simverse2026 / croissant.json
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{
"@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": {
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"@type": "@vocab"
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"dct": "http://purl.org/dc/terms/",
"examples": {
"@id": "cr:examples",
"@type": "@json"
},
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"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",
"conformsTo": "http://mlcommons.org/croissant/1.0",
"name": "SimVerse",
"description": "A multi-task benchmark for evaluating multimodal LLMs on interactive simulation puzzles. Five independent tasks (Text-VOI placements, cube reconstruction, cube goal-roll, mechanical-arm lamp targeting, Cut the Rope video-to-command) share a uniform 9-section prompt skeleton and a uniform FINAL_JSON output contract for cross-task comparability. Each per-level record carries the literal prompt text the benchmark presents to models, so reproductions need no auxiliary code. Anonymized for double-blind review; author and citation fields contain placeholder values until the review concludes.",
"license": "https://opensource.org/licenses/MIT",
"url": "https://huggingface.co/datasets/SimVer-ano/simverse2026",
"version": "1.0.0",
"datePublished": "2026",
"keywords": [
"benchmark",
"multimodal",
"spatial-reasoning",
"video-understanding",
"tool-use",
"evaluation"
],
"rai:dataCollection": "All five tasks were programmatically generated. VOI uses a polygon-rasterization XOR generator. cube1 and cube2 use a pure-Python cube-state simulator that records roll sequences and bottom-face imprints. lamp uses a forward-kinematics generator with axis-aligned obstacle placement. cutRope is built on top of the open-source yell0wsuit/cuttherope-h5dx HTML5 port (MIT-licensed) by recording deterministic gameplay clips and authoring matching command scripts. No human subjects or real-world data were involved.",
"rai:dataAnnotationProtocol": "Reference solutions are produced by the same generators that create each puzzle (programmatic ground truth). For closed-form tasks (VOI, cube1, lamp), the answer is uniquely determined and machine-verifiable. For open-ended tasks (cube2 goal-roll, cutRope), the dataset's `answer` field carries one known-valid reference solution; validators run the underlying engine against the model's actual output rather than performing string equality, so multiple correct answers earn full credit.",
"rai:dataAnnotationAnalysis": "Each generator's output was post-validated by the same engine the eval validator uses, so every shipped record is provably solvable. Sentinel '?' values appear in cube1 records when puzzle constraints leave a face under-determined; this is intentional and follows the documented sentinel-pair rule (rotation forced to 0 when patternId is '?').",
"rai:dataPreprocessingProtocol": "All per-level JSON files conform to a v1 schema documented in PROMPT_SKELETON.md (locked schemas per task). A migration step lifts legacy task-native answer formats into the unified `answer` envelope and a separate populate_prompts step embeds the literal system+user prompt strings into each record. Both transformations are idempotent and reproducible from the accompanying code.",
"rai:dataLimitations": "Synthetic puzzles in fixed visual styles per task — models may learn render-style shortcuts rather than the underlying reasoning skill. All prompts are English-only. Difficulty distribution is hand-tuned per generator and not necessarily uniform. cube1 includes '?' sentinel patterns when faces are under-determined; downstream uses outside the SimVerse evaluation flow may need to filter these. Because reference solutions for open-ended tasks are non-unique, simple string-match scoring is inappropriate and the bundled engine-based validator should be used.",
"rai:dataBiases": "Each task's visual style is uniform across all of its records, which can bias evaluations toward render-style recognition rather than the reasoning skill the task is intended to probe. Reference solutions for open-ended tasks favor specific solution paths even when other paths are equally valid; validators are designed to score by simulation outcome rather than reference matching, but downstream comparisons that ignore this design will systematically under-credit divergent strategies. Object-count distributions in cutRope reflect the original yell0wsuit corpus and are not balanced across gameplay-element categories.",
"rai:personalSensitiveInformation": "None. Fully synthetic; the dataset contains no personally identifiable information, no human-subject data, and no sensitive content. cutRope videos are gameplay recordings of an open-source physics-puzzle game with no real-world imagery.",
"rai:dataUseCases": "Evaluating multimodal large language models on (a) spatial-pattern reconstruction (VOI, cube1, cube2), (b) multi-step planning under physical constraints (lamp, cube2, cutRope), and (c) short-horizon video-to-program inference (cutRope). The dataset is designed strictly for held-out benchmarking — there is no train split. It is NOT intended as a held-out training set, NOT a real-world capability predictor, and NOT a substitute for application-specific evaluation.",
"rai:dataSocialImpact": "Low. Synthetic abstract puzzles are unlikely to encode harmful content or to enable misuse beyond benchmark gaming. Primary risk: if widely adopted, models tuned specifically on these visual styles may show inflated capability claims that do not transfer to real-world spatial reasoning tasks. The dataset is purely for evaluation and contains no instructions toward harmful behaviors.",
