Datasets:

AlibabaQwenintern
/

gap-data

---
license: other
license_name: gap-data-per-source
license_link: https://huggingface.co/datasets/AlibabaQwenintern/gap-data#licenses
language:
- en
pretty_name: GAP Latent-Supervision Data
size_categories:
- 10K<n<100K
task_categories:
- visual-question-answering
- image-text-to-text
tags:
- multimodal
- visual-reasoning
- latent-reasoning
- chain-of-thought
- neurips-2026
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
  - split: validation
    path: val.jsonl
---
# GAP Latent-Supervision Data

This is the training data used for all results in **Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models** (NeurIPS 2026). It contains **36,985 examples**: 33,279 in `train` and 3,706 in `validation`, with no duplicates and no overlap between the splits.

The construction pipeline described in the paper produced 49,309 candidate examples. After preprocessing, 36,985 remain, and these are split 90/10 into the training and validation sets released here. <!-- TODO: optionally describe what preprocessing removes. -->

Each example pairs a query image and question with a structured teacher response. Examples that use latent supervision also mark where a visual latent goes and name the auxiliary image that supervises it.

An extended version used in follow-up experiments, which adds the official We-Math 2.0 SFT set and repeats some training examples, is available at [AlibabaQwenintern/gap-p2-data](https://huggingface.co/datasets/AlibabaQwenintern/gap-p2-data). The paper's results use the data in this repository.

## Composition

| `data_source` | Train | Validation | Examples with latent supervision (train + val) |
|---|---:|---:|---:|
| `monet_Visual_CoT` | 16,449 | 1,812 | 13,608 |
| `chart` | 6,187 | 671 | 5,831 |
| `GEOQA` | 3,760 | 401 | 1,174 |
| `monet_Zebra_CoT_visual_search` | 2,236 | 280 | 910 |
| `MM_Math` | 1,988 | 246 | 730 |
| `monet_Zebra_CoT_count` | 832 | 91 | 134 |
| `geo_3k` | 656 | 79 | 253 |
| `monet_CogCoM` | 401 | 43 | 376 |
| `MathVision` | 393 | 44 | 233 |
| `monet_ReFocus` | 358 | 38 | 282 |
| `monet_Zebra_CoT_geometry` | 19 | 1 | 17 |
| **Total** | **33,279** | **3,706** | **23,548** |

## How the data was built

1. **Auxiliary image.** For each training query, Nano Banana 2 (Gemini image generation) produces an intermediate auxiliary image intended to expose the visual evidence the question needs.
2. **Structured response.** The query image, question, and auxiliary image are given to Qwen3-VL-235B-A22B-Thinking (`reinfer_model: qwen3-vl-235b`), which writes the reasoning trace, a `<parser>` description of the intended auxiliary signal, and the final answer.
3. **Quality filtering.** Examples with degenerate auxiliary-image embeddings, or with `<parser>` outputs that fail a length or coherence check, are removed.
4. **Difficulty-aware assignment.** The base model (Qwen2.5-VL-7B) is sampled 8 times per query, and `accuracy` stores the fraction it solved. Only examples it never solved (accuracy 0) are marked `need_latent: true`. Examples it solved at least once keep text-only supervision.

## Response format

Assistant turns follow this structure:

```
<think> reasoning ... <|vision_start|><|image_pad|><|vision_end|>
<parser> description of the auxiliary visual signal </parser> continued reasoning </think>
<answer> final answer </answer>
```

The image placeholder inside the assistant turn marks where the visual latent goes. The image it refers to (listed in that turn's `image_filenames` with `image_types: "target"`) is the auxiliary image whose frozen-ViT features supervise the latent. In the paper's training code this placeholder becomes `<|latent_start|>`, 36 `<|latent_pad|>` tokens, and `<|latent_end|>`. Text-only examples (`need_latent: false`) have no image in the assistant turn.

## Files

| Path | Contents |
|---|---|
| `train.jsonl` | 33,279 training examples (216 MB) |
| `val.jsonl` | 3,706 validation examples (24 MB) |
| `vit_pca_mu.npy` | Mean vector of the vision-embedding PCA basis, shape (3584,), float32 |
| `vit_pca_W.npy` | Projection matrix of the vision-embedding PCA basis, shape (3584, 629), float32: 629 components retaining 95% of the variance of 3584-d vision embeddings |
| `images/*.tar` | Image archives, one per source folder (below); about 50 GB in total |

| Archive | Extracted folder | `data_source` values |
|---|---|---|
| `images/monet.tar` | `monet/` | `monet_Visual_CoT`, `monet_Zebra_CoT_*`, `monet_CogCoM`, `monet_ReFocus` |
| `images/chart.tar` | `chart/` | `chart` |
| `images/GEOQA.tar` | `GEOQA/` | `GEOQA` |
| `images/MM_MATH.tar` | `MM_MATH/` | `MM_Math` |
| `images/MATHVISION.tar` | `MATHVISION/` | `MathVision` |
| `images/geo3k_train.tar` | `geo3k_train/` | `geo_3k` |

