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---
dataset_info:
features:
- name: id
dtype: string
- name: turncase
dtype: string
- name: prompt_category
dtype: string
- name: eval_focus
dtype: string
- name: prompt
dtype: string
- name: images_by_turn
sequence:
sequence:
dtype: image
- name: rubrics
sequence: string
splits:
- name: train
num_bytes: 3506927656
num_examples: 1191
download_size: 3506927656
dataset_size: 3506927656
configs:
- config_name: default
data_files:
- split: train
path: data/*.parquet
---
VisuAlToolBench is a challenging benchmark to assess tool-enabled visual perception, transformation, and reasoning in multimodal LLMs. It evaluates whether models can not only think about images but also think with images by actively manipulating visuals (e.g., crop, edit, enhance) and integrating general-purpose tools to solve complex tasks. The dataset contains single-turn and multi-turn tasks across diverse domains, each accompanied by detailed rubrics for systematic evaluation. Parquet files under `data/` are auto-indexed by the Hub and power the Dataset Viewer.
Paper: [BEYOND SEEING: Evaluating Multimodal LLMs on Tool-enabled Image Perception, Transformation, and Reasoning](https://static.scale.com/uploads/654197dc94d34f66c0f5184e/vtb_paper.pdf)
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