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---
license: cc-by-4.0
---
# Dataset Card for PackBench 🧳
## Dataset Summary
**PackBench** is a suite of visual-spatial reasoning tasks where language models are asked to "pack" items into virtual suitcases. Each suitcase is represented as a grid that folds in half, and models must determine the correct location to place a missing item based on a mirrored folding operation. The dataset is designed to evaluate LLMs' abilities in spatial reasoning, mirroring transformations, and structured decision-making.
PackBench is structured as a collection of multiple-choice or short-answer evaluation tasks with clear visual-textual instructions and examples. It is ideal for evaluating models that claim multi-step spatial inference capabilities.
---
## Supported Tasks and Leaderboards
**Task:** `visual spatial reasoning`
**Type:** `Evaluation / Benchmarks`
**Format:** Prompt-based QA with grounded visual instructions (ASCII art).
**Answer format:** Coordinates in `\boxed{(x, y)}` format.
**Evaluation Metric:** Exact match with allowed correct boxed answers.
---
## Languages
English (`en`)
---
## Dataset Structure
Each example contains:
* `"question"`: A list of one user message with the prompt.
* `"answer"`: A dictionary with accepted answer(s) (using `contains_any` for flexibility).
### Example Entry
```json
{
"question": [
{
"role": "user",
"content": "You are an expert at packing suitcases.\nYou must place an item in an empty slot..."
}
],
"answer": {
"type": "contains_any",
"contains_any": ["\\boxed{(3, 2)}"]
}
}
```
---
## Dataset Creation
The dataset was procedurally generated using a Python script. For each suitcase:
1. The suitcase is defined as a 2D grid split into two halves.
2. One cell in the **folded** final view is left empty.
3. The folded state is decomposed into a plausible left and right half (non-overlapping).
4. The model must reason about folding the left side over the right to determine where the empty cell is in the final folded suitcase.
This mirrors a cognitive visual-spatial task often found in human IQ or pattern reasoning tests.
Suitcase sizes range from `2x3` to `10x20` (i.e., up to 400 cells), testing both fine-grained spatial reasoning and scale handling.
---
## Sizes
PackBench includes suitcases of varying complexity:
* Sizes: From `3x6` up to `20x40`
* Number of examples per size: `20`
* Total examples: **360**
---
## Intended Use
### Use Cases
* Evaluate the **spatial reasoning** capabilities of large language models (LLMs).
* Benchmark models trained on visual or multimodal reasoning tasks.
* Include in broader diagnostic evaluation sets for LLM alignment, logical reasoning, and task generalization.
* Test raw reasoning especially at larger sizes (10x20+).
### Limitations
* ASCII art may be misinterpreted by purely text-based models not trained for structured visual parsing.
* Assumes the model understands spatial mirroring and coordinate systems.
---
## Citation
If you use PackBench in your research or applications, please cite it as:
```bibtex
@misc{packbench2025,
title={PackBench: A Spatial Reasoning Benchmark for Language Models},
year={2025},
author={{Deca AI}},
howpublished={\url{https://huggingface.co/datasets/deca-ai/packbench}},
}
```
---
## License
CC BY 4.0
---
## Tags
`llm-evaluation` · `spatial-reasoning` · `benchmarks` · `folding` · `mirroring` · `suitcase` · `ASCII` · `reasoning` · `alignment`
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