---
license: gpl-3.0
task_categories:
  - visual-question-answering
  - question-answering
language:
  - en
tags:
  - abstract-reasoning
  - cognitive-evaluation
  - raven-progressive-matrices
  - neuropsychology
  - llm-evaluation
pretty_name: NeuroCognition RAPM
size_categories:
  - n<1K
---

# NeuroCognition RAPM Dataset

Evaluation dataset for the paper **"A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities"** ([arXiv:2603.02540](https://arxiv.org/abs/2603.02540)), submitted to NeurIPS 2026 Evaluations & Datasets Track.

This dataset contains two variants of Raven's Progressive Matrices (RAPM) used to evaluate abstract reasoning in large language models:

## Dataset Structure

### Visual RAPM (`visual_rapm.json` + `images/`)

- **140 items** drawn from the [RAVEN dataset](https://github.com/WellyZhang/RAVEN) (Zhang et al., CVPR 2019)
- Each item is a composite image combining the 3×3 problem matrix and 8 answer choices into a single figure, rendered from raw RAVEN `.npz` files using matplotlib
- Covers 5 RAVEN dataset types: `center_single`, `distribute_four`, `distribute_nine`, `in_center_single_out_center_single`, `left_center_single_right_center_single`, `up_center_single_down_center_single`

**Fields:**
| Field | Description |
|---|---|
| `id` | Unique identifier (e.g. `RAVEN_238_test_center_single`) |
| `dataset_type` | RAVEN configuration type |
| `full_image` | Path to composite image (problem matrix + answer choices) |
| `correct_answer` | Index of the correct answer (0-based, out of 8 choices) |

### Text RAPM (`text_rapm.jsonl`)

- **200 items** of an original text-based analogue of RAPM
- Each item presents a 3×3 matrix of character strings instead of geometric figures, where cells follow quantitative or structural rules across rows and columns
- Minimum difficulty of 20 (axis attempts) and designed to require multi-step rule inference

**Fields:**
| Field | Description |
|---|---|
| `question_grid` | 3×3 list of strings (last cell is `null`) |
| `options` | 8 candidate answers |
| `correct_index` | Index of the correct answer |
| `answer` | Correct answer string |
| `row_attribute` | Rule governing rows |
| `col_attribute` | Rule governing columns |
| `cell_constraints` | Per-cell character constraints |
| `axis_attempts` | Difficulty indicator (generation attempts needed) |

## Usage

```python
import json

# Visual RAPM
with open("visual_rapm.json") as f:
    visual_data = json.load(f)["questions"]

# Text RAPM
with open("text_rapm.jsonl") as f:
    text_data = [json.loads(line) for line in f]
```

## Scripts

`scripts/render_raven_images.py` contains the script used to render composite images from raw RAVEN `.npz` files. Raw `.npz` files are available from the original [RAVEN repository](https://github.com/WellyZhang/RAVEN).

## License & Attribution

**Visual RAPM images** are derived from the RAVEN dataset and are distributed under the same [GPL-3.0 license](https://github.com/WellyZhang/RAVEN/blob/master/LICENSE). Please cite the original RAVEN paper:

```bibtex
@inproceedings{zhang2019raven,
  title={RAVEN: A Dataset for Relational and Analogical Visual rEasoNing},
  author={Zhang, Chi and Gao, Feng and Jia, Baoxiong and Zhu, Yixin and Zhu, Song-Chun},
  booktitle={CVPR},
  year={2019}
}
```

**Text RAPM** is original work by the authors of this paper.

If you use this dataset, please also cite our paper:

```bibtex
@article{haznitrama2026neurocognition,
  title={A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities},
  author={Haznitrama, Faiz Ghifari and Ardi, Faeyza Rishad and Oh, Alice},
  journal={arXiv:2603.02540},
  year={2026}
}
```

## RAI Metadata

- **Intended use**: Evaluation of abstract reasoning in LLMs; research only
- **Out-of-scope use**: Training data for commercial models without respecting GPL-3.0 terms
- **Data limitations**: Visual RAPM items are a subset (140/10000) of RAVEN test split; text RAPM uses ASCII-like character strings which may not fully capture visuospatial reasoning
- **Sensitive information**: None
- **Social impact**: Contributes to understanding of cognitive capabilities and limitations of LLMs, grounded in neuropsychological theory
- **Provenance**: Visual RAPM rendered from RAVEN-10000 (Zhang et al., CVPR 2019); Text RAPM generated by the authors