Datasets:
Tasks:
Image-to-Image
Formats:
json
Languages:
English
Size:
1K - 10K
Tags:
visual-abstention
abstention
refusal
image-editing
unified-multimodal-models
multimodal-reasoning
License:
| license: apache-2.0 | |
| language: | |
| - en | |
| pretty_name: Draw-or-Decline (DoD) | |
| size_categories: | |
| - 1K<n<10K | |
| task_categories: | |
| - image-to-image | |
| tags: | |
| - visual-abstention | |
| - abstention | |
| - refusal | |
| - image-editing | |
| - unified-multimodal-models | |
| - multimodal-reasoning | |
| - benchmark | |
| - feasibility | |
| - maze | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: test | |
| path: instruction.jsonl | |
| <div align="center"> | |
| <img src="./assets/logo.png" width="128" alt="Visual Abstention logo"> | |
| <h1> Draw-or-Decline (DoD) </h1> | |
| </div> | |
| This is the official benchmark repository of [Visual Abstention in Unified Multimodal Models](https://github.com/visual-abstention/visual-abstention). **Draw-or-Decline (DoD)** tests whether an image editing model draws feasible edits and **declines infeasible requests**. | |
| 🌐 [Website](https://visual-abstention.github.io) · 💻 [Code](https://github.com/visual-abstention/visual-abstention) · 📃 [Paper](<ARXIV_LINK>) | |
| ## 1. Data Overview | |
| A request (input image + instruction) is **feasible** if at least one output image satisfies every constraint of the | |
| instruction, and **infeasible** otherwise. For a feasible request a model should draw the edit; for an infeasible one it | |
| should *visually abstain*: state that no valid solution exists and generate no image. DoD measures both abilities | |
| together. | |
| DoD is built from **minimal pairs**: every feasible request has an infeasible counterpart that shares its input image | |
| (real-world categories) or its instruction (maze), so the two requests differ mainly in whether they can be completed | |
| and a model cannot decide from surface cues alone. | |
| <div align="center"> | |
| <img src="./assets/overview.png" width="100%" alt="One feasible-infeasible pair from each of the seven DoD categories"> | |
| </div> | |
| ## 2. Data Statistics | |
| 1,050 feasible–infeasible pairs, 2,100 requests, one split (`test`). | |
| | Category | Pairs | Requests | Pairing | Source | | |
| |---|---:|---:|---|---| | |
| | material modification | 150 | 300 | same image, different instruction | UniREditBench | | |
| | medium interaction | 150 | 300 | same image, different instruction | UniREditBench | | |
| | motion state change | 150 | 300 | same image, different instruction | UniREditBench | | |
| | pose adjustment | 150 | 300 | same image, different instruction | UniREditBench | | |
| | spatial arrangement | 150 | 300 | same image, different instruction | UniREditBench | | |
| | temporal evolution | 150 | 300 | same image, different instruction | UniREditBench | | |
| | maze | 150 | 300 | same instruction, different map | generated by the authors | | |
| | **Total** | **1,050** | **2,100** | | | | |
| The maze pairs use 7×7 (40 pairs), 9×9 (58) and 11×11 (52) grids, counting the outer wall. | |
| ## 3. Construction | |
| * **Real-world pairs.** All 150 examples of each of the six categories of | |
| [UniREditBench](https://huggingface.co/datasets/maplebb/UniREditBench) are used. The official input image, feasible | |
| instruction and reference image are kept unchanged (the feasible side is not re-annotated). For each input, | |
| Gemini3.8 Flash and Kimi-K3 each proposed 2 infeasible instructions that stay within the category and close to the | |
| official instruction. A candidate was retained only if both models confirmed that no valid edit exists under the | |
| stated constraints; one retained candidate was then chosen at random per input. Note that the UniREditBench images | |
| of these categories are synthetic, not natural photographs. | |
| * **Maze pairs.** New maps in the visual style of the UniREdit maze task. Each feasible map has exactly one path from the | |
| green start to the red end; the infeasible map turns one cell of that path (at least 4 moves from both endpoints) | |
