Files
sci-gui-agent-benchmark/evaluation_examples
yuanmengqi e433f35c1f feat: standardize configuration fields across all evaluation examples
- Add `fixed_ip` field to all 369 JSON files in examples directory
  - Set to `true` for 8 files listed in google_chrome.json multi_apps
  - Set to `false` for remaining 361 files
- Add `possibility_of_env_change` field to 363 JSON files missing this field
  - Set to "low" for newly added fields
  - Preserve existing values (4 medium, 2 high) for 6 files that already had this field

This ensures consistent configuration schema across all evaluation examples
while maintaining backward compatibility with existing settings.
2025-07-16 13:45:34 +00:00
..
2025-07-08 18:59:00 +08:00
2024-11-25 16:30:59 +08:00

Evaluation examples

Here we put the data examples to benchmark the ability of agents when interacting with GUI. The examples are stored in ./examples where each data item formatted as:

{
    "id": "uid", # unique id
    "snapshot": "snapshot_id", # the snapshot id of the environment, with some data already there and apps already opened, or just desktop
    "instruction": "natural_language_instruction", # the natural language instruction of the task, what we want the agent to do
    "source": "website_url", # where we know this example, some forum, or some website, or some paper
    "config": {xxx}, # the scripts to setup the donwload and open files actions, as the initial state of a task
    # (coming in next project) "trajectory": "trajectory_directory", # the trajectory directory, which contains the action sequence file, the screenshots and the recording video
    "related_apps": ["app1", "app2", ...], # the related apps, which are opened during the task
    "evaluator": "evaluation_dir", # the directory of the evaluator, which contains the evaluation script for this example
…
}

The ./trajectories file contains the annotated trajectories for each data item in ./examples for finishing the task.

For now, it is under construction, and only tested on Windows 10. Please:

  • Modify the path accordingly to run the evaluation;
  • Remind us if some parts are overfit to our environment.