Datasets:

AlibabaQwenintern
/

gap-p2-data

---
license: other
license_name: gap-p2-data-per-source
license_link: https://huggingface.co/datasets/AlibabaQwenintern/gap-p2-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 P2, an extended version of the training data for **Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models** (NeurIPS 2026), used in follow-up experiments. The release has **45,702 rows**: 41,996 in `train` and 3,706 in `validation`. Some training examples are deliberately repeated (see "Oversampling"), so the training split holds 34,408 distinct examples, and the release holds 38,114 distinct examples overall.

P2 extends the data used in the paper ([AlibabaQwenintern/gap-data](https://huggingface.co/datasets/AlibabaQwenintern/gap-data), "P1"): it adds 1,129 examples from the official We-Math 2.0 SFT set, each repeated 5 times, and repeats 3,072 existing training examples. **The paper's results use P1, not this version.**

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

## Composition

| `data_source` | Train rows | Distinct train examples | Validation rows | Rows with latent supervision (train + val) |
|---|---:|---:|---:|---:|
| `monet_Visual_CoT` | 16,465 | 16,449 | 1,812 | 13,622 |
| `chart` | 7,334 | 6,187 | 671 | 6,818 |
| `WeMath2_SFT` | 5,645 | 1,129 | 0 | 4,150 |
| `GEOQA` | 4,857 | 3,760 | 401 | 1,477 |
| `MM_Math` | 2,415 | 1,988 | 246 | 855 |
| `monet_Zebra_CoT_visual_search` | 2,240 | 2,236 | 280 | 910 |
| `monet_Zebra_CoT_count` | 920 | 832 | 91 | 149 |
| `geo_3k` | 860 | 656 | 79 | 325 |
| `MathVision` | 471 | 393 | 44 | 277 |
| `monet_CogCoM` | 404 | 401 | 43 | 378 |
| `monet_ReFocus` | 360 | 358 | 38 | 283 |
| `monet_Zebra_CoT_geometry` | 25 | 19 | 1 | 21 |
| **Total** | **41,996** | **34,408** | **3,706** | **29,265** |

### Oversampling

- The 1,129 We-Math 2.0 SFT examples each appear 5 times (`oversample_copy` = 1 to 5).
- Another 3,072 training rows, marked `p2_oversampled: true`, repeat existing examples, mostly from `chart`, `GEOQA`, `MM_Math` and `geo_3k`.
- No validation prompt appears in the training split.

## 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.
5. **Oversampling** for the P2 run, as described above.

## 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 rows (`need_latent: false`) have no image in the assistant turn.

## Files

| Path | Contents |
|---|---|
| `train.jsonl` | 41,996 training rows (256 MB) |
| `val.jsonl` | 3,706 validation rows (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` |
| `images/wemath2_sft_latent.tar` | `wemath2_sft_latent/` | `WeMath2_SFT` |

