157 lines
4.4 KiB
Markdown
157 lines
4.4 KiB
Markdown
# TIR-Bench
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## Overview
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TIR-Bench (Thinking-with-Images Reasoning Benchmark) is a comprehensive multimodal benchmark
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that evaluates agentic visual reasoning capabilities of vision-language models. It covers
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diverse task categories requiring spatial, compositional, and multi-step visual reasoning.
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## Task Description
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- **Task Type**: Multi-task Visual Reasoning (MCQ, OCR, word search, spot difference, jigsaw, etc.)
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- **Input**: One or two images + question (most tasks use multiple-choice format)
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- **Output**: Answer letter (MCQ) or numeric/text response depending on task type
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- **Domains**: instrument, color, refcoco, rotation_game, math, word_search, visual_search, ocr, symbolic, spot_difference, contrast, jigsaw, maze
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## Key Features
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- 1,215 test samples across 13 diverse visual reasoning task categories
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- Covers single-image and dual-image reasoning scenarios
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- Answers span letter choices (A-J), integers, floats, and text
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- Task-specific scoring with LLM-as-judge fallback for robust evaluation
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## Evaluation Notes
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- Default evaluation uses the **test** split (1,215 samples)
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- Primary metric: **Accuracy** (acc)
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- Images are downloaded as `data.zip` from ModelScope and extracted automatically
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- Rule-based scoring: OCR (substring match), jigsaw (grid IoU), spot_difference (set IoU),
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word_search (numeric match), all other tasks (MCQ / numeric judge)
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- **Recommended**: set `judge_strategy=JudgeStrategy.LLM_RECALL` and provide `judge_model_args`
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to activate LLM-as-judge as a recall mechanism — the judge is called only when rule-based
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scoring gives 0, providing more accurate evaluation without unnecessary API overhead
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- [Paper](https://arxiv.org/abs/2511.01833) | [GitHub](https://github.com/agents-x-project/TIR-Bench)
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `tir_bench` |
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| **Dataset ID** | [evalscope/TIR-Bench](https://modelscope.cn/datasets/evalscope/TIR-Bench/summary) |
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| **Paper** | [Paper](https://arxiv.org/abs/2511.01833) |
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| **Tags** | `MultiModal`, `QA`, `Reasoning` |
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| **Metrics** | `acc` |
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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 | 1,215 |
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| Prompt Length (Mean) | 384.97 chars |
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| Prompt Length (Min/Max) | 19 / 4039 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `instrument` | 80 | 110.96 | 57 | 196 |
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| `color` | 100 | 130.26 | 98 | 241 |
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| `refcoco` | 120 | 144.51 | 132 | 182 |
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| `rotation_game` | 75 | 146.44 | 140 | 148 |
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| `math` | 120 | 126.69 | 50 | 397 |
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| `word_search` | 100 | 126.72 | 24 | 307 |
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| `visual_search` | 120 | 111.64 | 19 | 501 |
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| `ocr` | 60 | 35.08 | 29 | 116 |
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| `symbolic` | 50 | 88.18 | 66 | 243 |
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| `spot_difference` | 100 | 1114.79 | 93 | 1379 |
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| `contrast` | 50 | 48.1 | 31 | 123 |
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| `jigsaw` | 120 | 605 | 605 | 605 |
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| `maze` | 120 | 1527.06 | 626 | 4039 |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 1,255 |
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| Images per Sample | min: 1, max: 2, mean: 1.03 |
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| Resolution Range | 60x23 - 6944x9280 |
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| Formats | jpeg, mpo, png, webp |
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## Sample Example
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**Subset**: `instrument`
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```json
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{
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"input": [
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{
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"id": "dd25f10d",
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"content": [
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{
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"image": "[BASE64_IMAGE: jpg, ~2.9MB]"
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},
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{
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"text": "According to the image, what is the thermometer reading in Fahrenheit? Answer as an integer like 1,2,3."
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}
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]
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}
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],
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"target": "72",
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"id": 0,
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"group_id": 0,
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"subset_key": "instrument",
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"metadata": {
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"task": "instrument",
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"meta_data": {},
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"id": 6
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}
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}
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```
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## Prompt Template
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*No prompt template defined.*
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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 tir_bench \
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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 run_task
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from evalscope.config import TaskConfig
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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=['tir_bench'],
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dataset_args={
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'tir_bench': {
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# subset_list: ['instrument', 'color', 'refcoco'] # optional, evaluate specific subsets
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}
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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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