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metadata 
configs:
  - config_name: default
    data_files:
      - split: test
        path: metadata/eval_metadata.jsonl
task_categories:
  - text-to-image
license: apache-2.0

Search Beyond What Can Be Taught

Evolving the Knowledge Boundary in Agentic Visual Generation

Haozhe Wang1 · Weijia Feng3 · Jinpeng Yu3 · Che Liu4 · Ping Nie2 · Fangzhen Lin1 · Jiaming Liu3✉ · Ruihua Huang3 · Jimmy Lin2 · Wenhu Chen2 · Cong Wei2✉

1 HKUST · 2 University of Waterloo · 3 Qwen Applications · 4 Imperial College London

✉ Corresponding authors: Jiaming Liu, Cong Wei

📄 arXiv · 🌐 Project Page · 💻 GitHub

🤗 SearchGen-20K · 🤗 SearchGen-Corpus-1M · 🤗 AgentGen-Bench

Image generators fabricate what they don't know. This one looks it up first—and knows when not to.

AgentGen-Bench

This dataset contains the standalone AgentGen benchmark prompts and reference metadata.

Dataset Statistics

Statistic Count
Benchmark prompts 751
Visual reference images 1,099

Official Evaluation Subsets

AgentGen-Bench includes three official compact-evaluation labels: testmini, testmini_easy, and testmini_hard.

Subset label Rows Intended track
testmini 200 Compact evaluation
testmini_easy 100 Compact easy track
testmini_hard 100 Compact hard track

Each row stores its memberships in the subset array. The labels are independent membership tags, so do not infer one label from another. Rows that do not belong to these subsets have "subset": [].

The published Test-mini result uses exactly the 200 rows carrying the testmini label. It is a fixed benchmark slice, not a fresh or random sample from the 751-row Full benchmark.

Example with Hugging Face Datasets:

from datasets import load_dataset

benchmark = load_dataset("JasperHaozhe/AgentGen-Bench", split="test")
testmini = benchmark.filter(lambda row: "testmini" in row["subset"])
testmini_easy = benchmark.filter(lambda row: "testmini_easy" in row["subset"])
testmini_hard = benchmark.filter(lambda row: "testmini_hard" in row["subset"])

assert len(testmini) == 200

Benchmark Results

Scores are on a 0–100 scale. Each table reports Overall9 for the same 18 generators. Coverage is scored prompts / slice prompts, with missing evaluator scores excluded from aggregation.

Full benchmark (751 prompts)

Rank Model Coverage Overall9
1 GPT-Image-2 718/751 76.0
2 Grok-Imagine-2.0-Low 715/751 75.2
3 Qwen-Image-3-Max 738/751 68.4
4 Grok-Imagine 717/751 67.8
5 Nano Banana Pro 738/751 65.0
6 Qwen-Image-2-Pro 742/751 59.4
7 Qwen-Image-2 741/751 56.4
8 SeedDream-4.5 746/751 56.2
9 SeedDream-4.0 750/751 52.5
10 Nano-Banana 740/751 49.7
11 SenseNova-U1 750/751 35.7
12 Qwen-Image 751/751 34.1
13 Mage-Flow 751/751 32.8
14 Flux.2-Klein-9B 750/751 31.1
15 Flux.2-Klein-4B 751/751 27.7
16 Bagel 751/751 25.8
17 OmniGen2 750/751 24.0
18 Show-o2 751/751 19.8

Test-mini (200 prompts)

Rank Model Coverage Overall9
1 GPT-Image-2 200/200 76.3
2 Grok-Imagine-2.0-Low 196/200 73.9
3 Qwen-Image-3-Max 195/200 68.2
4 Grok-Imagine 196/200 67.7
5 Nano Banana Pro 200/200 64.7
6 Qwen-Image-2-Pro 197/200 60.3
7 Qwen-Image-2 197/200 56.9
8 SeedDream-4.5 199/200 56.6
9 SeedDream-4.0 200/200 51.8
10 Nano-Banana 200/200 48.4
11 SenseNova-U1 200/200 35.4
12 Qwen-Image 200/200 32.7
13 Mage-Flow 200/200 31.4
14 Flux.2-Klein-9B 200/200 30.5
15 Flux.2-Klein-4B 200/200 27.1
16 Bagel 200/200 24.8
17 OmniGen2 199/200 22.2
18 Show-o2 200/200 19.1
Mini-easy and Mini-hard leaderboards (100 prompts each)

testmini_easy and testmini_hard are independent official label slices; they are not necessarily a partition of the 200-prompt testmini slice.