## 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-p2-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/
└── wemath2_sft_latent/
```

Most image paths in the JSONL files are absolute and start with `/root/data/`; a small number (1,343 of 74,193) 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 row.

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique row 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 row uses latent supervision |
| `accuracy` | float | Base-model accuracy over 8 samples (absent for `WeMath2_SFT`) |
| `reinfer_response` | string | Raw teacher response (absent for `WeMath2_SFT`) |
| `reinfer_model` | string | Teacher model, `"qwen3-vl-235b"` (absent for `WeMath2_SFT`) |
| `reinfer_prompt_template` | string | Prompt template used for the teacher (absent for `WeMath2_SFT`) |
| `source_dataset` | string | `WeMath2_SFT` only: `"We-Math/We-Math2.0-SFT"` |
| `gold_answer`, `gold_solution` | string | `WeMath2_SFT` only: answer and solution from the upstream dataset |
| `oversample_source_id`, `oversample_copy` | string, int | `WeMath2_SFT` only: original example ID and copy number (1 to 5) |
| `p2_oversampled` | bool | Present and `true` on the 3,072 repeated rows from other sources |

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-p2-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 (all rows except `WeMath2_SFT`) 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.

`WeMath2_SFT` rows come from We-Math's official SFT training set (`We-Math/We-Math2.0-SFT`). None of its 1,129 distinct examples duplicates any of the 1,740 We-Math testmini questions (exact text, exact image, or perceptual-hash match).

## 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` (and `source_dataset` for We-Math 2.0 SFT).
- **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).
- **`WeMath2_SFT` rows** were not re-inferred by the teacher (their `reinfer_*` fields are absent); their question text and solutions come from We-Math 2.0 SFT and keep its CC BY-NC 4.0 license.
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 |
| We-Math 2.0 SFT (`We-Math/We-Math2.0-SFT`) | `WeMath2_SFT` | CC BY-NC 4.0 |
| 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-p2-data-per-source
	license_link: https://huggingface.co/datasets/AlibabaQwenintern/gap-p2-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 P2, an extended version of the training data for Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models (NeurIPS 2026), used in follow-up experiments. The release has 45,702 rows: 41,996 in `train` and 3,706 in `validation`. Some training examples are deliberately repeated (see "Oversampling"), so the training split holds 34,408 distinct examples, and the release holds 38,114 distinct examples overall.

	P2 extends the data used in the paper ([AlibabaQwenintern/gap-data](https://huggingface.co/datasets/AlibabaQwenintern/gap-data), "P1"): it adds 1,129 examples from the official We-Math 2.0 SFT set, each repeated 5 times, and repeats 3,072 existing training examples. The paper's results use P1, not this version.

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

	## Composition

	\| `data_source` \| Train rows \| Distinct train examples \| Validation rows \| Rows with latent supervision (train + val) \|
	\|---\|---:\|---:\|---:\|---:\|
	\| `monet_Visual_CoT` \| 16,465 \| 16,449 \| 1,812 \| 13,622 \|
	\| `chart` \| 7,334 \| 6,187 \| 671 \| 6,818 \|
	\| `WeMath2_SFT` \| 5,645 \| 1,129 \| 0 \| 4,150 \|
	\| `GEOQA` \| 4,857 \| 3,760 \| 401 \| 1,477 \|
	\| `MM_Math` \| 2,415 \| 1,988 \| 246 \| 855 \|
	\| `monet_Zebra_CoT_visual_search` \| 2,240 \| 2,236 \| 280 \| 910 \|
	\| `monet_Zebra_CoT_count` \| 920 \| 832 \| 91 \| 149 \|
	\| `geo_3k` \| 860 \| 656 \| 79 \| 325 \|
	\| `MathVision` \| 471 \| 393 \| 44 \| 277 \|
	\| `monet_CogCoM` \| 404 \| 401 \| 43 \| 378 \|
	\| `monet_ReFocus` \| 360 \| 358 \| 38 \| 283 \|
	\| `monet_Zebra_CoT_geometry` \| 25 \| 19 \| 1 \| 21 \|
	\| Total \| 41,996 \| 34,408 \| 3,706 \| 29,265 \|

	### Oversampling

	- The 1,129 We-Math 2.0 SFT examples each appear 5 times (`oversample_copy` = 1 to 5).
	- Another 3,072 training rows, marked `p2_oversampled: true`, repeat existing examples, mostly from `chart`, `GEOQA`, `MM_Math` and `geo_3k`.
	- No validation prompt appears in the training split.

	## 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.
	5. Oversampling for the P2 run, as described above.

	## 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 rows (`need_latent: false`) have no image in the assistant turn.

	## Files

	\| Path \| Contents \|
	\|---\|---\|
	\| `train.jsonl` \| 41,996 training rows (256 MB) \|
	\| `val.jsonl` \| 3,706 validation rows (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` \|
	\| `images/wemath2_sft_latent.tar` \| `wemath2_sft_latent/` \| `WeMath2_SFT` \|

	## 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-p2-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/
	└── wemath2_sft_latent/
	```

	Most image paths in the JSONL files are absolute and start with `/root/data/`; a small number (1,343 of 74,193) 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 row.

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `id` \| string \| Unique row 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 row uses latent supervision \|
	\| `accuracy` \| float \| Base-model accuracy over 8 samples (absent for `WeMath2_SFT`) \|
	\| `reinfer_response` \| string \| Raw teacher response (absent for `WeMath2_SFT`) \|
	\| `reinfer_model` \| string \| Teacher model, `"qwen3-vl-235b"` (absent for `WeMath2_SFT`) \|
	\| `reinfer_prompt_template` \| string \| Prompt template used for the teacher (absent for `WeMath2_SFT`) \|
	\| `source_dataset` \| string \| `WeMath2_SFT` only: `"We-Math/We-Math2.0-SFT"` \|
	\| `gold_answer`, `gold_solution` \| string \| `WeMath2_SFT` only: answer and solution from the upstream dataset \|
	\| `oversample_source_id`, `oversample_copy` \| string, int \| `WeMath2_SFT` only: original example ID and copy number (1 to 5) \|
	\| `p2_oversampled` \| bool \| Present and `true` on the 3,072 repeated rows from other sources \|

	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-p2-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 (all rows except `WeMath2_SFT`) 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.

	`WeMath2_SFT` rows come from We-Math's official SFT training set (`We-Math/We-Math2.0-SFT`). None of its 1,129 distinct examples duplicates any of the 1,740 We-Math testmini questions (exact text, exact image, or perceptual-hash match).

	## 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` (and `source_dataset` for We-Math 2.0 SFT).
	- 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).
	- `WeMath2_SFT` rows were not re-inferred by the teacher (their `reinfer_*` fields are absent); their question text and solutions come from We-Math 2.0 SFT and keep its CC BY-NC 4.0 license.

	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 \|
	\| We-Math 2.0 SFT (`We-Math/We-Math2.0-SFT`) \| `WeMath2_SFT` \| CC BY-NC 4.0 \|
	\| 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.