README.md

---
license: mit
task_categories:
- visual-question-answering
- image-to-text
language:
- en
tags:
- physical-tool-use
- mllm-evaluation
- tool-planning
- multimodal
pretty_name: PhysTool-Bench
size_categories:
- 1K<n<10K
---

<h1 align="center">PhysTool-Bench: Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use</h1>

<p align="center">
  <a href="https://github.com/ModalityDance/PhysTool-Bench">🐱 GitHub</a> ｜ 
  <a href="https://arxiv.org/abs/2606.10803">📄 Paper</a> ｜ 
  <a href="https://modalitydance.github.io/PhysTool-Bench/">🏠 Project Page</a> ｜ 
  <a href="https://huggingface.co/papers/2606.10803">🤗 HuggingFace Papers</a>
</p>

---


## 📊 Dataset Summary

**PhysTool-Bench** is a multimodal benchmark designed to evaluate how well Multimodal Large Language Models (MLLMs) perceive, select, and sequence physical tools in real-world scenes. Unlike traditional tool-use benchmarks that focus on digital APIs, this dataset probes an MLLM's ability to ground functional reasoning in cluttered, physical environments. 

The benchmark features 2,510 high-quality queries covering 2,678 unique physical tools across 57 distinct categories (e.g., manufacturing, healthcare, farming).

### Key Features
- **Two‑Task Evaluation:** Decouples pure visual recognition from functional planning and sequencing.
- **Real‑World Clutter:** Each scene contains an average of 8.6 tools (3.1 required targets and 5.5 visually/functionally similar distractors).
- **Sequential Logic:** 86.9% of the tasks require a strict execution order, rigorously testing the model's physical commonsense.

---

## 🎯 Supported Tasks

The dataset separates evaluation into two distinct tracks to pinpoint whether model failures stem from visual bottlenecks or poor physical reasoning.

### Task I: Tool Recognition
* **Input:** A real-world scenario image.
* **Objective:** Enumerate all visible tools in the cluttered scene.
* **Purpose:** Measures pure visual enumeration and recognition capabilities.

### Task II: Tool Selection & Planning
* **Input:** A real-world scenario image paired with a brief task instruction.
* **Objective:** Output the exact, ordered sequence of tools required to complete the specified task.
* **Purpose:** Measures functional mapping, physical commonsense, and multi-step planning capabilities.

---

## 📁 Dataset Structure

Unlike standard text-to-text datasets, **PhysTool-Bench** relies on a decoupled structure to support complex visual reasoning evaluations. The repository contains the raw images and two primary metadata files:

* `images/`: Directory containing all high-resolution physical scenario images.
* `generation_checkpoint.json`: The input file used for model inference. It contains the image paths and `task_instruct` prompts for Task II.
* `corrected_tools.json`: The ground truth file used for evaluation. It contains the refined taxonomy, required tools (`target_tools`), `target_steps` for ordered tasks, and `negative_tools` (distractors).
* `final_matching_info.json`: The alignment and mapping metadata file utilized by the offline evaluation pipeline to support tool taxonomy normalization and rule-based verification.

### Example: Loading the Raw Data
You can easily download and explore the raw dataset using the `huggingface_hub` or standard Python tools:

```python
import json
import os
from huggingface_hub import snapshot_download
from PIL import Image

# 1. Download the dataset folder
dataset_path = snapshot_download(repo_id="ModalityDance/PhysTool-Bench", repo_type="dataset")

# 2. Load the input metadata
with open(os.path.join(dataset_path, "generation_checkpoint.json"), "r") as f:
    inputs = json.load(f)

# 3. Explore a sample
sample = inputs[0]
print(f"Task Instruction: {sample['task_instruct']}")

# Load corresponding image
img_path = os.path.join(dataset_path, sample['image_path'])
Image.open(img_path).show()
```

---

## ⚠️ Inference & Evaluation (Important)

Due to the complex nature of physical tool planning, **standard HuggingFace pipelines (`pipeline("visual-question-answering")`) are not sufficient for evaluating this benchmark.** To properly run PhysTool-Bench, please use our **[Official GitHub Repository](https://github.com/ModalityDance/PhysTool-Bench)**.

### Why use the official codebase?

- **Environment Isolation:** Different MLLMs require conflicting dependency versions (e.g., PyTorch, Transformers, Accelerate). Our repo provides standalone inference scripts for major models.
- **Dual Evaluation Pipelines:** Simple exact string matching fails on open-ended generation due to synonyms and morphological variations. We provide two robust alternatives:
   * **Offline Evaluation (`eval_offline.py`):** Fast, local rule-based matching using `final_matching_info.json` for API-free evaluation.
   * **LLM-as-a-Judge (`eval_gemini.py`):** Deep semantic one-to-one mapping via the Gemini API to resolve complex synonyms and functional equivalents.
   * 
**Head over to [ModalityDance/PhysTool-Bench](https://github.com/ModalityDance/PhysTool-Bench) for the complete quickstart guide, environment setups, and automated evaluation scripts.**


---

## 📚 Citation

If you use _PhysTool-Bench_ in your research or applications, please consider citing:

```bibtex
@article{PhysTool-Bench2026,
  title        = {Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use},
  author       = {Zhixin Ma and Yutong Zhou and Yongqi Li and Chong-Wah Ngo and Wenjie Li},
  journal      = {arXiv preprint arXiv:2606.10803},
  year         = {2026}
}

```

---


## 📜 License

The dataset is released under the **MIT** license.