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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.

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## 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.

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## Languages

English (`en`)

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## 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)}"]
  }
}
```

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## 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.

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## Sizes

PackBench includes suitcases of varying complexity:

* Sizes: From `3x6` up to `20x40`
* Number of examples per size: `20`
* Total examples: **360**

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## 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.

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## 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}},
}
```

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## License

CC BY 4.0

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## Tags

`llm-evaluation` · `spatial-reasoning` · `benchmarks` · `folding` · `mirroring` · `suitcase` · `ASCII` · `reasoning` · `alignment`