Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+maze/ground_truth_criteria.jsonl filter=lfs diff=lfs merge=lfs -text
+motion_state_change/ground_truth_criteria.jsonl filter=lfs diff=lfs merge=lfs -text
+pose_adjustment/ground_truth_criteria.jsonl filter=lfs diff=lfs merge=lfs -text
+maze/instruction.jsonl filter=lfs diff=lfs merge=lfs -text
+medium_interaction/ground_truth_criteria.jsonl filter=lfs diff=lfs merge=lfs -text
+material_modification/ground_truth_criteria.jsonl filter=lfs diff=lfs merge=lfs -text
+temporal_evolution/ground_truth_criteria.jsonl filter=lfs diff=lfs merge=lfs -text
+spatial_arrangement/ground_truth_criteria.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,234 @@
+---
+license: apache-2.0
+language:
+- en
+pretty_name: VisTA-Train
+size_categories:
+- 10K<n<100K
+task_categories:
+- image-to-image
+tags:
+- visual-abstention
+- abstention
+- refusal
+- image-editing
+- unified-multimodal-models
+- multimodal-reasoning
+- chain-of-thought
+- training-data
+- maze
+configs:
+- config_name: material_modification
+  data_files:
+  - split: train
+    path: material_modification/instruction.jsonl
+- config_name: medium_interaction
+  data_files:
+  - split: train
+    path: medium_interaction/instruction.jsonl
+- config_name: motion_state_change
+  data_files:
+  - split: train
+    path: motion_state_change/instruction.jsonl
+- config_name: pose_adjustment
+  data_files:
+  - split: train
+    path: pose_adjustment/instruction.jsonl
+- config_name: spatial_arrangement
+  data_files:
+  - split: train
+    path: spatial_arrangement/instruction.jsonl
+- config_name: temporal_evolution
+  data_files:
+  - split: train
+    path: temporal_evolution/instruction.jsonl
+- config_name: maze
+  data_files:
+  - split: train
+    path: maze/instruction.jsonl
+---
+
+# VisTA-Train
+
+**VisTA-Train** is the training data of **VisTA** (**Vi**sual **T**ransformation and **A**bstention), the paired
+training method of the paper *Visual Abstention in Unified Multimodal Models*
+([arXiv:<ARXIV_ID>](https://arxiv.org/abs/<ARXIV_ID>), code: https://github.com/visual-abstention/visual-abstention).
+The evaluation benchmark is [Draw-or-Decline](https://huggingface.co/datasets/visual-abstention/Draw-or-Decline).
+
+Every example is a request (input image + instruction) with a target response. The data comes in feasible–infeasible
+pairs, so a model learns to judge feasibility before it decides whether to draw:
+
+* **feasible**: a reasoning block `<think>...</think>` that plans the edit, followed by the target image;
+* **infeasible**: a reasoning block that explains which requirement cannot be met and ends with the plain-text marker
+  `[ABSTAIN]` (`<think>... [ABSTAIN]</think>`), with no target image.
+
+## Statistics
+
+38,224 pairs, 76,448 examples, one split (`train`). Every pair that passed the checks is kept, so the categories are not
+balanced.
+
+| Category | Pairs | Examples | Pairing | Source |
+|---|---:|---:|---|---|
+| material modification | 5,958 | 11,916 | same image, different instruction | UniREdit-Data-100K |
+| medium interaction | 5,573 | 11,146 | same image, different instruction | UniREdit-Data-100K |
+| motion state change | 5,512 | 11,024 | same image, different instruction | UniREdit-Data-100K |
+| pose adjustment | 5,695 | 11,390 | same image, different instruction | UniREdit-Data-100K |
+| spatial arrangement | 5,837 | 11,674 | same image, different instruction | UniREdit-Data-100K |
+| temporal evolution | 4,449 | 8,898 | same image, different instruction | UniREdit-Data-100K |
+| maze | 5,200 | 10,400 | same instruction, different map | generated by the authors |
+| **Total** | **38,224** | **76,448** | | |
+
+Maze grids are 7×7 (446 pairs), 9×9 (2,503) or 11×11 (2,251), counting the outer wall. There are 114,672 PNG images.