## Download and restore

Access is gated: log in to Hugging Face and accept the conditions on this page before downloading.

```bash
hf auth login
hf download AlibabaQwenintern/gap-data --repo-type dataset --local-dir /root/data
cd /root/data
for f in images/*.tar; do tar -xf "$f" -C /root/data; done
```

With older versions of `huggingface_hub`, run `huggingface-cli download` with the same arguments.

After extraction the layout is:

```
/root/data/
├── train.jsonl
├── val.jsonl
├── vit_pca_mu.npy
├── vit_pca_W.npy
├── monet/
├── GEOQA/
├── MATHVISION/
├── MM_MATH/
├── chart/
└── geo3k_train/
```

Most image paths in the JSONL files are absolute and start with `/root/data/`; a small number are relative to that same root. Keep the data at `/root/data`, or rewrite the `/root/data/` prefix and resolve relative paths against your own root.

## Data fields

Each line of `train.jsonl` and `val.jsonl` is one example.

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique example identifier (UUID) |
| `data_source` | string | Source category (see Composition) |
| `data_type` | string | Always `"interleave"` |
| `message` | list | Two turns, `user` then `assistant` (fields below) |
| `reference_answer` | string | Reference answer text |
| `need_latent` | bool | Whether the example uses latent supervision |
| `accuracy` | float | Base-model accuracy over 8 samples |
| `reinfer_response` | string | Raw teacher response |
| `reinfer_model` | string | Teacher model, `"qwen3-vl-235b"` |
| `reinfer_prompt_template` | string | Prompt template used for the teacher |

Fields of each `message` turn:

| Field | Description |
|---|---|
| `role` | `"user"` or `"assistant"` |
| `dialog` | Turn text; images appear as `<\|vision_start\|><\|image_pad\|><\|vision_end\|>` |
| `image_filenames` | Image paths for the placeholders in `dialog`, in order |
| `image_types` | `"input"` for query images, `"target"` for auxiliary latent targets (omitted when the turn has no image) |
| `need_mask` | Always `false` in this release |

## Usage

After `hf auth login` and accepting the access conditions:

```python
from datasets import load_dataset
ds = load_dataset("AlibabaQwenintern/gap-data")  # train / validation metadata from the JSONL files
print(ds)
```

This loads the JSONL metadata only. Images come from the extracted archives (see "Download and restore").

## Leakage audit

We checked the curated corpus against the evaluation benchmarks used in the paper (HRBench4K, MMStar, MME-RealWorld-Lite, MathVista, We-Math, POPE). There are no exact image duplicates and no exact normalized question-text duplicates. Some high-similarity geometry cases remain, because GEOQA and Geometry3K are also reused by public evaluation benchmarks, so we do not claim the training and evaluation distributions are fully disjoint.

## Licenses

This dataset does not have a single license. Each example combines content taken from an upstream dataset with content we added, and each part keeps its own license:

- **Upstream content** (the query image and question text taken from a source dataset) keeps the license and terms of that source, listed below. The source of every example is recorded in `data_source`.
- **Our additions** (auxiliary images, teacher responses and reasoning traces, latent-supervision markup, the `need_latent` and `accuracy` labels, and the PCA basis files `vit_pca_mu.npy` and `vit_pca_W.npy`) are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/), for non-commercial research. The auxiliary images were generated with Nano Banana 2 through the Gemini API and are also subject to the Gemini API terms and prohibited-use policy; the teacher responses were generated with Qwen3-VL-235B-A22B-Thinking (Apache-2.0).

