13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
136 lines
3.6 KiB
Markdown
136 lines
3.6 KiB
Markdown
# MiniWoB
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## Overview
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MiniWoB evaluates whether a multimodal agent can complete short browser tasks such as clicking buttons, filling forms,
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scrolling and dragging items.
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## Task Description
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- **Task Type**: Interactive browser tasks
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- **Input**: A task goal, an accessibility tree and a screenshot
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- **Output**: Browser actions selected through function calling
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- **Dataset**: 125 MiniWoB tasks
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- **Metrics**: `success_rate` for completed tasks and `error_rate` for environment failures
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## Evaluation Notes
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- The default run evaluates one deterministic episode per task.
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- Set `repeats=5` for the five-episode schedule.
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- Each episode allows up to 10 model/tool turns by default.
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- The model must support image input and function calling.
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- See the [MiniWoB usage guide](../third_party/miniwob.html) for installation and examples.
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `miniwob` |
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| **Dataset ID** | [BrowserGym](https://github.com/ServiceNow/BrowserGym) |
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| **Paper** | N/A |
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| **Tags** | `Agent`, `FunctionCalling`, `MultiModal`, `MultiTurn` |
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| **Metrics** | `success_rate`, `error_rate` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 125 |
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| Prompt Length (Mean) | 77 chars |
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| Prompt Length (Min/Max) | 77 / 77 chars |
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## Sample Example
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**Subset**: `default`
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```json
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{
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"input": [
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{
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"id": "4b7219db",
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"content": "The task goal and browser observation are supplied when the episode is reset."
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}
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],
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"target": "1",
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"id": 0,
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"group_id": 0,
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"tools": [
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{
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"name": "browser_action",
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"description": "Execute exactly one BrowserGym MiniWoB action. Supported signatures: noop(wait_ms=1000), mouse_move(x, y), mouse_click(x, y, button=\"left\"), mouse_dblclick(x, y, button=\"left\"), mouse_down(x, y, button=\"left\"), mouse_up(x, y, button=\"left\"), ... [TRUNCATED 44 chars] ... \"left\"), keyboard_press(key), keyboard_type(text), fill(bid, value). click accepts a string BID, for example click(\"13\"); use mouse_click(x, y) for visual targets. Coordinates are absolute screenshot pixels, not normalized 0-1000 coordinates.",
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"parameters": {
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"properties": {
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"action": {
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"type": "string",
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"description": "One BrowserGym function-call expression."
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}
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},
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"required": [
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"action"
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]
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}
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}
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],
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"metadata": {
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"task_name": "miniwob.ascending-numbers",
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"miniwob_category": "hidden test",
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"comment": "",
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"webgum_subset": "False",
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"similarity_group": "0",
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"browsergym_split": "test",
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"task_id": "miniwob.ascending-numbers",
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"seed": 1608637542,
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"repeat": 0
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}
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}
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```
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*Note: Some content was truncated for display.*
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## Prompt Template
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**Prompt Template:**
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```text
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{question}
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```
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets miniwob \
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--agent-config '{"mode":"native","strategy":"function_calling","max_steps":10}' \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import TaskConfig, run_task
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from evalscope.api.agent import NativeAgentConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['miniwob'],
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agent_config=NativeAgentConfig(
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strategy='function_calling',
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max_steps=10,
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),
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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