ```bash |
| # task workspaces |
| hf download chengpingan/EC2-Bench workspace/ACE_Bench.tar.gz --repo-type dataset --local-dir . |
| tar -xzf workspace/ACE_Bench.tar.gz |
| |
| # prebuilt Docker image (Hugging Face is not a Docker registry: download, then docker load) |
| hf download chengpingan/EC2-Bench Images/acebench-openclaw-v1.0.tar.gz --repo-type dataset --local-dir . |
| docker load -i Images/acebench-openclaw-v1.0.tar.gz |
| ``` |
|
|
| ```python |
| from datasets import load_dataset |
| tasks = load_dataset("chengpingan/EC2-Bench", split="test") |
| ``` |
|
|
| See the [GitHub README](https://github.com/OpenBMB/EC2-Bench#quick-start) for running edge-only, cloud-only and collaborative agents and for grading. |
|
|
| ## Key findings |
|
|
| 1. **Collaboration works for long-horizon agents.** Partial cloud participation improves on edge-only execution at a fraction of cloud-only cost and exposure. |
| 2. **Stronger edges close the performance gap, not the privacy gap.** As the edge model gets stronger, completion differences across strategies shrink, while privacy exposure stays strongly strategy-dependent. |
| 3. **The best collaboration mode shifts with edge capability**, from external routing and direct cloud execution toward edge-controlled invocation and cloud advisory. Deciding when to invoke the cloud and following cloud guidance are the two edge-side abilities that matter. |
|
|
| Full results: [paper](https://arxiv.org/abs/2610.17816) and [leaderboard](https://github.com/OpenBMB/EC2-Bench#leaderboard). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{huang2026ec2bench, |
| title = {Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution}, |
| author = {Huang, Pengcheng and Liu, Zhenghao and Ren, Shixin and Yu, Shi and Yao, Sijia and Yan, Yukun and Xiao, Chaojun and Sun, Maosong}, |
| journal = {arXiv preprint arXiv:2610.17816}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT, same as the [code repository](https://github.com/OpenBMB/EC2-Bench/blob/main/LICENSE). Adapted tasks are used under the licenses of their source benchmarks: Claw-Eval, WildClawBench, QwenClawBench, LiveClawBench and PinchBench are MIT-licensed, and ClawBench is Apache-2.0. |
|
|
