Plans You Can Check: Verifier-Grounded Learning of an Open-Weight Planner for Executable Video-Editing
Paper • 2608.25622 • Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
repair_id: string
task_id: string
split: string
task_type: string
task_subtype: string
brief: string
target_duration: int64
constraint_ledger: list<item: struct<evidence: string, item_id: string, satisfied: bool, spec: struct<seconds: double, (... 291 chars omitted)
child 0, item: struct<evidence: string, item_id: string, satisfied: bool, spec: struct<seconds: double, clip_id: st (... 279 chars omitted)
child 0, evidence: string
child 1, item_id: string
child 2, satisfied: bool
child 3, spec: struct<seconds: double, clip_id: string, subject: string, scene_category: string, style: string, ele (... 199 chars omitted)
child 0, seconds: double
child 1, clip_id: string
child 2, subject: string
child 3, scene_category: string
child 4, style: string
child 5, element: string
child 6, alternation: list<item: string>
child 0, item: string
child 7, max: int64
child 8, scene_category_sequence: list<item: string>
child 0, item: string
child 9, theme: string
child 10, bpm: int64
child 11, enabled: bool
child 12, motion_arc: list<item: string>
child 0, item: string
child 13, visual_error: string
child 4, type: string
structure: struct<include_opening: bool, include_outro: bool, pacing: string, transition_style: string>
child 0, include_opening: bool
child 1, include_outro: bo
...
p_usage_policy: struct<>
child 12, generated_assets: list<item: null>
child 0, item: null
violated_constraints: list<item: struct<item_id: string, type: string, satisfied: bool, evidence: string>>
child 0, item: struct<item_id: string, type: string, satisfied: bool, evidence: string>
child 0, item_id: string
child 1, type: string
child 2, satisfied: bool
child 3, evidence: string
already_satisfied_constraints: list<item: struct<item_id: string, type: string, satisfied: bool, evidence: string>>
child 0, item: struct<item_id: string, type: string, satisfied: bool, evidence: string>
child 0, item_id: string
child 1, type: string
child 2, satisfied: bool
child 3, evidence: string
expected_repair_fields: list<item: string>
child 0, item: string
clip_pool_metadata: list<item: struct<clip_id: string, caption_short: string, subject: string, action: string, scene_cat (... 59 chars omitted)
child 0, item: struct<clip_id: string, caption_short: string, subject: string, action: string, scene_category: stri (... 47 chars omitted)
child 0, clip_id: string
child 1, caption_short: string
child 2, subject: string
child 3, action: string
child 4, scene_category: string
child 5, motion_intensity: string
child 6, duration: double
requires_clip_reference: bool
failure_template: string
closed_loop_id: string
max_steps: int64
task_ids: list<item: string>
child 0, item: string
n: int64
to
{'n': Value('int64'), 'task_ids': List(Value('string'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
repair_id: string
task_id: string
split: string
task_type: string
task_subtype: string
brief: string
target_duration: int64
constraint_ledger: list<item: struct<evidence: string, item_id: string, satisfied: bool, spec: struct<seconds: double, (... 291 chars omitted)
child 0, item: struct<evidence: string, item_id: string, satisfied: bool, spec: struct<seconds: double, clip_id: st (... 279 chars omitted)
child 0, evidence: string
child 1, item_id: string
child 2, satisfied: bool
child 3, spec: struct<seconds: double, clip_id: string, subject: string, scene_category: string, style: string, ele (... 199 chars omitted)
child 0, seconds: double
child 1, clip_id: string
child 2, subject: string
child 3, scene_category: string
child 4, style: string
child 5, element: string
child 6, alternation: list<item: string>
child 0, item: string
child 7, max: int64
child 8, scene_category_sequence: list<item: string>
child 0, item: string
child 9, theme: string
child 10, bpm: int64
child 11, enabled: bool
child 12, motion_arc: list<item: string>
child 0, item: string
child 13, visual_error: string
child 4, type: string
structure: struct<include_opening: bool, include_outro: bool, pacing: string, transition_style: string>
child 0, include_opening: bool
child 1, include_outro: bo
...
p_usage_policy: struct<>
child 12, generated_assets: list<item: null>
child 0, item: null
violated_constraints: list<item: struct<item_id: string, type: string, satisfied: bool, evidence: string>>
child 0, item: struct<item_id: string, type: string, satisfied: bool, evidence: string>
child 0, item_id: string
child 1, type: string
child 2, satisfied: bool
child 3, evidence: string
already_satisfied_constraints: list<item: struct<item_id: string, type: string, satisfied: bool, evidence: string>>
child 0, item: struct<item_id: string, type: string, satisfied: bool, evidence: string>
child 0, item_id: string
child 1, type: string
child 2, satisfied: bool
child 3, evidence: string
expected_repair_fields: list<item: string>
child 0, item: string
clip_pool_metadata: list<item: struct<clip_id: string, caption_short: string, subject: string, action: string, scene_cat (... 59 chars omitted)
child 0, item: struct<clip_id: string, caption_short: string, subject: string, action: string, scene_category: stri (... 47 chars omitted)
child 0, clip_id: string
child 1, caption_short: string
child 2, subject: string
child 3, action: string
child 4, scene_category: string
child 5, motion_intensity: string
child 6, duration: double
requires_clip_reference: bool
failure_template: string
closed_loop_id: string
max_steps: int64
task_ids: list<item: string>
child 0, item: string
n: int64
to
{'n': Value('int64'), 'task_ids': List(Value('string'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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
{"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
@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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