---
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).