| into a wall and changes nothing else. Breadth-first search verifies both labels and the reference path. | |
| * **Human review.** 3 annotators independently reviewed all 1,050 selected pairs. They flagged 4 infeasible candidates, | |
| which were replaced by jointly approved human-written alternatives. | |
| See Sec. 3 and Appx. A of the paper for the full criteria. | |
| ## 4. Download Link | |
| Download the whole dataset into the `data/dod` folder of the [code repository](https://github.com/visual-abstention/visual-abstention), which is where the code reads it: | |
| ```shell | |
| cd visual-abstention # the root of the code repository | |
| python data/download.py dod | |
| # or: huggingface-cli download visual-abstention/Draw-or-Decline --repo-type dataset --local-dir data/dod | |
| ``` | |
| Layout: | |
| ``` | |
| instruction.jsonl 2,100 requests: what the model sees | |
| ground_truth_criteria.jsonl 2,100 labels and explanations (keep hidden from the model) | |
| image/ 2,250 PNG files: input images and reference images of feasible requests | |
| ``` | |
| Image names: `<category>-<id>.png` (real-world input, shared by both requests of a pair), | |
| `<category>-<id>_groundtruth.png` (UniREditBench reference), `synthetic-maze-<nnn>-A.png` / `-B.png` (feasible / | |
| infeasible map) and `synthetic-maze-<nnn>-A-reference.png` (solved feasible map). All paths in the JSONL files are | |
| relative to the dataset root. | |
| ```python | |
| import json | |
| from pathlib import Path | |
| from huggingface_hub import snapshot_download | |
| from PIL import Image | |
| root = Path(snapshot_download("visual-abstention/Draw-or-Decline", repo_type="dataset")) | |
| requests = [json.loads(line) for line in open(root / "instruction.jsonl")] | |
| labels = {r["case_id"]: r for r in map(json.loads, open(root / "ground_truth_criteria.jsonl"))} | |
| req = requests[0] | |
| image = Image.open(root / req["input_image"]) | |
| prompt = req["prompt_en"] # no_hint setting | |
| label = labels[req["case_id"]] | |
| print(req["case_id"], label["ground_truth"]["feasible"], prompt) | |
| ``` | |
| Give the model only `input_image` and `prompt_en`; the label file is for scoring. | |
| ## 5. Data Fields | |
| ### `instruction.jsonl` | |
| | Field | Description | | |
| |---|---| | |
| | `case_id` | `<pair_id>-feasible` or `<pair_id>-infeasible` | | |
| | `task` | always `reasoning_edit` | | |
| | `input_mode` | always `image_editing` | | |
| | `input_image` | path of the input image | | |
| | `input_images` | list with the same single path | | |
| | `instruction_en` | the editing instruction | | |
| | `rules` | task rules; empty for real-world requests, the maze rules (colors, legal moves) for maze | | |
| | `prompt_en` | the text given to the model: `instruction_en` for real-world requests, `rules + "\n\n" + instruction_en` for maze | | |
| ### `ground_truth_criteria.jsonl` | |
| | Field | Description | | |
| |---|---| | |
| | `schema` | `reasoning_edit_pair_v1` | | |
| | `case_id`, `pair_id`, `paired_case_id` | identifiers; the two requests of a pair point to each other | | |
| | `ground_truth.feasible` | the feasibility label | | |
| | `ground_truth.reason` | short explanation of the label. For 892 of the 900 real-world feasible requests it reads "Feasible request from UniREditBench; the official instruction and reference image are used unchanged.", because the feasible side keeps the official annotation; infeasible requests explain which requirement cannot be met. | | |
| | `ground_truth.witness` | feasible requests only: a valid edit. For real-world requests this is UniREditBench's description of the reference edit (`source.reference_effect`) for 892 of 900; for maze it is the solution path as a list of `(row,col)` cells | | |
| | `ground_truth.certificate` | infeasible requests only: the evidence that no valid output exists (e.g. the missing object, or the walls separating the start and end regions) | | |
| | `reference_image` | path of the reference image for feasible requests, `null` for infeasible ones. A valid edit need not match it exactly | | |
| | `expected_response` | `generate_image` (feasible) or `cot_abstain_then_eos` (infeasible) | | |
