| { |
| "@context": { |
| "@language": "en", |
| "@vocab": "https://schema.org/", |
| "citeAs": "cr:citeAs", |
| "column": "cr:column", |
| "conformsTo": "dct:conformsTo", |
| "cr": "http://mlcommons.org/croissant/", |
| "rai": "http://mlcommons.org/croissant/RAI/", |
| "data": {"@id": "cr:data", "@type": "@json"}, |
| "dataType": {"@id": "cr:dataType", "@type": "@vocab"}, |
| "dct": "http://purl.org/dc/terms/", |
| "examples": {"@id": "cr:examples", "@type": "@json"}, |
| "extract": "cr:extract", |
| "field": "cr:field", |
| "fileProperty": "cr:fileProperty", |
| "fileObject": "cr:fileObject", |
| "fileSet": "cr:fileSet", |
| "format": "cr:format", |
| "includes": "cr:includes", |
| "isLiveDataset": "cr:isLiveDataset", |
| "jsonPath": "cr:jsonPath", |
| "key": "cr:key", |
| "md5": "cr:md5", |
| "parentField": "cr:parentField", |
| "path": "cr:path", |
| "recordSet": "cr:recordSet", |
| "references": "cr:references", |
| "regex": "cr:regex", |
| "repeated": "cr:repeated", |
| "replace": "cr:replace", |
| "sc": "https://schema.org/", |
| "separator": "cr:separator", |
| "source": "cr:source", |
| "subField": "cr:subField", |
| "transform": "cr:transform" |
| }, |
| "@type": "sc:Dataset", |
| "name": "VBVR-MultiStep", |
| "conformsTo": "http://mlcommons.org/croissant/1.0", |
| "description": "The ~360,000-sample programmatic training corpus for long-horizon multi-step image-to-video reasoning. 36 parameterized tasks across six reasoning families (Navigation, Planning, CSP, Execution, Geometry, Physics). Distributed as 7,200 tar.gz shards (≈50 samples per shard) plus Parquet metadata; each instance follows a five-artifact contract identical to the VBVR-MultiStep-Bench evaluation split.", |
| "alternateName": ["VBVR-MultiStep Training Corpus"], |
| "creator": { |
| "@type": "sc:Organization", |
| "name": "Video-Reason", |
| "url": "https://video-reason.com" |
| }, |
| "datePublished": "2026-05-06", |
| "keywords": ["video reasoning", "multi-step reasoning", "long-horizon", "image-to-video", "training", "synthetic", "tar.gz", "parquet"], |
| "license": "https://creativecommons.org/licenses/by/4.0/", |
| "url": "https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep", |
| "version": "1.0.0", |
| "isLiveDataset": false, |
| "rai:dataCollection": "Fully synthetic. Each of the 36 tasks ships a deterministic generator that emits the five-artifact contract (first_frame.png, prompt.txt, final_frame.png, ground_truth.mp4, question_metadata.json) from a (task, seed) pair. No scraping, no human subjects, no third-party media, no manual annotation. The training corpus is partitioned into disjoint seed bands (1–5,000 and 5,001–10,000 per task) that are themselves disjoint from the evaluation seeds released in VBVR-MultiStep-Bench.", |
| "rai:dataCollectionType": ["Synthetic"], |
| "rai:hasSyntheticData": true, |
| "rai:dataPreprocessingProtocol": "Per-task generator output is grouped into 50-sample batches and packed into tar.gz shards under questions/. Per-task Parquet metadata files (data/metadata_shards/) and a global metadata.parquet index every instance with task id, family, seed, and per-task fields. No sample is filtered, dropped, or transformed after generation.", |
| "rai:dataAnnotationProtocol": "No human annotation. ground_truth.mp4 is rendered by each task's deterministic ground-truth solver.", |
| "rai:dataAnnotationPlatform": "N/A (no annotation).", |
| "rai:dataReleaseMaintenancePlan": "Versioned releases on the Hugging Face Hub. The Croissant file in this repository is the canonical long-term record. A 5 GB representative subset is provided under sample/ for quick inspection and reviewer convenience.", |
| "rai:dataLimitations": [ |
| "Synthetic and stylized: transfer to unconstrained open-world video is not validated.", |
| "Visual rendering is intentionally simplified to keep the symbolic state recoverable from frames; this is not a photorealism corpus.", |
| "Per-task generator parameter ranges are bounded (e.g., maze sizes, planning horizons, physics regimes); the corpus does not span the long tail of any single family.", |
| "Reference rollouts encode one valid trajectory per instance; alternative valid trajectories are not enumerated.", |
| "Although 36 tasks ship, only 34 are used in the training experiments described in the companion paper; this release contains all 36 task families." |
| ], |
| "rai:dataBiases": [ |
| "Family balance is uniform (6 tasks per family) by design and does not reflect natural prevalence of these reasoning patterns.", |
| "Generator parameters bias the difficulty distribution toward bounded and seed-controlled regimes that are amenable to symbolic ground truth; rare or open-ended cases are out of scope.", |
| "Visual style is monocular, planar, and rendered by a fixed family of renderers; appearance distribution does not approximate any real-world video corpus and does not contain demographic content.", |
| "No human demographic information is generated; bias along human demographic axes does not apply." |
| ], |
| "rai:personalSensitiveInformation": "None. The dataset contains no personal information, no biometric data, no demographic information, and no human subjects. All visual content is procedurally generated geometric, symbolic, or physical scenes.", |
| "rai:dataUseCases": "Training image-to-video systems on long-horizon multi-step reasoning under explicit per-step rules. Validated use case in the companion paper: fine-tuning Wan2.2-I2V-A14B (Apache-2.0) with Dual-DiT two-phase LoRA. Out-of-scope: production VLM pretraining at scale, real-world video generation, or any safety-critical use.", |
| "rai:dataSocialImpact": "Intended for academic research on reasoning evaluation in video generation. Risks are minimal: the dataset is synthetic, free of personal content, and rendered in a stylized regime not representative of any real population. The most plausible concern is research-direction effects (e.g., over-investing in stylized synthetic benchmarks), which we mitigate by positioning this corpus as a complement to (not a replacement for) appearance-centric and real-world video corpora.", |
| "rai:dataReleaseUpdate": "If post-release errors are discovered, fixes will be published as additive shards or replacement Parquet entries in a new dataset version, with the prior version retained at its commit hash for backwards reproducibility." |
| } |
|
|