Mini-easy

Rank Model Coverage Overall9
1 GPT-Image-2 100/100 79.7
2 Grok-Imagine-2.0-Low 98/100 79.5
3 Qwen-Image-3-Max 99/100 74.9
4 Grok-Imagine 98/100 73.4
5 Qwen-Image-2-Pro 99/100 69.3
6 Nano Banana Pro 100/100 66.5
7 SeedDream-4.0 100/100 64.3
8 SeedDream-4.5 99/100 63.9
9 Qwen-Image-2 99/100 63.5
10 Nano-Banana 100/100 54.4
11 SenseNova-U1 100/100 40.6
12 Qwen-Image 100/100 38.7
13 Mage-Flow 100/100 37.7
14 Flux.2-Klein-9B 100/100 34.8
15 Flux.2-Klein-4B 100/100 32.6
16 Bagel 100/100 29.3
17 OmniGen2 100/100 26.5
18 Show-o2 100/100 20.2

Mini-hard

Rank Model Coverage Overall9
1 Grok-Imagine-2.0-Low 96/100 71.9
2 GPT-Image-2 100/100 71.3
3 Qwen-Image-3-Max 99/100 64.3
4 Grok-Imagine 94/100 61.8
5 Nano Banana Pro 100/100 60.8
6 Qwen-Image-2-Pro 100/100 50.6
7 Qwen-Image-2 100/100 50.0
8 SeedDream-4.5 100/100 49.0
9 Nano-Banana 100/100 44.4
10 SeedDream-4.0 100/100 38.9
11 SenseNova-U1 100/100 29.7
12 Qwen-Image 100/100 29.5
13 Mage-Flow 100/100 28.3
14 Flux.2-Klein-9B 100/100 27.3
15 Bagel 100/100 23.0
16 Flux.2-Klein-4B 100/100 21.9
17 OmniGen2 100/100 21.9
18 Show-o2 100/100 15.8

Purpose

Use this section to evaluate image generation systems on prompts with explicit visual references, text knowledge slots, verification checklists, and rubrics. It is separate from the 20K training metadata and is intended for evaluation.

Main Files

  • eval_metadata.jsonl: benchmark prompt metadata.
  • benchmark_manifest.json: benchmark counts, schema fields, and reference-image policy.
  • eval_query_coverage.jsonl: coverage map from benchmark reference slots to searchgen-corpus-1m queries.

Schema

Each eval_metadata.jsonl row contains:

  • bench_id
  • subset
  • user_prompt
  • language
  • generation_task_type
  • failure_modes
  • domains
  • verification_checklist
  • evaluation_rubric
  • text_knowledge_slots
  • visual_reference_slots

visual_reference_slots[].relative_path points to the benchmark-local reference image path. It is relative to data_release/searchgen-bench/. After repairing source eval-dataset reference paths, all 1,099 benchmark reference-image targets are materialized. The benchmark-only copy audit is ../../validation_logs/payload_copy_benchmark_reference_after_fix.tsv; the current missing-image worklist ../../validation_logs/missing_benchmark_reference_images.tsv is empty.

Expected final image layout:

data_release/searchgen-bench/reference_images/{bench_id}/ref_00.jpg

Cross-References

eval_query_coverage.jsonl links benchmark reference slots to ../searchgen-corpus-1m/database/search.sqlite through query_id when a matching search corpus query exists.

The training dataset is separate and documented in ../searchgen-20k/README.md.

Minimal load check:

python3 - <<'PY'
import json
rows=[json.loads(x) for x in open('data_release/searchgen-bench/eval_metadata.jsonl')]
print(len(rows), rows[0]['bench_id'])
PY

Rights and takedowns

Rights questions and takedown requests: jasper.whz@outlook.com.

Citation

If you find this dataset useful, please cite:

@article{wang2026searchgen,
  title   = {Search Beyond What Can Be Taught: Evolving the Knowledge
             Boundary in Agentic Visual Generation},
  author  = {Wang, Haozhe and Feng, Weijia and Yu, Jinpeng and Liu, Che and
             Nie, Ping and Lin, Fangzhen and Liu, Jiaming and Huang, Ruihua and
             Lin, Jimmy and Chen, Wenhu and Wei, Cong},
  journal = {arXiv preprint arXiv:2607.05382},
  year    = {2026},
  url     = {https://arxiv.org/abs/2607.05382}
}