README.md

---
language:
- en
license: other
pretty_name: XL-DocBench
task_categories:
- question-answering
tags:
- document-understanding
- long-context
- multimodal-document-ai
- cross-document-qa
- benchmark
size_categories:
- 1K<n<10K
configs:
- config_name: single_doc
  default: true
  data_files:
  - split: test
    path: data/qa_single_doc.jsonl
- config_name: cross_doc
  data_files:
  - split: test
    path: data/qa_cross_doc.jsonl
- config_name: documents
  data_files:
  - split: documents
    path: data/documents.jsonl
- config_name: scores
  data_files:
  - split: results
    path: results/scores.jsonl
---

<div align="center">

<h1>XL-DocBench</h1>

<p>
  Evidence-grounded reasoning across hundreds or thousands of pages.<br>
  Fully verified by 194 human experts.
</p>

<p>
  Hongchen Wei<sup>1,†,‡</sup>, Yuanzhe Wang<sup>2,†,‡</sup>,
  Bei Liu<sup>2,*</sup>, Yifan Yang<sup>2</sup>, Qi Dai<sup>2</sup>,
  Ruichun Ma<sup>2</sup>, Kai Qiu<sup>2</sup>, Yunsheng Li<sup>2</sup>,<br>
  Dongdong Chen<sup>2</sup>, Chong Luo<sup>2</sup>,
  Zhenzhong Chen<sup>1</sup>, Baining Guo<sup>2</sup>
</p>

<p>
  <sup>1</sup>Wuhan University &nbsp; <sup>2</sup>Microsoft &nbsp;
  <sup>†</sup>Equal contribution &nbsp; <sup>‡</sup>Work done during an internship at MSRA &nbsp;
  <sup>*</sup>Project leader
</p>

<p>
  <a href="https://officeintelligence.github.io/xl-docbench/"><b>Project Page</b></a> ·
  <a href="https://arxiv.org/abs/2608.00036"><b>Paper</b></a> ·
  <a href="https://officeintelligence.github.io/xl-docbench/#leaderboard"><b>Live Leaderboard</b></a>
</p>

<p>
  <a href="https://arxiv.org/abs/2608.00036"><img src="https://img.shields.io/badge/arXiv-2608.00036-b31b1b.svg" alt="arXiv"></a>
  <img src="https://img.shields.io/badge/questions-1,345-3b5b92.svg" alt="1,345 questions">
  <img src="https://img.shields.io/badge/documents-292-6a4c93.svg" alt="292 documents">
  <img src="https://img.shields.io/badge/human_verified-194_experts-2d6a4f.svg" alt="Verified by 194 experts">
</p>

</div>

## TL;DR

This is the **conservative XL-DocBench release**. It contains
**1,345 QA rows over 292 documents** after
removing every question that touches an exact source URL marked
`RAG: Not Approved`.

## This release

**292 documents** · **1,191 single-document QA** ·
**154 cross-document QA** · **1,345 total QA**

- `data/documents.jsonl`: retained document metadata and source URLs.
- `data/qa_single_doc.jsonl`: retained single-document questions.
- `data/qa_cross_doc.jsonl`: retained cross-document questions.
- `manifest.json`: recomputed release statistics.
- `results/scores.jsonl`: per-question scores for the 13 reproducible systems.
- `results/summary.json`: aggregate scores for the same systems.
- `code/quickstart.py`: one-command data and evaluator smoke test.
- `code/evaluate.py`: self-contained deterministic evaluator.

The filter uses exact URL matching. This variant addresses exact `Not Approved`
RAG rows only and does not interpret separate `Portions Approved` entries.

## About XL-DocBench

XL-DocBench asks systems to **find the evidence, combine all required support,
apply the right rule, and know when to abstain**. This strict release contains
1,345 expert-verified questions from six professional domains. Among 1,280
records with parseable historical human page annotations, 975 (76.2%) use
multiple evidence pages. Released supporting evidence is multimodal for 429
questions (31.9%), 154 questions (11.4%) use cross-document contexts, and 188
require a `None` answer. Full-series contexts reach 2,935 pages.