+
+## Construction (summary)
+
+* **Real-world categories (33,024 pairs).** Each feasible example keeps its
+  [UniREdit-Data-100K](https://huggingface.co/datasets/maplebb/UniREdit-Data-100K) instruction, chain of thought, input
+  image and target image unchanged. For each source example, Gemini3.8 Flash and Kimi-K3 each proposed one infeasible
+  instruction on the same image; a candidate was retained only if both models confirmed infeasibility (a second round
+  covered the 0.5% of sources without a retained candidate), and one was chosen at random. Kimi-K3 wrote the reasoning
+  of the infeasible example, and both models reviewed it against the image, instruction and constraints, with
+  feedback for revision.
+* **Maze (5,200 pairs).** New map pairs generated as for DoD: both maps share the instruction and endpoints and differ in
+  one corridor cell; BFS verifies that the feasible map has a path and the infeasible one has none. Kimi-K3 wrote and
+  reviewed the reasoning for both maps.
+* **Quality and separation from DoD.** The data was not verified in full by humans. A human audit of 300 random pairs
+  found 2 unusable pairs (99.3% acceptance); they were kept unchanged, so the released data is what the paper's model
+  was trained on. Every training input and target image was compared pixel-wise with all DoD images, and training mazes
+  whose wall layout matches a DoD maze under rotation or reflection were excluded. These checks detect exact overlap,
+  not semantically similar scenes.
+
+See Sec. 5.2 and Appx. C of the paper for details.
+
+## Files
+
+```
+<category>/instruction.jsonl            requests, one folder per category (7 folders)
+<category>/ground_truth_criteria.jsonl  labels and target responses
+images/*.tar                            uncompressed tar shards with all images
+images/manifest.json                    description of the shards
+```
+
+Extract all shards into **one** directory (the dataset root) to recreate `image/...`; the image paths in the JSONL files
+(`image/<name>.png`) are relative to that directory. Both examples of a real-world pair have their own input file
+(`<category>-<id>-feasible.png`, `<category>-<id>-infeasible.png`) holding the same image; the target is
+`<category>-<id>-target.png`. Maze pairs use `matched-maze-<nnnnn>-A.png` (feasible), `-B.png` (infeasible) and
+`-target.png` (solved feasible map).
+
+## Schema
+
+### `instruction.jsonl`
+
+| Field | Description |
+|---|---|
+| `case_id` | `<pair_id>-feasible` or `<pair_id>-infeasible` |
+| `task` | always `training_editing` |
+| `input_mode` | always `image_editing` |
+| `input_image` | path of the input image |
+| `input_images` | maze only: list with the same single path |
+| `prompt_en` | the instruction given to the model (without the reminder). For maze: `rules + "\n\n" + instruction_en` |
+| `instruction_en` | the instruction alone; present for maze and for real-world infeasible examples (for real-world feasible examples, `prompt_en` is the official instruction) |
+| `rules` | task rules; empty for real-world examples |
+| `subset` | the category |
+| `split` | always `train` |
+
+### `ground_truth_criteria.jsonl`
+
+| Field | Description |
+|---|---|
+| `schema` | `training_pair_v1` |
+| `case_id`, `pair_id`, `paired_case_id` | identifiers; the two examples of a pair point to each other |
+| `split` | always `train` |
+| `ground_truth.feasible` | the feasibility label |
+| `ground_truth.reason` | short explanation of the label; present for real-world infeasible examples and for all maze examples |
+| `expected_response` | `generate_image` (feasible) or `cot_abstain_then_eos` (infeasible) |
+| `training_response` | the target text: `<think>...</think>`; infeasible responses end with `[ABSTAIN]</think>` |
+| `training_output_image` | path of the target image (feasible), `null` (infeasible) |
+| `reference_image` | same as `training_output_image` |
+| `source` | provenance. Real-world: `dataset` (`maplebb/UniREdit-Data-100K`), `revision` (dataset commit), `category`, `id`, `original_image_path`, `edited_image_path` in that dataset, and `synthetic_input` (`false`) on infeasible examples. Maze: `dataset`, `category`, `category_en`, `id`, `origin`, `official_image` (`false`), `seed`, `instruction_origin`, `matched_pair`, `difference` |
+| `program_verification` | maze only: BFS verification record (0-based `[row, column]` including the outer wall): start, end, the changed cell, the unique feasible path, the reachable sets of both endpoints on the infeasible map, distances of the cut from both endpoints, and pixel-difference checks |
+
+## Usage
+
+```python
+import json, tarfile
+from pathlib import Path
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+root = Path(snapshot_download("visual-abstention/VisTA-Train", repo_type="dataset"))
+for shard in sorted((root / "images").glob("*.tar")):      # recreates root/image/...
