README.md

---
license: apache-2.0
language:
- en
pretty_name: InSight-doc-SFT-18k
task_categories:
- visual-question-answering
- question-answering
tags:
- document-vqa
- long-document-understanding
- multimodal
- agentic-ai
- tool-use
- qwen3-vl
- supervised-fine-tuning
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# InSight-doc-SFT-18k

<p align="center">
  <img alt="InSight-doc logo" src="assets/insight_doc_logo.png" width="700" style="max-width: 100%;">
</p>

<h3 align="center">Agentic Visual Perception for Long-Document Understanding</h3>

<div align="center">

📄 **[Paper](https://arxiv.org/abs/2608.10628)** |
💻 **[Code](https://github.com/m-Just/InSight-doc)** |
🤗 **[Model](https://huggingface.co/InSight-doc/InSight-doc-8B)** |
🎯 **[RL Data](https://huggingface.co/datasets/m-Just/InSight-doc-RL-19k)** |
🎬 **[Replay Demo](https://vaynexie.github.io/insight-doc-demo-display/demo_display.html)** |
🚀 **[Live Demo](https://huggingface.co/spaces/leoyu112211/insight-doc-online-demo)**

</div>

<p align="center">
  <i>Understand the big picture.&nbsp; Focus on the right details.&nbsp; Answer from the evidence.</i>
</p>

**InSight-doc-SFT-18k** is the supervised fine-tuning corpus used to train the
InSight-doc long-document understanding agent. Each example is a complete
multimodal trajectory: the agent starts from low-resolution document pages,
zooms into selected regions, observes the returned crops, and produces a final
answer grounded in the visual evidence.

## Dataset Summary

The dataset contains 17,913 supervised trajectories for a Qwen3-VL-style agent
with an `image_zoom_in_tool`. The conversations include the user question,
assistant tool calls, tool observations, and the final assistant answer. The
release keeps the conversation, image, tool-schema, and source-label fields
needed to reproduce the SFT setup.

## Example Trajectory

The example below illustrates the coarse-to-fine supervision format: the agent
starts from low-resolution page views, issues zoom-in calls for relevant regions,
receives cropped observations, and answers from the collected evidence.

<p align="center">
  <img alt="Example InSight-doc SFT trajectory" src="assets/insight_doc_sft_example.png" width="900">
</p>

## Dataset Statistics

| Statistic | Value |
|---|---:|
| Rows | 17,913 |
| Answerable rows | 14,216 (79.36%) |
| Unanswerable rows | 3,697 (20.64%) |
| arXiv-derived rows | 8,115 |
| Non-arXiv rows | 9,798 |
| Mean document length | 17.75 pages |
| Mean zoom-in tool calls | 1.61 |

Resize ratios are relative to 200-DPI page renders:

| Resize ratio `r` | Approx. DPI | Rows | Share |
|---:|---:|---:|---:|
| 0.25 | 50 | 10,051 | 56.1% |
| 0.35 | 70 | 4,913 | 27.4% |
| 0.50 | 100 | 2,949 | 16.5% |

## Data Fields

| Field | Description |
|---|---|
| `id` | Stable row identifier in this release. |
| `images` | Ordered images referenced by `messages`, including initial page views and tool-returned crops. |
| `question` | Original user question. |
| `messages` | Chat-format trajectory with `system`, `user`, `assistant`, and `tool` roles. Image references appear as `Image k:<image>`. Assistant tool calls are stored inline in assistant message content. |
| `num_tool_calls` | Number of zoom-in tool calls in the trajectory. |
| `data_source` | Source/category label, including answerability. |
| `tools` | OpenAI/Qwen-style schema for `image_zoom_in_tool`. Pass this to `apply_chat_template` when reconstructing model inputs. |

The final answer is stored in the final assistant message in `messages`; there
is no separate `answer` column in the SFT release.

