Datasets:

xiaomoguhzz
/

R3_backup

---
license: apache-2.0
task_categories:
  - image-to-text
  - visual-question-answering
language:
  - en
size_categories:
  - n<1K
tags:
  - benchmark
  - reflection
  - rectification
  - text-to-image
  - visual-reasoning
  - image-editing
pretty_name: R3-Bench
---

# R3-Bench: Reason-Reflect-Rectify Benchmark

**Code**: [github.com/xiaomoguhz/R3-Bench](https://github.com/xiaomoguhz/R3-Bench)
**Paper**: *Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation* — accepted to ICML 2026.

R³-Bench evaluates **reflective visual generation**: given a generated image and the original prompt, a model must (i) reason about whether the image matches the prompt, (ii) explain any discrepancy, and (iii) propose a rectification action. The benchmark measures two complementary scores:

- **S_ref** — Reflective Verdict Score (verdict + explanation correctness, judged by an LLM)
- **S_rect** — Rectification Score (normalised VQA-alignment gain after applying the proposed edit)

## Contents

| File | Description |
|---|---|
| `images.tar.gz` | 670 source images (PNG, ~717 MB), organised as `images/{category}/{verdict}/r3b_{idx:06d}.png` |

The 670-sample **prompt + ground-truth JSONL** ships with the code repository at [`r3bench/data/r3bench.jsonl`](https://github.com/xiaomoguhz/R3-Bench/blob/main/r3bench/data/r3bench.jsonl) — labels travel with the eval code so version drift is captured by git.

## Schema

Each JSONL record:

```json
{
  "idx": 0,
  "original_prompt": "a black candle and a white holder",
  "bad_image": "images/color/false/r3b_000000.png",
  "answer": false,
  "explanation": "The white object is a candle, not a holder as specified in the prompt.",
  "category": "color",
  "generated_qa": {
    "yn_question_list": ["Is there a candle in the image?", "..."]
  }
}
```

| Field | Type | Description |
|---|---|---|
| `idx` | int | Unique sample id (0–669) |
| `original_prompt` | str | The text-to-image prompt |
| `bad_image` | str | Image path relative to the data root |
| `answer` | bool | Ground-truth verdict: does the image match the prompt? (`false` = mismatch) |
| `explanation` | str | Ground-truth discrepancy description (used as S_ref reference) |
| `category` | str | Error dimension: color · object · numeracy · spatial · shape · texture · complex · non |
| `generated_qa.yn_question_list` | list[str] | Yes/no VQA probes used by the S_rect rectification scorer |

## Category & verdict distribution (670 total)

| Category | false (mismatch) | true (match) | Total |
|---|---:|---:|---:|
| color    | 71 | 26 | 97 |
| complex  | 46 | 48 | 94 |
| non      | 7  | 42 | 49 |
| numeracy | 73 | 23 | 96 |
| object   | 54 | 17 | 71 |
| shape    | 72 | 20 | 92 |
| spatial  | 76 | 25 | 101 |
| texture  | 49 | 21 | 70 |
| **Sum**  | **448** | **222** | **670** |

`non` = "no error" probe samples (image matches prompt) used to calibrate false-positive reflection.

## Usage

```bash
# 1. Download images
hf download xiaomoguhzz/R3-Bench-data images.tar.gz \
    --repo-type dataset --local-dir /path/to/r3bench-data
cd /path/to/r3bench-data && tar -xzf images.tar.gz
# → /path/to/r3bench-data/images/{category}/{true,false}/r3b_{idx:06d}.png

# 2. Run the 4-step pipeline (clone the code repo first)
git clone https://github.com/xiaomoguhz/R3-Bench && cd R3-Bench
export R3BENCH_DATA_DIR=/path/to/r3bench-data

bash scripts/run_reflection.sh 8 qwen2.5vl    # Step 1 — reflection
bash scripts/run_edit.sh       8 qwen2.5vl qwen_image_2511  # Step 2 — editing
bash scripts/eval_reflection.sh qwen2.5vl       # Step 3 — S_ref
bash scripts/eval_edit.sh      qwen2.5vl qwen_image_2511    # Step 4 — S_rect
```

See the [code repo README](https://github.com/xiaomoguhz/R3-Bench) for full pipeline details, supported backends, and how to plug in a custom reflection / editor model.

## Citation

```bibtex
@inproceedings{r3bench2026,
  title={Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation},
  booktitle={Proceedings of the International Conference on Machine Learning (ICML)},
  year={2026}
}
```

## License

Released under the **Apache 2.0** license. Source prompts adapted from T2I-CompBench, GenEval++, and GEdit-Bench under their respective licenses; see the code repository for full attribution.