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README.md

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-license: cc-by-4.0
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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`
+
+