| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - agents |
| - benchmark |
| configs: |
| - config_name: tasks |
| default: true |
| data_files: |
| - split: test |
| path: preview/tasks.jsonl |
| --- |
| |
| <div align="center"> |
|
|
| <h1>ReScopeBench</h1> |
| <h3>Towards Robust Long-Lived Agents</h3> |
|
|
| <p><i>Can agents keep experience useful when their environment changes?</i></p> |
|
|
| [Homepage](https://rescopebench.github.io/) | |
| [Code](https://github.com/JoeYing1019/ReScopeBench) | |
| [Quick Start](https://github.com/JoeYing1019/ReScopeBench#quick-start) | |
| [Main Results](https://github.com/JoeYing1019/ReScopeBench#main-results) |
|
|
| <p> |
| <b>18</b> stateful environments · |
| <b>352</b> tasks · |
| <b>26</b> drift events |
| </p> |
|
|
| </div> |
|
|
| ## Overview |
|
|
| **ReScopeBench evaluates the lifecycle of agent experience:** acquiring local |
| knowledge, reusing it for new goals, and selectively revising it when the |
| environment changes. Its task streams share structural and rule knowledge, |
| while unannounced drift events make only part of that knowledge obsolete. |
|
|
| - **Structural experience:** how resources are located, connected, and accessed. |
| - **Rule experience:** the local conditions and consequences that govern actions. |
| - **Evolving tasks:** streams of 16, 24, or 32 tasks with persistent business |
| state and one, two, or three drift events. |
|
|
| This dataset contains the executable **benchmark cases**. The |
| [GitHub repository](https://github.com/JoeYing1019/ReScopeBench) provides the agent |
| harness, model API adapters, metric computation, and report generation. |
|
|
| ## Dataset Contents |
|
|
| - **`cases/`** contains environment implementations, initial states, task streams, |
| drift definitions, and evaluator assets. Each environment has one `driver.py` |
| and one `formal-binding.json`. |
| - **`preview/tasks.jsonl`** exposes the 352 public task prompts for browsing. |
| The `case_path` field identifies the corresponding environment directory. |
| - **`cases-manifest.json`** records the file inventory and SHA-256 checksums used |
| by the installer. |
|
|
| The preview is a view of the task streams, **not an independent-question |
| evaluation set**. Evaluating a row in isolation does not reproduce the benchmark. |
|
|
| ## Run the Benchmark |
|
|
| Follow the [installation guide](https://github.com/JoeYing1019/ReScopeBench#installation) |
| to set up Python 3.10, Docker, and the evaluation repository. From its root, |
| download the matching cases: |
|
|
| ```bash |
| python scripts/download_cases.py |
| ``` |
|
|
| The installer selects the immutable dataset revision pinned in the code |
| repository's `config/cases.json` and verifies the downloaded files. Use that |
| matched code/data pair rather than combining arbitrary revisions. |
|
|
| For every model and environment, run **both complete settings**: |
|
|
| - **w exp:** retain naturally produced memory, skills, and permitted history. |
| - **w/o exp:** clear explicit experience between tasks. |
|
|
| The two streams start from the same initial environment and then maintain their |
| own business state. Clearing experience does not reset that state. See the |
| [quick start](https://github.com/JoeYing1019/ReScopeBench#quick-start) for model |
| configuration, batched tool calls, and execution commands. |
|
|
| Reference knowledge and grading assets belong to the evaluator, not to learner |
| inputs. These cases use the ReScopeBench harness; they are not native Harbor |
| task bundles. Preserve applicable third-party notices and licenses. |
|
|
| ## Evaluate Results |
|
|
| The [metric pipeline](https://github.com/JoeYing1019/ReScopeBench#metrics) reports |
| task performance, structural and rule experience coverage, immediate updating, |
| and recovery timeliness, with CSV and HTML outputs. Experience scores measure |
| coverage of selected reference knowledge and accept equivalent expressions; |
| task graders also allow other valid strategies. |
|
|
| The [main results](https://github.com/JoeYing1019/ReScopeBench#main-results) |
| compare 20 models across all 18 environments under both settings. |
|
|
| ## Acknowledgements |
|
|
| This research was financially supported by the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), under Grant No. GML-KF-26-06. |
|
|
