Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
During handling of the above exception, another exception occurred:
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 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ThunderWorld benchmark generated videos
This release contains 33,880 generated MP4 videos for Cosmos3 Edge and Nano, evaluated on the full RBench and PAI-Bench scene inventories used by this study. Each model/method combination has two fixed seeds per scene: 650 RBench scenes and 1,044 PAI-Bench scenes. The files are generated outputs for downstream judging; judge scores are not included in this release.
Download packages
Each ZIP is independently usable and includes all five methods for its model and benchmark. Sizes below are decimal GB.
| Package | Model | Benchmark | Videos | Size |
|---|---|---|---|---|
| edge_rbench.zip | Edge | RBench | 6,500 | 2.106 GB |
| edge_paibench.zip | Edge | PAI-Bench | 10,440 | 3.496 GB |
| nano_rbench.zip | Nano | RBench | 6,500 | 1.891 GB |
| nano_paibench.zip | Nano | PAI-Bench | 10,440 | 3.302 GB |
Total ZIP payload: 10,794,295,158 bytes (10.79 GB).
packages.json and the four *.package.json files contain exact sizes, SHA256
checksums, sample counts, source fingerprints and verification results.
hf download Concyclics/thunderworld-benchmark --type dataset --local-dir thunderworld-benchmark
cd thunderworld-benchmark
sha256sum -c SHA256SUMS
unzip edge_rbench.zip
cd edge_rbench
sha256sum -c SHA256SUMS
The other ZIP files extract to edge_paibench/, nano_rbench/, and nano_paibench/.
Methods and generation settings
| Method ID in metadata | Configuration |
|---|---|
arcquant |
ARCQuant, 50% activation correction |
arcquant100 |
ARCQuant, 100% activation correction |
sharq_perf |
SharQ baseline |
b1.5_protected_n |
ThunderWorld 1.5x repair configuration |
b2_joint |
ThunderWorld 2x joint repair configuration |
Per-method runtime definitions, scope and limitations are preserved in each video's
original metadata (method_spec) and the frozen model manifest. These labels identify
the study configurations; the full method specification is authoritative.
- Output resolution: 320 x 192.
- Video length: 61 frames at 24 FPS.
- Generation: 35 steps; exact seeds and request records are included.
- Five methods x two seeds x (650 + 1,044) scenes x two models = 33,880 videos.
- Generation finished on 2026-09-21; this delivery packages the frozen full I2V run.
- Runs used shared GPU resources. Recorded request times are not an exclusive-device performance benchmark.
Package layout and portable indices
edge_rbench/
manifest.json
judge_index.jsonl
judge_inputs.jsonl
SHA256SUMS
README.md
videos/<sample_id>.mp4
inputs/<case_id>.jpg
metadata/<sample_id>.json
receipts/<method>/<case_id>.json
logs/<log_id>.log
provenance/
judge_index.jsonlmaps each anonymous sample ID to model, method, benchmark, scene, seed, prompt, input image, video, original metadata, generation receipt and generation log. It also records video/input hashes and output dimensions.judge_inputs.jsonlprovides sample ID, benchmark/category/source ID, prompt, input image and video path without a method label.- Paths in both indices are relative to their extracted package directory. Resolve them against that directory when evaluating on another machine.
- Original metadata and receipts retain original source-machine paths as provenance. Use the portable indices to locate files in the downloaded package.
- Input images and referenced generation logs are deduplicated within each package. Source symlinks were dereferenced into regular ZIP members, so no source directory is required after extraction.
Encoding and integrity
The MP4s are the existing H.264/yuv420p, CRF 15 exports, copied without re-encoding during packaging. All MP4 and input-image bytes were checked against their recorded export SHA256 hashes. Every archive member was read back and verified by SHA256 and ZIP CRC; one video per method per package was decoded to verify frame count, shape and FPS (20 representative videos total).
Lossless MKV copies, original floating-point NPZ outputs and model weights are not included in these MP4 delivery packages. For pixel-exact comparisons, the MP4 codec changes must be distinguished from model output differences; the original lossless and floating-point artifacts remain in the source run.
Provenance
RBench and PAI-Bench input images, prompts and source IDs are included as used by the frozen generation manifests. The packages preserve the original model/method metadata and source fingerprints. This dataset card does not assign a new license to upstream benchmark inputs or model assets; consult their original terms for reuse.
The code project is Concyclics/ThunderWorld.
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