metadata

language:
  - en
license: cc-by-nc-4.0
size_categories:
  - 1K<n<10K
task_categories:
  - image-text-to-text
tags:
  - multimodality
  - reasoning
configs:
  - config_name: cube
    data_files:
      - split: train
        path: cube/train-*
  - config_name: cube_easy
    data_files:
      - split: train
        path: cube_easy/train-*
  - config_name: cube_perception
    data_files:
      - split: train
        path: cube_perception/train-*
  - config_name: portal_binary
    data_files:
      - split: train
        path: portal_binary/train-*
  - config_name: portal_blanks
    data_files:
      - split: train
        path: portal_blanks/train-*
dataset_info:
  - config_name: cube
    features:
      - name: image
        dtype: image
      - name: face_arrays
        sequence:
          sequence:
            sequence: int64
      - name: question
        dtype: string
      - name: reference_solution
        dtype: string
    splits:
      - name: train
        num_bytes: 274367533
        num_examples: 1000
    download_size: 270371502
    dataset_size: 274367533
  - config_name: cube_easy
    features:
      - name: question
        dtype: string
      - name: face_arrays
        sequence:
          sequence:
            sequence: int64
      - name: reference_solution
        dtype: string
    splits:
      - name: train
        num_bytes: 3940912
        num_examples: 1000
    download_size: 394861
    dataset_size: 3940912
  - config_name: cube_perception
    features:
      - name: image
        dtype: image
      - name: question
        dtype: string
      - name: answer
        sequence:
          sequence: int64
    splits:
      - name: train
        num_bytes: 7924582
        num_examples: 200
    download_size: 7775024
    dataset_size: 7924582
  - config_name: portal_binary
    features:
      - name: map_name
        dtype: string
      - name: images
        sequence: string
      - name: system_prompt
        dtype: string
      - name: user_prompt
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 2051248
        num_examples: 512
    download_size: 160438
    dataset_size: 2051248
  - config_name: portal_blanks
    features:
      - name: map_name
        dtype: string
      - name: images
        sequence: string
      - name: system_prompt
        dtype: string
      - name: user_prompt
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 2468355
        num_examples: 512
    download_size: 305275
    dataset_size: 2468355

MARBLE: A Hard Benchmark for Multimodal Spatial Reasoning and Planning

🌐 Homepage | 📖 Paper | 🤗 Dataset | 🔗 Code

Introduction

MARBLE is a challenging multimodal reasoning benchmark designed to scrutinize multimodal language models (MLLMs) in their ability to carefully reason step-by-step through complex multimodal problems and environments. MARBLE is composed of two highly challenging tasks, M-Portal and M-Cube, that require the crafting and understanding of multistep plans leveraging spatial, visual, and physical constraints. We find that current MLLMs perform poorly on MARBLE—all the 12 advanced models obtain near-random performance on M-Portal and 0% accuracy on M-Cube. Only in simplified subtasks some models outperform the random baseline, indicating that complex reasoning is still a challenge for existing MLLMs. Moreover, we show that perception remains a bottleneck, where MLLMs occasionally fail to extract information from the visual inputs. By shedding a light on the limitations of MLLMs, we hope that MARBLE will spur the development of the next generation of models with the ability to reason and plan across many, multimodal reasoning steps.

Dataset Details

The benchmark consists of two datasets M-Portal and M-CUBE, each also contains 2 subtasks respectively (portal_binary and portal_blanks for M-PORTAL and cube and cube_easy for M-CUBE). Besides, M-CUBE also contains a simple perception tasks cube_perception.

M-PORTAL: multi-step spatial-planning puzzles modelled on levels from Portal 2.
- map_name: Portal 2 map name.
- images: images for each map (images.zip).
- system_prompt and user_prompt: instruction of the problem.
- answer: solution.
M-CUBE: 3D Cube assemblies from six jigsaw pieces, inspired by Happy Cube puzzles.
- image: image of 6 jigsaw pieces.
- face_arrays: 6 jigsaw pieces converted to binary arrays (0=gap, 1=bump).
- question: instruction of the Happy Cube Puzzle.
- reference_solution: one of the valid solutions.

Evaluation

Please refer to 🔗 Code

Overall Results

Performance on M-PORTAL:

Model	Plan-correctness (F1 %)	Fill-the-blanks (Acc %)
GPT-o3	6.6	17.6
Gemini-2.5-pro	4.7	16.1
DeepSeek-R1-0528*	0.0	8.4
Claude-3.7-Sonnet	6.3	6.8
DeepSeek-R1*	6.1	5.5
Seed1.5-VL	7.6	3.5
GPT-o4-mini	0.0	3.1
GPT-4o	6.5	0.4
Llama-4-Scout	6.5	0.2
Qwen2.5-VL-72B	6.6	0.2
InternVL3-78B	6.4	0.0
Qwen3-235B-A22B*	0.0	0.0
Random	6.1	3e-3

Performance on M-CUBE:

Model	CUBE (Acc %)	CUBE-easy (Acc %)
GPT-o3	0.0	72.0
GPT-o4-mini	0.0	16.0
DeepSeek-R1*	0.0	14.0
Gemini-2.5-pro	0.0	11.0
DeepSeek-R1-0528*	0.0	8.0
Claude-3.7-Sonnet	0.0	7.4
InternVL3-78B	0.0	2.8
Seed1.5-VL	0.0	2.0
GPT-4o	0.0	2.0
Qwen2.5-VL-72B	0.0	2.0
Llama-4-Scout	0.0	1.6
Qwen3-235B-A22B*	0.0	0.3
Random	1e-5	3.1

Contact

Yulun Jiang: [email protected]

BibTex

@article{jiang2025marble,
  title={MARBLE: A Hard Benchmark for Multimodal Spatial Reasoning and Planning},
  author={Jiang, Yulun and Chai, Yekun and Brbi'c, Maria and Moor, Michael},
  journal={arXiv preprint arXiv:2506.22992},
  year={2025},
  url={https://arxiv.org/abs/2506.22992}
}