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license: cc-by-nc-4.0
language:
- en
task_categories:
- text-generation
tags:
- video-editing
- planning
- verifier
- benchmark
pretty_name: RefineCut-Bench
size_categories:
- 1K<n<10K
---
# RefineCut-Bench
A planning-level benchmark for **executable video-editing planning** (EMNLP 2026, *Plans You Can Check:
Verifier-Grounded Learning of an Open-Weight Planner for Executable Video-Editing*). A task gives a planner a
brief, a real clip pool with schema-constrained captions and metadata, optional music metadata with beat
tracks, the current timeline state, and an explicit **constraint ledger**; the planner emits a `RefinePatch`
(RFC 6902-style JSON Patch over a typed timeline) and a deterministic verifier applies it and recomputes every
ledger entry. Paper: **https://arxiv.org/abs/2608.25622** (EMNLP 2026 Main). Evaluation code and prompt: **https://github.com/Lancelot-wy/RefineCut**.
| | Count |
|---|---|
| Canonical tasks (raw task records) | 3,578 (3,960) |
| Task families / subtypes | 3 (A composition 1,460 · B local edit 1,219 · C generative 899) / 15 |
| Constraint types | 14 |
| Captioned clips | 7,971 |
| Music tracks (features only) | 499 |
| Train / dev / test (task-id disjoint) | 2,773 / 596 / 591 |
| Evaluation sets | Common-100, dev100, canonical-clean (92) |
| Teacher trajectories, replayed | GPT-5.4 2,000 · Qwen3-Max 1,946 · DeepSeek-V4-Pro 1,959 |
## Layout
```
tasks/ all_tasks.jsonl, train.jsonl, dev.jsonl, test.jsonl, split_manifest.json, split_audit.json
eval/ common100_items.jsonl evaluation-ready Common-100 items (ledger, clip-pool metadata, failed
initial state, violated constraints) consumed by the evaluation harness
dev100_items.jsonl checkpoint-selection set
test_items.jsonl all 591 test items in the same format
canonical_clean_ids.json the 92 Common-100 tasks whose canonical id never appears in training
clips/ captions.jsonl (7,971 clips: subject / action / scene / camera / scene_category / motion_intensity /
caption_short / duration / source), clip_alias_maps/, clips_meta/<source>/ (public source identifiers)
music/ music_features.json (BPM, beat times, energy, duration for 499 tracks; no audio)
trajectories/ normalized/<teacher>.jsonl canonicalized multi-teacher trajectories (up to 3 steps x 4 branches)
replayed/<teacher>_replay.jsonl the same trajectories with per-branch verifier replay scores
schemas/ constraint_ledger, editplan, refinepatch, timeline_ir, verifier_output JSON schemas
docs/ metric definitions and the VES formula
```
## Task record
```json
{"task_id": "...", "task_type": "A", "task_subtype": "themed_montage", "brief": "...",
"target_duration": 24.0,
"constraint_ledger": [{"item_id": "...", "type": "must_keep_clip", "spec": {...}, "satisfied": false, "evidence": null}, ...],
"clip_pool": ["clip_0001", ...], "structure": {...}, "canonical_id": "..."}
```
Constraint types: `must_keep_clip`, `must_exclude_clip`, `target_duration`, `duration_tolerance`, `pacing`,
`transition_style`, `music_sync_bpm`, `music_sync_beat`, `must_open_with`, `must_close_with`,
`no_repeat_within_seconds`, `max_repeats_per_clip`, `tag_inclusion`, `tag_exclusion`. An entry is a hard
constraint when its spec admits a deterministic pass/fail test; softer entries earn graded credit through the
fractional constraint-satisfaction rate.
## Evaluation protocol
Closed loop with at most T=3 repair steps, greedy decoding, one fixed PatchPlanner prompt, and the frozen
Common-100 list. `VES = 0.30 FinalCSR + 0.15 HardPass + 0.15 PASR + 0.15 ReqClipRecall + 0.10 DurationPass +
0.10 TimelineValidity + 0.05 NoRegression` (`docs/METRIC_DEFINITIONS.md`). VES is a protocol-specific
executable-planning score; rendered video quality is evaluated separately.
## Sources and license
Clips: Pexels (3,219), Panda-70M sample (3,990), Pixabay (255), OpenVid (100), and 5-30 s segments of
non-game long videos (407); music features from FMA tracks. **Raw video and audio are not redistributed**;
`clips/clips_meta/` gives the public source identifiers. Benchmark metadata, captions, ledgers, and
trajectories: CC BY-NC 4.0; sources retain their original licenses.
## Citation
```bibtex
@inproceedings{refinecut2026,
title = {Plans You Can Check: Verifier-Grounded Learning of an Open-Weight Planner for Executable Video-Editing},
author = {Wang, Haoyu and Feng, Cheng and Bian, Liuyang and Huang, Ruiyang and Wei, Lei and Wen, Yafei and Chen, Xiaoxin and Tang, Xiaoying},
booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
year = {2026},
note = {to appear}
}
```
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