Dataset	arXiv	Website	Code
VSI-Bench
VSI-Bench-Debiased

[Nov. 7, 2025] UPDATE: This Dataset has been updated to include a "Debiased" subset following the TsT Pruning Methodology

Visual-Spatial Intelligence Benchmark (VSI-Bench & VSI-Bench-Debiased)

This repository contains the visual spatial intelligence benchmark (VSI-Bench), introduced in Thinking in Space: How Multimodal Large Language Models See, Remember and Recall Spaces, and its debiased counterpart VSI-Bench-Debiased, introduced in our follow-up work on systematic benchmark robustification Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts.

Overview

VSI-Bench evaluates visual-spatial intelligence of multimodal models through egocentric video understanding, comprising over 5,000 question-answer pairs from real-world indoor scenes.

VSI-Bench-Debiased is a robustified version that reduces non-visual shortcuts using our Test-set Stress-Test (TsT) and Iterative Bias Pruning (IBP) methodology. This version better isolates visual reasoning capabilities by systematically removing samples that can be solved without visual input.

Description

VSI-Bench quantitatively evaluates the visual-spatial intelligence of MLLMs from egocentric video. VSI-Bench comprises over 5,000 question-answer pairs derived from 288 real videos. These videos are sourced from the validation sets of the public indoor 3D scene reconstruction datasets ScanNet, ScanNet++, and ARKitScenes, and represent diverse environments -- including residential spaces, professional settings (e.g., offices, labs), and industrial spaces (e.g., factories) and multiple geographic regions. By repurposing these existing 3D reconstruction and understanding datasets, VSI-Bench benefits from accurate object-level annotations, which are used in question generation and could support future studies exploring the connection between MLLMs and 3D reconstruction.

Fields

The dataset contains the following fields:

Field Name	Description
id	Global index of the entry in the dataset
dataset	Video source: scannet, arkitscenes or scannetpp
scene_name	Scene (video) name for each question-answer pair
question_type	The type of task for question
question	Question asked about the video
options	Choices for the question (only for multiple choice questions)
ground_truth	Ground truth answer for the question
pruned	Boolean indicating if example was removed by Iterative Bias Pruning (IBP)

Why VSI-Bench-Debiased?

While the original VSI-Bench was designed to require visual understanding, our follow-up analysis revealed that a portion of questions could be answered using non-visual shortcuts—such as statistical biases in answer distributions or world knowledge priors—without actually processing the visual input.

VSI-Bench-Debiased addresses this through systematic robustification:

1. Test-set Stress-Test (TsT): We applied k-fold cross-validation directly on the test set to identify samples with high non-visual solvability, assigning each sample a bias score.
2. Iterative Bias Pruning (IBP): We iteratively removed samples with the highest bias scores, creating a subset that better compels genuine visual reasoning.

Key improvements in VSI-Bench-Debiased:

• Reduced non-visual solvability: Blind models (text-only, no vision) perform closer to chance
• Wider vision-blind gap: Greater performance difference between vision-enabled and vision-disabled models
• Better isolation of visual reasoning: Fine-tuning on in-distribution data improves vision-enabled performance much more than blind performance, confirming reduced shortcut reliance

For researchers interested in robust evaluation of visual-spatial intelligence, we recommend reporting results on both the full and debiased subsets to provide comprehensive assessment.

Usage

Dataset Configurations

This dataset provides three configurations for flexible evaluation:

Config	Description	Usage
full (default)	All 5,131 examples with pruned column	Load all data, filter as needed
debiased	2,363 examples (non-pruned subset)	Evaluate on robustified benchmark
pruned	2,768 examples (pruned by IBP)	Analyze removed samples

Loading the Dataset Annotations

Load specific configuration

If you want to load just a specific subset, you can use the config name with the load_dataset function as follows:

from datasets import load_dataset

# Load full dataset (default)
vsi_bench_full = load_dataset("nyu-visionx/VSI-Bench")
# or use the config name "full"
vsi_bench_full = load_dataset("nyu-visionx/VSI-Bench", "full")

# Load debiased version only
vsi_bench_debiased = load_dataset("nyu-visionx/VSI-Bench", "debiased")

# Load pruned examples only
vsi_bench_pruned = load_dataset("nyu-visionx/VSI-Bench", "pruned")

Load full dataset and filter using pruned column (recommended)

For LMMS-Eval users: We have updated the vsi-bench task to automatically report scores on both full and debiased subsets. (TODO: LINK).

We recommend loading the "full" set, evaluating on all samples, and then using the pruned column to compute scores on both the full and debiased subsets.

from datasets import load_dataset

# Load full dataset with pruned annotations
vsi_bench_full = load_dataset("nyu-visionx/VSI-Bench")

# Evaluate on full set
model_predictions = evaluate_model(vsi_bench_full)

# Score on both the full and debiased subsets
full_acc = compute_accuracy(model_predictions)
debiased_acc = compute_accuracy(model_predictions.filter(lambda x: not x["pruned"]))

Evaluation

TODO: link to the LMMS Eval Code

VSI-Bench evaluates performance using two metrics: for multiple-choice questions, we use Accuracy, calculated based on exact matches. For numerical-answer questions, we introduce a new metric, MRA (Mean Relative Accuracy), to assess how closely model predictions align with ground truth values.

We provide an out-of-the-box evaluation of VSI-Bench in our GitHub repository, including the metrics implementation used in our framework. For further detailes, users can refer to our paper and GitHub repository.

Files

• test-*.parquet: Parquet files containing dataset annotations (questions, answers, metadata).
  • test_debiased.parquet: Annotations for the debiased subset (2,363 examples)
  • test_pruned.parquet: Annotations for the pruned subset (2,768 examples)
• *.zip: Compressed video files for the dataset
  • arkitscenes.zip: Videos for the ARKitScenes dataset
  • scannet.zip: Videos for the ScanNet dataset
  • scannetpp.zip: Videos for the ScanNet++ dataset
• pruned_ids.txt: List of example IDs removed by Iterative Bias Pruning
• create_pq.py: Convenience script to regenerate parquet files from test.jsonl and pruned_ids.txt. Can be run with uv run create_pq.py.

Citation

If you use these datasets in your research, please cite the original VSI-Bench paper and our debiasing paper that produced VSI-Bench-Debiased:

@inproceedings{yang2025thinking,
    title={{Thinking in Space: How Multimodal Large Language Models See, Remember and Recall Spaces}},
    author={Yang, Jihan and Yang, Shusheng and Gupta, Anjali and Han, Rilyn and Fei-Fei, Li and Xie, Saining},
    booktitle={CVPR},
    year={2025},
}

@article{brown2025benchmark,
    title={{Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts}},
    author={Brown, Ellis and Yang, Jihan and Yang, Shusheng and Fergus, Rob and Xie, Saining},
    year={2025},
    journal={arXiv preprint arXiv:2511.04655},
}