+    with tarfile.open(shard) as tar:
+        tar.extractall(root, filter="data")
+
+category = "maze"
+requests = [json.loads(l) for l in open(root / category / "instruction.jsonl")]
+targets = {r["case_id"]: r for r in map(json.loads, open(root / category / "ground_truth_criteria.jsonl"))}
+
+req = requests[0]
+tgt = targets[req["case_id"]]
+image = Image.open(root / req["input_image"])
+output = tgt["training_output_image"] and Image.open(root / tgt["training_output_image"])
+print(req["prompt_en"], tgt["training_response"])
+```
+
+## Training recipe used in the paper
+
+VisTA-BAGEL starts from the official UniREdit-BAGEL weights and is trained on all 76,448 examples for 16,384 steps
+(learning rate 2e-6, 8 examples per step: 4 feasible and 4 infeasible, the two examples of a pair in the same step).
+The text loss covers the reasoning, including `[ABSTAIN]` and the end-of-sequence token for infeasible examples; the
+image loss covers the target images of feasible examples only. In every step, half of the instructions get the
+reminder "If no solution exists under these constraints, state that no solution exists and briefly explain why."
+appended at training time; the reminder is not stored in `prompt_en`. The full recipe and the training code are in the
+paper (Appx. C) and the [code repository](https://github.com/visual-abstention/visual-abstention). Model weights are not
+part of this release.
+
+## Evaluation
+
+This dataset is for training only. Evaluate on
+[Draw-or-Decline](https://huggingface.co/datasets/visual-abstention/Draw-or-Decline), whose images and mazes do not
+overlap exactly with this data (see above).
+
+**Note on translated fields.** `ground_truth.reason` for maze examples (and a few real-world ones), and 4 reasoning
+passages of feasible examples, were written partly in Chinese and machine-translated to English by Kimi-K3 for this
+release. All other text, including almost all `training_response` fields, is unchanged. The paper's model was trained
+on the original `training_response` texts; `ground_truth.reason` is not part of the training target.
+
+## License and attribution
+
+The new annotations (infeasible instructions, infeasible reasoning, maze maps, maze reasoning, explanations and
+verification records) are released under the **Apache License 2.0**.
+
+The real-world input images, target images, feasible instructions and feasible chains of thought come from
+**UniREdit-Data-100K** ([maplebb/UniREdit-Data-100K](https://huggingface.co/datasets/maplebb/UniREdit-Data-100K),
+Apache-2.0) and are unchanged, except that Chinese passages in 4 feasible chains of thought were translated to English;
+`source.revision` records the dataset commit. Please cite it as well. The maze images are generated by the
+authors.
+
+## Citation
+
+```bibtex
+@article{shi2026visual,
+  title   = {Visual Abstention in Unified Multimodal Models},
+  author  = {Shi, Chufan and Yang, Cheng and Yang, Tiannuo and White, Isadora and Chen, Yiwei and Berg-Kirkpatrick, Taylor and Ma, Xuezhe},
+  journal = {arXiv preprint arXiv:<ARXIV_ID>},
+  year    = {2026}
+}
+
+@inproceedings{han2026unireditbench,
+  title     = {UniREditBench: A Unified Reasoning-based Image Editing Benchmark},
+  author    = {Han, Feng and Wang, Yibin and Li, Chenglin and Liang, Zheming and Wang, Dianyi and Jiao, Yang and Wei, Zhipeng and Gong, Chao and Jin, Cheng and Wang, Jiaqi},
+  booktitle = {European Conference on Computer Vision},
+  pages     = {287--304},
+  year      = {2026}
+}
+```
+
+## Limitations
+
+* Infeasible instructions and their reasoning were written by models and checked by models; only a 300-pair sample was
+  audited by humans, and the 2 pairs it flagged remain in the data.
+* Feasible real-world examples inherit the instructions, reasoning and target images of UniREdit-Data-100K as they are.
+* The categories are unbalanced, and the data covers single-step image editing in 7 categories only.
+* The separation from DoD rules out exact image and maze overlap, not semantically similar scenes.
+* Some explanation fields are machine translations (see above).

images/manifest.json

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+  "member_prefix": "image/",
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+      "temporal_evolution": 4
+    }
+  }
+}

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