## Dataset Construction

The SFT data were produced by the InSight-doc data construction pipeline:

1. **Source QA collection.** The source pool combines non-arXiv document VQA data
   (MP-DocVQA, DUDE, poster QA, infographic QA, and map QA) with arXiv-derived
   scientific-document QA sources.
2. **Difficulty filtering.** For heterogeneous non-arXiv answerable rows, a
   low-resolution Qwen3-VL-8B filter removes questions that are already solved
   without zooming. All answerable sources then go through a stronger
   Qwen3-VL-32B no-zoom filter before trajectory generation. The arXiv rows are
   generated by an evidence-centric pipeline with explicit dependency checks,
   so they bypass the 8B gate but still use the 32B filter.
3. **Trajectory generation.** [InSight-o3](https://github.com/m-Just/InSight-o3)
   generates multi-turn zoom-in trajectories. The vReasoner identifies regions
   to inspect, the vSearcher localizes them, and the resulting crop is appended
   as a tool observation. Trajectories whose final answers match the reference
   answer are retained as SFT candidates.
4. **Unanswerable add-ons.** Naturally unanswerable DUDE/poster rows and
   verified synthetic unanswerable questions are included so the agent learns
   to abstain when the document does not contain sufficient evidence.
5. **Postprocessing.** Degenerate-trajectory removal drops malformed
   conversations, invalid tool arguments, missing answers, and unresolved image
   references. GPT-5-nano rewrites only the final assistant response from the
   [InSight-o3](https://github.com/m-Just/InSight-o3) "think + answer" style
   into a cleaner Qwen3-VL-Instruct-style final answer while preserving the tool
   trajectory.

## Trajectory Quality

Evidence-based metrics are computed only on answerable examples with evidence
annotations. Page and region hit rates also exclude trajectories with no crops.
For region hit, a crop hits an evidence box when it covers at least 50% of that
box.

| Metric | Value |
|---|---:|
| Rows with page evidence | 13,335 |
| Rows with box evidence | 6,925 |
| Evidence-page hit rate | 95.33% |
| Evidence-region hit rate, coverage >= 0.5 | 85.00% |
| Mean max evidence coverage | 85.24% |
| Mean max crop/evidence IoU | 52.37% |
| Crop region-hit rate | 65.34% |
| Crop area fraction | 14.50% |
| Same-source overlap rate, IoU >= 0.8 | 3.16% |
| Stuck rate | 0.99% |
| Stop exactly at first region hit | 71.14% |

## Loading And Prompt Reconstruction

```python
import copy
import re

from datasets import load_dataset
from transformers import AutoProcessor


def attach_images(messages, images):
    """Convert '<image>' placeholders into Qwen3-VL image content items."""
    messages = copy.deepcopy(messages)
    image_idx = 0
    for message in messages:
        content = message.get("content")
        if not isinstance(content, str) or "<image>" not in content:
            continue
        content_items = []
        for part in re.split(r"(<image>)", content):
            if not part:
                continue
            if part == "<image>":
                content_items.append({"type": "image", "image": images[image_idx]})
                image_idx += 1
            else:
                content_items.append({"type": "text", "text": part})
        message["content"] = content_items
    assert image_idx == len(images), (image_idx, len(images))
    return messages


processor = AutoProcessor.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct",
    trust_remote_code=True,
)
row = load_dataset("m-Just/InSight-doc-SFT-18k", split="train[:1]")[0]
messages = attach_images(row["messages"], row["images"])

prompt_text = processor.apply_chat_template(
    messages,
    tools=row["tools"],
    tokenize=False,
    add_generation_prompt=False,
)
model_inputs = processor(text=[prompt_text], images=row["images"], return_tensors="pt")
```

## License

The InSight-doc-added annotations, tool-use trajectories, and packaging metadata
are released under Apache-2.0. Source document images and source-derived content
may remain subject to their original upstream licenses and terms. Users should
review and respect the upstream terms where applicable.

## Citation

```bibtex
@article{li2026insightdoc,
  title={InSight-doc: Agentic Visual Perception for Long-Document Understanding},
  author={Li, Kaican and Xie, Weiyan and Yao, Lewei and Wu, Jiannan and Hong, Lanqing and Huang, Yongxiang and Zhang, Nevin L.},
  journal={arXiv preprint arXiv:2608.10628},
  year={2026}
}
```