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world-model
video-generation
benchmark
compositional-consistency
reproducibility
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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:    ValueError
Message:      Invalid string class label Evaluation@73755771d2b56be2d0fe575007282c71ece3278a
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label Evaluation@73755771d2b56be2d0fe575007282c71ece3278a

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Reasoning-Structured Videos: Evaluation Artifacts

This page hosts evaluation artifacts for Reasoning-Structured Videos: A Stratified Diagnostic Suite for Compositional Consistency in World Models.

The benchmark tests whether action-conditioned video world models respect three trajectory relations:

  • Inverse: a path followed by its inverse should return to the initial state.
  • Loop: a closed path should return to the initial state.
  • Equivalence: two different paths reaching the same state should produce matching endpoints.

We report self-consistency (SC) as the primary cross-model diagnostic and use GT-anchor and distributional metrics as complementary evidence.

Files

File Description
matrix_game.zip 1,371 released Matrix-Game 2.0 SC rollout videos
evaluate_matrix_game_sc.py Standalone Matrix-Game SC reproduction script
Reasoning-Structured-Videos-Rebuttal-main.zip Frozen R20/R50 manifests, minWM/HY-WorldPlay camera adapters, metric evaluators, tests, and result summaries

Reference videos and trajectory metadata are hosted separately in VideoWorldmodel/ReasoningStructureTestset.

Main results

Common camera-trajectory SC (frozen R20)

minWM and HY-WorldPlay receive the same frozen graph IDs and camera trajectories through their official pose-control pathways. Each model completed 80/80 rollouts with zero evaluation failures.

Model Inverse LPIPS / PSNR Loop LPIPS / PSNR Equivalence LPIPS / PSNR
HY-WorldPlay 1.5 AR Distill 0.4038 / 16.12 0.4562 / 15.48 0.3542 / 17.37
minWM 0.7229 / 10.46 0.7442 / 11.08 0.4227 / 14.05

R20 contains 20 graphs per relation: 60 graph units and 80 videos because every Equivalence graph has A/B branches. The code archive includes graph-bootstrap intervals and the corresponding GT-anchor summaries.

Matrix-Game Inverse SC versus revisit horizon

All 448 Inverse graphs are evaluated at matched-state revisits with increasing action separation. “Physical frame” denotes the zero-indexed position in the saved Matrix MP4. Matrix exports 357 frames for the complete 40-action schedule, so action boundary k is mapped to physical frame round(356k/40), anchoring both endpoints and preserving outward/return symmetry.

Revisit horizon LPIPS (95% CI) PSNR dB (95% CI)
10 actions 0.4851 [0.4652, 0.5053] 14.03 [13.53, 14.53]
20 actions 0.6106 [0.5976, 0.6234] 11.46 [11.14, 11.77]
30 actions 0.6685 [0.6584, 0.6784] 10.71 [10.45, 10.96]
Full rollout (40 actions) 0.7062 [0.6973, 0.7151] 10.45 [10.21, 10.69]

The paired full-minus-10 change is +0.2211 LPIPS [0.1988, 0.2434] and -3.58 dB PSNR [-4.02, -3.15]. Adjacent boundary-rounding schemes preserve the monotonic trend and change intermediate means by at most 0.005 LPIPS / 0.10 dB.

Endpoint distribution fidelity

Endpoint FID/KID compare generated and matched GT logical raw359 endpoint sets using clean-fid Inception-v3 pool3 features.

Matrix-Game, complete available output set:

Relation Graphs / images Endpoint FID ↓ KID ×1000 ↓
Inverse 448 / 448 134.94 23.018
Loop 445 / 445 141.33 24.362
Equivalence 239 / 478 145.55 22.943

minWM, frozen R50 SC outputs:

Relation Graphs / images Endpoint FID ↓ KID ×1000 ↓
Inverse 50 / 50 242.46 47.860
Loop 50 / 50 249.40 54.173
Equivalence 50 / 100 240.23 50.359

Endpoint FID is an image-set metric and is not numerically comparable to the clip-level FVD reported elsewhere. The Matrix analysis also includes 20/50/100/200/full graph sensitivity and a GT-vs-GT finite-sample floor. These endpoint metrics complement, rather than replace, paired SC LPIPS/PSNR.

Quick reproduction: Matrix-Game SC

This path requires only the files on the current page; no GT dataset is needed.

python -m pip install -U huggingface_hub

hf download VideoWorldmodel/Evaluation \
  matrix_game.zip evaluate_matrix_game_sc.py \
  --repo-type dataset \
  --local-dir .

unzip matrix_game.zip -d data/MatrixGame2_SC_videos

python -m pip install \
  "numpy>=1.26,<3" \
  "opencv-python-headless>=4.8,<5" \
  "torch>=2.2,<3" \
  "torchvision>=0.17,<1" \
  "lpips==0.1.4"

python evaluate_matrix_game_sc.py \
  --data data/MatrixGame2_SC_videos \
  --output results/matrix_game_sc \
  --lpips \
  --device auto \
  --check-paper

If the archive creates one additional top-level directory, point --data to the directory that directly contains the five relation folders.

Expected values:

Relation Graph N Recomputed LPIPS / PSNR Paper LPIPS / PSNR
Inverse-SC 448 0.7061 / 10.4476 0.71 / 10.45
Loop-SC 445 0.7173 / 10.6227 0.72 / 10.62
Equivalence-SC 239 0.5918 / 12.5732 0.59 / 12.57

The evaluator verifies file counts and Equivalence pair integrity, and writes per-graph scores, graph-bootstrap confidence intervals, an audit, and a paper-value check.

Reproduce the additional analyses

Download and unpack the code artifact:

hf download VideoWorldmodel/Evaluation \
  Reasoning-Structured-Videos-Rebuttal-main.zip \
  --repo-type dataset \
  --local-dir .

unzip Reasoning-Structured-Videos-Rebuttal-main.zip
cd Reasoning-Structured-Videos-Rebuttal-main

python -m pip install -r requirements-eval.txt
python -m unittest discover -s tests -v

The repository README documents four reproducibility paths:

  1. recompute SC/GT LPIPS and PSNR from minWM or HY-WorldPlay rollout directories;
  2. recompute Matrix-Game Inverse SC versus revisit horizon directly from matrix_game.zip;
  3. recompute relation-wise endpoint FID/KID;
  4. run the full Matrix endpoint audit using matrix_game.zip and the GT dataset.

The code archive contains frozen result summaries but not the large minWM/HY-WorldPlay rollout videos or model checkpoints. Regenerating those videos requires the official minWM or HY-WorldPlay repository, its checkpoint, its official environment, and suitable GPUs.

Frozen identifiers

  • Public subset seed: 2357
  • R20 manifest SHA-256: f8307b78ffb4633b1d6ca6340498a9db615dca4cc45a045d16139abf4375abdf
  • Nested R20/R50 manifest SHA-256: c1b3e051ec861f6babd00f67cf7c31d0ceba590d93a6fac1ac6a768b40448db4
  • matrix_game.zip SHA-256: caec3d8deb20cffbc645f0aeeaa2af425536715eef935b17347cdfab728a88d4

Scope

The released experiments evaluate deterministic camera trajectories in static scenes. They support relation-specific diagnostic conclusions under the stated protocol; they are not intended as a universal ranking of world-model architectures.
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