Using an example means following the terms of each of its parts.

| Upstream source | `data_source` | Upstream license / terms |
|---|---|---|
| Visual-CoT (`deepcs233/Visual-CoT`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) | `monet_Visual_CoT` | HF metadata lists Apache-2.0, but the dataset card states CC BY-NC 4.0 and research-purpose use; treated as CC BY-NC 4.0 |
| Zebra-CoT (`multimodal-reasoning-lab/Zebra-CoT`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) | `monet_Zebra_CoT_visual_search`, `monet_Zebra_CoT_count`, `monet_Zebra_CoT_geometry` | CC BY-NC 4.0 |
| CogCoM / CoMDataset (`qijimrc/CoMDataset`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) | `monet_CogCoM` | CC BY-SA 4.0 for dataset contributions; original image copyrights retained |
| ReFocus (`ReFocus/ReFocus_Data`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) | `monet_ReFocus` | Apache-2.0 |
| ChartQA (`ahmed-masry/ChartQA`; [vis-nlp/ChartQA](https://github.com/vis-nlp/ChartQA)) | `chart` | GPL-3.0. The ChartQA authors crawled the chart images from online sources such as Statista and Pew Research Center; image copyrights remain with the original owners |
| MM-MATH (`THU-KEG/MM_Math`) | `MM_Math` | MIT. Problems are collected from Chinese middle-school exams |
| MathVision (`MathLLMs/MathVision`) | `MathVision` | MIT |
| GeoQA (`hz2475/geoQA`) | `GEOQA` | MIT |
| Geometry3K (`xyliu6/geometry3k`) | `geo_3k` | Apache-2.0 |

## Intended use and limitations

- Intended for research on multimodal reasoning and visual latent modeling. Not intended for commercial use.
- Responses and auxiliary images are model-generated and may contain errors; the `<parser>` description states the intended auxiliary signal and is not a verified ground-truth caption.
- The data reflects the coverage of its source pools. In particular, real-world scenes are under-represented, which matches the weaker MME-RealWorld-Lite results reported in the paper.

## Citation

```bibtex
@inproceedings{miao2026gap,
  title     = {Fill the {GAP}: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models},
  author    = {Miao, Yanting and Sun, Yutao and Wang, Dexin and Zhou, Mengyu and Poupart, Pascal and Lv, Lei and Zhao, Qi and Wang, Li and Li, Hao and Jiang, Xiaoxi and Jiang, Guanjun},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2026}
}
```

Corresponding author: Mengyu Zhou.

	---
	license: other
	license_name: gap-data-per-source
	license_link: https://huggingface.co/datasets/AlibabaQwenintern/gap-data#licenses
	language:
	- en
	pretty_name: GAP Latent-Supervision Data
	size_categories:
	- 10K<n<100K
	task_categories:
	- visual-question-answering
	- image-text-to-text
	tags:
	- multimodal
	- visual-reasoning
	- latent-reasoning
	- chain-of-thought
	- neurips-2026
	configs:
	- config_name: default
	data_files:
	- split: train
	path: train.jsonl
	- split: validation
	path: val.jsonl
	---

	# GAP Latent-Supervision Data

	This is the training data used for all results in Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models (NeurIPS 2026). It contains 36,985 examples: 33,279 in `train` and 3,706 in `validation`, with no duplicates and no overlap between the splits.

	The construction pipeline described in the paper produced 49,309 candidate examples. After preprocessing, 36,985 remain, and these are split 90/10 into the training and validation sets released here. <!-- TODO: optionally describe what preprocessing removes. -->

	Each example pairs a query image and question with a structured teacher response. Examples that use latent supervision also mark where a visual latent goes and name the auxiliary image that supervises it.

	An extended version used in follow-up experiments, which adds the official We-Math 2.0 SFT set and repeats some training examples, is available at [AlibabaQwenintern/gap-p2-data](https://huggingface.co/datasets/AlibabaQwenintern/gap-p2-data). The paper's results use the data in this repository.

	## Composition

	\| `data_source` \| Train \| Validation \| Examples with latent supervision (train + val) \|
	\|---\|---:\|---:\|---:\|
	\| `monet_Visual_CoT` \| 16,449 \| 1,812 \| 13,608 \|
	\| `chart` \| 6,187 \| 671 \| 5,831 \|
	\| `GEOQA` \| 3,760 \| 401 \| 1,174 \|
	\| `monet_Zebra_CoT_visual_search` \| 2,236 \| 280 \| 910 \|
	\| `MM_Math` \| 1,988 \| 246 \| 730 \|
	\| `monet_Zebra_CoT_count` \| 832 \| 91 \| 134 \|
	\| `geo_3k` \| 656 \| 79 \| 253 \|
	\| `monet_CogCoM` \| 401 \| 43 \| 376 \|
	\| `MathVision` \| 393 \| 44 \| 233 \|
	\| `monet_ReFocus` \| 358 \| 38 \| 282 \|
	\| `monet_Zebra_CoT_geometry` \| 19 \| 1 \| 17 \|
	\| Total \| 33,279 \| 3,706 \| 23,548 \|

	## How the data was built

	1. Auxiliary image. For each training query, Nano Banana 2 (Gemini image generation) produces an intermediate auxiliary image intended to expose the visual evidence the question needs.
	2. Structured response. The query image, question, and auxiliary image are given to Qwen3-VL-235B-A22B-Thinking (`reinfer_model: qwen3-vl-235b`), which writes the reasoning trace, a `<parser>` description of the intended auxiliary signal, and the final answer.
	3. Quality filtering. Examples with degenerate auxiliary-image embeddings, or with `<parser>` outputs that fail a length or coherence check, are removed.
	4. Difficulty-aware assignment. The base model (Qwen2.5-VL-7B) is sampled 8 times per query, and `accuracy` stores the fraction it solved. Only examples it never solved (accuracy 0) are marked `need_latent: true`. Examples it solved at least once keep text-only supervision.