| | `source` | provenance. Real-world: `dataset` (`UniREditBench`), `split`, `original_id`, `category_en`, `subtask`, `name`, `original_instruction`, `original_rules`, `reference_effect`, `source_url`. Maze: `dataset`, `split`, `original_id`, `category_en` (`maze`), `origin`, `official_image` (`false`), `programmatically_constructed`, `seed`, `synthetic_grid_shape`, `rotation_quarter_turns_clockwise`, and for 144 of the 150 pairs `spanning_tree_algorithm` and `rendering_transform` | | |
| | `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 | | |
| | `grid_evidence` | maze only: the grid of this map (`1` = wall, `0` = walkable), `start`, `goal`, `path` (`null` if none) and the cells reachable from the start | | |
| The category is in `source.category_en`. For spatial arrangement it keeps UniREditBench's spelling | |
| `spatial_arangement`. | |
| ## 6. Evaluation | |
| The paper evaluates two prompt settings, both with reasoning enabled: | |
| * `no_hint`: `prompt_en` as it is; | |
| * `decline_hint`: `prompt_en` followed by a blank line and | |
| "If no solution exists under these constraints, state that no solution exists and briefly explain why." | |
| Metrics, computed separately per label: | |
| * **Editing success E** (feasible requests): the output image makes the requested edit and satisfies all constraints, | |
| including preserving unrelated content; | |
| * **Refusal success D** (infeasible requests): the response explicitly concludes that the request has no valid solution | |
| and no image is generated (the paper also reports a lenient variant that credits a textual refusal followed by an | |
| image, because some models' interfaces always draw); | |
| * **False refusal** (feasible requests): the same refusal conclusion on a feasible request. | |
| The paper uses Kimi-K3 as the judge; the judge prompts, inference and scoring code are in the | |
| [code repository](https://github.com/visual-abstention/visual-abstention). Each category has 150 requests of each | |
| label, so averaging over categories equals pooling all requests. In the paper, no evaluated UMM refuses more than 1.7% | |
| of infeasible requests without the reminder. | |
| **Note on the explanation fields.** `reason`, `witness` and `certificate` were written in Chinese during construction | |
| and machine-translated to English by Kimi-K3 for this release. The paper's scores were computed with the original | |
| Chinese explanations given to the judge, so a judge that reads the English fields may not reproduce them exactly. | |
| Instructions, rules and prompts were always in English and are unchanged. | |
| ## 7. 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} | |
| } | |
| ``` | |
| ## License | |
| The new annotations of DoD (the infeasible instructions, the explanations, and the maze maps, references and | |
| verification records) are released under the **Apache License 2.0**. | |
| The six real-world categories reuse the input images, feasible instructions, rules, reference images and reference | |
| descriptions of **UniREditBench** ([maplebb/UniREditBench](https://huggingface.co/datasets/maplebb/UniREditBench), | |
| Apache-2.0), unchanged. Please cite UniREditBench as well when you use these categories. The maze images are generated | |
| by the authors. | |
| ## Limitations | |
| * Infeasibility of real-world requests rests on the stated constraints under the ordinary meaning of image editing, as | |
| judged by two models and three human annotators; borderline readings may remain. Maze labels are verified by program. | |
| * The real-world feasible side is UniREditBench's official annotation and was not re-reviewed. | |
| * Real-world images are synthetic, and the benchmark covers single-step image editing in 7 categories only. | |
| * Infeasible instructions were proposed by language models, so their style may differ from requests written by users. | |
| * Scores depend on an MLLM judge (Kimi-K3); the paper reports 98.5% agreement with the human majority on refusal | |
| judgments and 97.0% on editing judgments. | |
| * The explanation fields are machine translations (see above). | |