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README.md

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+---
+license: mit
+task_categories:
+  - visual-question-answering
+  - image-classification
+language:
+  - en
+tags:
+  - maze
+  - spatial-reasoning
+  - multimodal
+  - vision-language
+  - benchmark
+size_categories:
+  - 10K<n<100K
+---
+
+# MazeBench
+
+A large-scale visual maze reasoning dataset for evaluating and training multimodal language models.
+
+**Paper:** [From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning](https://arxiv.org/abs/2603.26839)
+
+**Code:** [github.com/alrod97/LLMs_mazes](https://github.com/alrod97/LLMs_mazes)
+
+## Overview
+
+MazeBench contains ~65,000 procedurally generated maze images spanning 8 structural families, 5 grid sizes (5x5 to 20x20), and balanced reachable/unreachable splits. Each record includes a maze PNG, a chat-formatted prompt, and a ground-truth JSON answer with the shortest path.
+
+## Splits
+
+| Split | Records | Purpose |
+| --- | ---: | --- |
+| `train` | ~50,000 | Training |
+| `val_iid` | ~2,000 | In-distribution validation (same families and palettes as train) |
+| `val_ood` | ~4,000 | Out-of-distribution validation (held-out palettes and families) |
+| `test` | ~9,000 | Held-out evaluation |
+
+## Maze Families
+
+- **sanity** — open corridors, minimal branching
+- **bottleneck** — narrow chokepoints
+- **dead_ends** — many dead-end branches
+- **snake** — long winding corridors
+- **symmetric** — mirror-symmetric layouts
+- **trap** — trap tiles that must be avoided
+- **near_goal_blocked** — goal is close but path is indirect
+- **near_start_blocked** — start area has limited exits
+
+## Record Format
+
+Each JSONL line contains:
+
+```json
+{
+  "id": "train_00001",
+  "image_path": "train/images/train_00001.png",
+  "messages": [
+    {"role": "system", "content": "..."},
+    {"role": "user", "content": "..."},
+    {"role": "assistant", "content": "{\"reachable\": true, \"shortest_path_length\": 12, \"path\": [\"R\",\"D\",...]}"}
+  ],
+  "meta": {
+    "family": "bottleneck",
+    "grid_size": [10, 10],
+    "reachable": true,
+    "shortest_path_length": 12,
+    "palette": "forest"
+  }
+}
+```
+
+## Directory Structure
+
+```
+train/
+  train.jsonl
+  images/
+    train_00001.png
+    ...
+val_iid/
+  val_iid.jsonl
+  images/
+val_ood/
+  val_ood.jsonl
+  images/
+test/
+  test.jsonl
+  images/
+metadata.json
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("albertoRodriguez97/MazeBench")
+```
+
+Or download directly:
+
+```bash
+pip install huggingface_hub
+huggingface-cli download albertoRodriguez97/MazeBench --repo-type dataset --local-dir dataset/
+```
+
+## Citation
+
+```bibtex
+@article{rodriguezsalgado2026mazebench,
+  title   = {From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning},
+  author  = {Rodriguez Salgado, Alberto},
+  journal = {arXiv preprint arXiv:2603.26839},
+  year    = {2026},
+}
+```
+
+## License
+
+MIT