Datasets:

OxRML
/

MADQA

license: cc-by-nc-4.0
dataset_info:
  features:
    - name: id
      dtype: string
    - name: question
      dtype: string
    - name: answer_variants
      list:
        list: string
    - name: evidence
      list:
        - name: document
          dtype: string
        - name: page
          dtype: int64
    - name: document_category
      dtype: string
    - name: domain
      dtype: string
  splits:
    - name: train
      num_bytes: 320371
      num_examples: 1550
    - name: dev
      num_bytes: 49432
      num_examples: 200
    - name: test
      num_bytes: 57522
      num_examples: 500
  download_size: 223929
  dataset_size: 427325
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*
  - config_name: documents
    data_files:
      - split: links
        path: data/documents/document_urls-*
task_categories:
  - visual-document-retrieval
  - visual-question-answering
language:
  - en
tags:
  - benchmark
  - agent
  - document
  - multimodal
  - RAG
size_categories:
  - 1K<n<10K
extra_gated_prompt: >-
  ## License and Disclaimer

  The material in this repo is intended for non-commercial research purposes and
  is provided under the Creative Commons Attribution-NonCommercial (CC BY-NC)
  license.


  ## Data Availability and DocumentCloud Access Notice

  The benchmark described in this [research](https://arxiv.org/abs/2504.xxxxx)
  consists of a curated index of documents hosted on
  [DocumentCloud.org](http://DocumentCloud.org) which were uploaded to
  DocumentCloud by third-party organizations (e.g., news outlets, government
  agencies, non-profits, etc.).  Please refer to [DocumentCloud's Terms of
  Service](https://www.muckrock.com/tos/) for information regarding the rights
  of the underlying documents.

  1. Independent Data Source: Reasoning with Machines Lab at University of
  Oxford and its co-authors do not own, host, or control DocumentCloud.org or
  the third party documents referenced in the benchmark. The links provided
  point directly to the original public-facing documents on the DocumentCloud
  platform.

  2. Terms of Service & API Compliance: Users accessing these documents via the
  DocumentCloud API or web interface are responsible for complying with the
  [DocumentCloud Terms of Service](https://www.muckrock.com/tos/) and any
  specific usage restrictions or rate limits (e.g., those governing automated
  downloads) set by the platform and the original document uploader.

  3. For Non-Commercial Research Only: This index is provided solely for
  non-commercial, scientific benchmarking and research. Any commercial use of
  the documents found on DocumentCloud, including but not limited to the
  training of commercial Large Language Models (LLMs) or other proprietary
  products, may require separate licensing or permission from the original
  copyright holders.

  4. Liability Disclaimer: Reasoning with Machines Lab at University of Oxford
  and its co-authors provide this index "as-is" and without any warranty
  regarding the copyright status, data integrity, or accuracy of the linked
  documents. Users are solely liable for any copyright infringement or other
  legal claims arising from their downloading or use of the materials.

MADQA Dataset

An agentic document question-answering benchmark with 2250 questions over a collection of 800 real-world PDF documents spanning multiple domains.

For more details, see the paper, leaderboard, and code.

Splits

Split	Questions	Description
train	1,550	Training set
dev	200	Development/validation set
test	500	Held-out test set (answers hidden)

Schema

Column	Type	Description
`id`	string	Unique identifier (`split/index`)
`question`	string	Natural language question
`answer_variants`	list[list[str]]	Acceptable answer variants (hidden for test)
`evidence`	list[{document, page}]	Gold evidence locations (hidden for test)
`document_category`	string	Document type (e.g., "Annual Report")
`domain`	string	High-level domain (e.g., "Financial")

Usage

from datasets import load_dataset

ds = load_dataset("OxRML/MADQA")

# Access splits
train = ds["train"]
dev = ds["dev"]
test = ds["test"]

# Example
print(train[0]["question"])

PDF Documents

PDF documents are hosted externally. The "documents" configuration provides a mapping from document filenames to their download URLs.

Setup

from datasets import load_dataset, DownloadManager

ds = load_dataset("OxRML/MADQA")
docs = load_dataset("OxRML/MADQA", "documents", split="links")
dm = DownloadManager()

# Build lookup: filename -> URL
doc_urls = {r["document"]: r["url"] for r in docs}

Listing All Documents

print(f"Found {len(docs)} PDF documents")
for row in docs:
    print(f"  {row['document']}")

Downloading and Reading a PDF

import fitz  # PyMuPDF

# Download a specific PDF (cached locally after first download)
pdf_path = dm.download(doc_urls["6414850.pdf"])

# Read with PyMuPDF
pdf = fitz.open(pdf_path)
print(f"Pages: {len(pdf)}")

# Extract text from first page
text = pdf[0].get_text()
print(text[:500])

Citation

If you use MADQA in your research, please cite:

@misc{borchmann2026madqa,
  title        = {Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections},
  author       = {Łukasz Borchmann and Jordy Van Landeghem and Michał Turski and Shreyansh Padarha and Ryan Othniel Kearns and Adam Mahdi and Niels Rogge and Clémentine Fourrier and Siwei Han and Huaxiu Yao and Artemis Llabrés and Yiming Xu and Dimosthenis Karatzas and Hao Zhang and Anupam Datta},
  year         = {2026},
  eprint       = {2603.12180},
  archivePrefix= {arXiv},
  primaryClass = {cs.CL},
  url          = {https://arxiv.org/abs/2603.12180}
}