Release the Draw-or-Decline (DoD) benchmark

README.md

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+---
+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
+---
+
+# Draw-or-Decline (DoD)
+
+**Draw-or-Decline (DoD)** is the benchmark 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).
+
+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.
+
+## 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.
+
+## Construction (summary)
+
+* **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.
+
+## Files
+
+```
+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.
+
+## Schema
+
+### `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`.
+
+## Usage
+
+```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.
+
+## 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.
+
+## License and attribution
+
+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.
+
+## 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
+
+* 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).