## Reproducible strict results

All systems below have complete scores for the 1,345 retained IDs and use the
hardened evaluator shipped in this release.

| Rank | System | Input | Accuracy ↑ | Token F1 ↑ | ANLS ↑ |
|---:|---|:---:|---:|---:|---:|
| 1 | **GPT-5.4** | OCR | **38.36** | **39.70** | **34.17** |
| 2 | SimpleDoc + GPT-5.4 | Agent | 36.21 | 33.62 | 24.30 |
| 3 | Kimi-K2.5 | OCR | 36.06 | 38.24 | 32.64 |
| 4 | MDocAgent + GPT-5.4 | Agent | 31.82 | 30.58 | 22.64 |
| 5 | GPT-5.2 | OCR | 31.15 | 33.69 | 28.51 |
| 6 | DeepSeek-V3.2 | OCR | 29.67 | 32.99 | 28.91 |
| 7 | Qwen3.5-4B | OCR | 29.00 | 32.23 | 28.13 |
| 8 | DeepRead + GPT-5.4 | Agent | 27.73 | 26.99 | 21.17 |
| 9 | GPT-5.4 | Img | 26.77 | 29.76 | 26.33 |
| 10 | Kimi-K2.5 | Img | 26.02 | 27.59 | 23.04 |
| 11 | GPT-5.2 | Img | 21.71 | 24.95 | 21.92 |
| 12 | Qwen3.5-4B | Img | 20.52 | 20.86 | 18.26 |
| 13 | Qwen3.5-9B | Img | 20.22 | 22.69 | 20.18 |

## Per-question scores

Source documents are referenced by public URLs rather than redistributed.
Because some URLs may change or become unavailable over time, we also provide
the benchmark scores for every retained question ID. This gives future users a
stable comparison point even when a source URL is temporarily unavailable.

- `results/scores.jsonl`: one row for each of the 1,345 question IDs.
- `results/summary.json`: aggregate Accuracy, Token F1, and ANLS.

Scores are stored on a 0-to-1 scale. 

## Quick start

Validate the complete release and run a five-question evaluation fixture:

```bash
uv run --no-project python code/quickstart.py
```

Load all three JSONL tables with the included standard-library example:

```bash
uv run --no-project python code/examples/load_data.py
```

## Evaluation

Try the bundled five-question example:

```bash
uv run --no-project python code/evaluate.py \
  --gold-files code/examples/gold_sample.jsonl \
  --predictions code/examples/predictions_sample.jsonl
```

For a complete run, provide one prediction per question:

```jsonl
{"question_id": "adubench_single_000001", "prediction": "the biggest single risk to human health worldwide"}
{"question_id": "adubench_cross_000001", "prediction": "macroprudential measures"}
```

```bash
uv run --no-project python code/evaluate.py \
  --predictions predictions.jsonl \
  --output eval_report.json \
  --per-question-csv per_question.csv
```

The evaluator reports rule-based **Accuracy**, token-level **F1**, and **ANLS**.
Missing, failed, and unparsable predictions count as incorrect unless
`--ignore-missing` is enabled.

## Record structure

```text
question
├── answer: value + format + verification rule
├── document / documents
│   ├── document_id + public URL
│   ├── evidence_pages: one-based PDF/release page indices
│   └── evidence_items: annotator locator + page references + excerpt kind
└── metadata: domain + difficulty + reasoning type + answerability
```

`evidence_pages` locate pages in the released PDF context. Evidence-item
`pages` preserve annotator-supplied page references; printed pagination can
differ from PDF indices. The `page_numbering` and `evidence_kind` fields make
those cases explicit. Row-level `unassigned_evidence_items` are retained as
provenance and are not counted as released supporting evidence.

PDF binaries and local filenames are not included. Source documents remain
subject to their original licenses and terms; this release does not grant
redistribution rights for third-party PDFs.

## Citation

```bibtex
@article{wei2026xldocbench,
  title   = {XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understanding},
  author  = {Wei, Hongchen and Wang, Yuanzhe and Liu, Bei and Yang, Yifan and Dai, Qi and Ma, Ruichun and Qiu, Kai and Li, Yunsheng and Chen, Dongdong and Luo, Chong and Chen, Zhenzhong and Guo, Baining},
  journal = {arXiv preprint arXiv:2608.00036},
  year    = {2026}
}
```