	## Response format

	Assistant turns follow this structure:

	```
	<think> reasoning ... <\|vision_start\|><\|image_pad\|><\|vision_end\|>
	<parser> description of the auxiliary visual signal </parser> continued reasoning </think>
	<answer> final answer </answer>
	```

	The image placeholder inside the assistant turn marks where the visual latent goes. The image it refers to (listed in that turn's `image_filenames` with `image_types: "target"`) is the auxiliary image whose frozen-ViT features supervise the latent. In the paper's training code this placeholder becomes `<\|latent_start\|>`, 36 `<\|latent_pad\|>` tokens, and `<\|latent_end\|>`. Text-only examples (`need_latent: false`) have no image in the assistant turn.

	## Files

	\| Path \| Contents \|
	\|---\|---\|
	\| `train.jsonl` \| 33,279 training examples (216 MB) \|
	\| `val.jsonl` \| 3,706 validation examples (24 MB) \|
	\| `vit_pca_mu.npy` \| Mean vector of the vision-embedding PCA basis, shape (3584,), float32 \|
	\| `vit_pca_W.npy` \| Projection matrix of the vision-embedding PCA basis, shape (3584, 629), float32: 629 components retaining 95% of the variance of 3584-d vision embeddings \|
	\| `images/*.tar` \| Image archives, one per source folder (below); about 50 GB in total \|

	\| Archive \| Extracted folder \| `data_source` values \|
	\|---\|---\|---\|
	\| `images/monet.tar` \| `monet/` \| `monet_Visual_CoT`, `monet_Zebra_CoT_*`, `monet_CogCoM`, `monet_ReFocus` \|
	\| `images/chart.tar` \| `chart/` \| `chart` \|
	\| `images/GEOQA.tar` \| `GEOQA/` \| `GEOQA` \|
	\| `images/MM_MATH.tar` \| `MM_MATH/` \| `MM_Math` \|
	\| `images/MATHVISION.tar` \| `MATHVISION/` \| `MathVision` \|
	\| `images/geo3k_train.tar` \| `geo3k_train/` \| `geo_3k` \|

	## Download and restore

	Access is gated: log in to Hugging Face and accept the conditions on this page before downloading.

	```bash
	hf auth login
	hf download AlibabaQwenintern/gap-data --repo-type dataset --local-dir /root/data
	cd /root/data
	for f in images/*.tar; do tar -xf "$f" -C /root/data; done
	```

	With older versions of `huggingface_hub`, run `huggingface-cli download` with the same arguments.

	After extraction the layout is:

	```
	/root/data/
	├── train.jsonl
	├── val.jsonl
	├── vit_pca_mu.npy
	├── vit_pca_W.npy
	├── monet/
	├── GEOQA/
	├── MATHVISION/
	├── MM_MATH/
	├── chart/
	└── geo3k_train/
	```

	Most image paths in the JSONL files are absolute and start with `/root/data/`; a small number are relative to that same root. Keep the data at `/root/data`, or rewrite the `/root/data/` prefix and resolve relative paths against your own root.

	## Data fields

	Each line of `train.jsonl` and `val.jsonl` is one example.

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `id` \| string \| Unique example identifier (UUID) \|
	\| `data_source` \| string \| Source category (see Composition) \|
	\| `data_type` \| string \| Always `"interleave"` \|
	\| `message` \| list \| Two turns, `user` then `assistant` (fields below) \|
	\| `reference_answer` \| string \| Reference answer text \|
	\| `need_latent` \| bool \| Whether the example uses latent supervision \|
	\| `accuracy` \| float \| Base-model accuracy over 8 samples \|
	\| `reinfer_response` \| string \| Raw teacher response \|
	\| `reinfer_model` \| string \| Teacher model, `"qwen3-vl-235b"` \|
	\| `reinfer_prompt_template` \| string \| Prompt template used for the teacher \|

	Fields of each `message` turn:

	\| Field \| Description \|
	\|---\|---\|
	\| `role` \| `"user"` or `"assistant"` \|
	\| `dialog` \| Turn text; images appear as `<\\|vision_start\\|><\\|image_pad\\|><\\|vision_end\\|>` \|
	\| `image_filenames` \| Image paths for the placeholders in `dialog`, in order \|
	\| `image_types` \| `"input"` for query images, `"target"` for auxiliary latent targets (omitted when the turn has no image) \|
	\| `need_mask` \| Always `false` in this release \|

	## Usage

	After `hf auth login` and accepting the access conditions:

	```python
	from datasets import load_dataset

	ds = load_dataset("AlibabaQwenintern/gap-data") # train / validation metadata from the JSONL files
	print(ds)
	```

	This loads the JSONL metadata only. Images come from the extracted archives (see "Download and restore").

	## Leakage audit

	We checked the curated corpus against the evaluation benchmarks used in the paper (HRBench4K, MMStar, MME-RealWorld-Lite, MathVista, We-Math, POPE). There are no exact image duplicates and no exact normalized question-text duplicates. Some high-similarity geometry cases remain, because GEOQA and Geometry3K are also reused by public evaluation benchmarks, so we do not claim the training and evaluation distributions are fully disjoint.

	## Licenses

	This dataset does not have a single license. Each example combines content taken from an upstream dataset with content we added, and each part keeps its own license:

	- Upstream content (the query image and question text taken from a source dataset) keeps the license and terms of that source, listed below. The source of every example is recorded in `data_source`.
	- Our additions (auxiliary images, teacher responses and reasoning traces, latent-supervision markup, the `need_latent` and `accuracy` labels, and the PCA basis files `vit_pca_mu.npy` and `vit_pca_W.npy`) are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/), for non-commercial research. The auxiliary images were generated with Nano Banana 2 through the Gemini API and are also subject to the Gemini API terms and prohibited-use policy; the teacher responses were generated with Qwen3-VL-235B-A22B-Thinking (Apache-2.0).

	Using an example means following the terms of each of its parts.

	\| Upstream source \| `data_source` \| Upstream license / terms \|
	\|---\|---\|---\|
	\| Visual-CoT (`deepcs233/Visual-CoT`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) \| `monet_Visual_CoT` \| HF metadata lists Apache-2.0, but the dataset card states CC BY-NC 4.0 and research-purpose use; treated as CC BY-NC 4.0 \|
	\| Zebra-CoT (`multimodal-reasoning-lab/Zebra-CoT`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) \| `monet_Zebra_CoT_visual_search`, `monet_Zebra_CoT_count`, `monet_Zebra_CoT_geometry` \| CC BY-NC 4.0 \|
	\| CogCoM / CoMDataset (`qijimrc/CoMDataset`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) \| `monet_CogCoM` \| CC BY-SA 4.0 for dataset contributions; original image copyrights retained \|
	\| ReFocus (`ReFocus/ReFocus_Data`), via [Monet-SFT-125K](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K) (MIT) \| `monet_ReFocus` \| Apache-2.0 \|
	\| ChartQA (`ahmed-masry/ChartQA`; [vis-nlp/ChartQA](https://github.com/vis-nlp/ChartQA)) \| `chart` \| GPL-3.0. The ChartQA authors crawled the chart images from online sources such as Statista and Pew Research Center; image copyrights remain with the original owners \|
	\| MM-MATH (`THU-KEG/MM_Math`) \| `MM_Math` \| MIT. Problems are collected from Chinese middle-school exams \|
	\| MathVision (`MathLLMs/MathVision`) \| `MathVision` \| MIT \|
	\| GeoQA (`hz2475/geoQA`) \| `GEOQA` \| MIT \|
	\| Geometry3K (`xyliu6/geometry3k`) \| `geo_3k` \| Apache-2.0 \|

	## Intended use and limitations

	- Intended for research on multimodal reasoning and visual latent modeling. Not intended for commercial use.
	- Responses and auxiliary images are model-generated and may contain errors; the `<parser>` description states the intended auxiliary signal and is not a verified ground-truth caption.
	- The data reflects the coverage of its source pools. In particular, real-world scenes are under-represented, which matches the weaker MME-RealWorld-Lite results reported in the paper.

	## Citation

	```bibtex
	@inproceedings{miao2026gap,
	title = {Fill the {GAP}: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models},
	author = {Miao, Yanting and Sun, Yutao and Wang, Dexin and Zhou, Mengyu and Poupart, Pascal and Lv, Lei and Zhao, Qi and Wang, Li and Li, Hao and Jiang, Xiaoxi and Jiang, Guanjun},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2026}
	}
	```

	Corresponding author: Mengyu Zhou.