"rai:dataReleaseMaintenancePlan": "All levels are deterministically regenerable from the generators in the accompanying code repository (linked from the dataset card under the post-acceptance camera-ready URL). Schema and prompt-template changes are released as new versioned snapshots; per-record `legacy_*` fields preserve pre-migration values for one release cycle to ease comparisons across versions.",
"distribution": [
{
"@type": "cr:FileObject",
"@id": "voi-test-jsonl",
"name": "voi/test.jsonl",
"description": "Per-record JSON-Lines for the VOI (Text-VOI placements) task, one level per line.",
"contentUrl": "voi/test.jsonl",
"encodingFormat": "application/jsonlines",
"sha256": "cfd99ae4cf52689d26c6e7d2488a124636c2d8aa73d3f48ed80cd916b8edb572",
"contentSize": "4230614 B"
},
{
"@type": "cr:FileSet",
"@id": "voi-images",
"name": "voi/images",
"description": "Per-level rendered images (target pattern + base-shape pieces) for VOI.",
"encodingFormat": "image/png",
"includes": "voi/images/**/*.png"
},
{
"@type": "cr:FileObject",
"@id": "cube1-test-jsonl",
"name": "cube1/test.jsonl",
"description": "Per-record JSON-Lines for the cube reconstruction task.",
"contentUrl": "cube1/test.jsonl",
"encodingFormat": "application/jsonlines",
"sha256": "40d836f859c2ae775ba7649702623ea2f7d48410d8c394ee78feda06d42dfb3a",
"contentSize": "4680065 B"
},
{
"@type": "cr:FileSet",
"@id": "cube1-images",
"name": "cube1/images",
"description": "Blank-cross-net and path-imprint images for cube reconstruction.",
"encodingFormat": "image/png",
"includes": "cube1/images/**/*.png"
},
{
"@type": "cr:FileObject",
"@id": "cube2-test-jsonl",
"name": "cube2/test.jsonl",
"description": "Per-record JSON-Lines for the cube goal-roll task.",
"contentUrl": "cube2/test.jsonl",
"encodingFormat": "application/jsonlines",
"sha256": "3055027fd6946cc6711be3bc6717347b80266e80c2fd8dde48844df9f9e3413f",
"contentSize": "4525952 B"
},
{
"@type": "cr:FileSet",
"@id": "cube2-images",
"name": "cube2/images",
"description": "Initial-net and target-top-face images for cube goal-roll.",
"encodingFormat": "image/png",
"includes": "cube2/images/**/*.png"
},
{
"@type": "cr:FileObject",
"@id": "lamp-test-jsonl",
"name": "lamp/test.jsonl",
"description": "Per-record JSON-Lines for the mechanical-lamp targeting task.",
"contentUrl": "lamp/test.jsonl",
"encodingFormat": "application/jsonlines",
"sha256": "6d92c608683f02228ca5dc32c448863889a1aed441960b9df18dac1dbeb77889",
"contentSize": "3742112 B"
},
{
"@type": "cr:FileSet",
"@id": "lamp-images",
"name": "lamp/images",
"description": "Workspace images (arm, target, obstacles) for the mechanical-lamp task.",
"encodingFormat": "image/png",
"includes": "lamp/images/**/*.png"
},
{
"@type": "cr:FileObject",
"@id": "cutrope-test-jsonl",
"name": "cutrope/test.jsonl",
"description": "Per-record JSON-Lines for the Cut the Rope video-to-command task.",
"contentUrl": "cutrope/test.jsonl",
"encodingFormat": "application/jsonlines",
"sha256": "06b28abdf1d57be23abc18c7e57211bb8e5efcc017e746c7b3a9268c9ed35235",
"contentSize": "2634596 B"
},
{
"@type": "cr:FileSet",
"@id": "cutrope-videos",
"name": "cutrope/videos",
"description": "Short MP4 gameplay clips (~3s each, 1920x1080) for cutRope.",
"encodingFormat": "video/mp4",
"includes": "cutrope/videos/*.mp4"
}
],
"recordSet": [
{
"@type": "cr:RecordSet",
"@id": "voi",
"name": "voi",
"description": "Text-VOI placements task: 600 records.",
"field": [
{
"@type": "cr:Field",
"@id": "voi/sample_id",
"name": "sample_id",
"description": "Stable level id, e.g. voi-000.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "voi-test-jsonl"
},
"extract": {
"jsonPath": "$.__sample_id__"
}
}
},
{
"@type": "cr:Field",
"@id": "voi/prompt_system",
"name": "prompt_system",
"description": "Verbatim system-prompt text the benchmark presents.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "voi-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.system"
}
}
},
{
"@type": "cr:Field",
"@id": "voi/prompt_user",
"name": "prompt_user",
"description": "Verbatim user-prompt text (9-section skeleton, instance-filled).",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "voi-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.user"
}
}
},
{
"@type": "cr:Field",
"@id": "voi/answer",
"name": "answer",
"description": "Reference placements list. Schema: {placements:[{shape,angle,vertex,grid}]}. (Stored as a JSON-encoded string.)",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "voi-test-jsonl"
},
"extract": {
"jsonPath": "$.answer"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "cube1",
"name": "cube1",
"description": "Cube reconstruction task: 502 records.",
"field": [
{
"@type": "cr:Field",
"@id": "cube1/sample_id",
"name": "sample_id",
"description": "Stable level id, e.g. C001.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube1-test-jsonl"
},
"extract": {
"jsonPath": "$.__sample_id__"
}
}
},
{
"@type": "cr:Field",
"@id": "cube1/prompt_system",
"name": "prompt_system",
"description": "Verbatim system-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube1-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.system"
}
}
},
{
"@type": "cr:Field",
"@id": "cube1/prompt_user",
"name": "prompt_user",
"description": "Verbatim user-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube1-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.user"
}
}
},
{
"@type": "cr:Field",
"@id": "cube1/answer",
"name": "answer",
"description": "Reference six-face map. Schema: {faces:{TOP:{patternId,rotation},...}}. (Stored as a JSON-encoded string.)",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube1-test-jsonl"
},
"extract": {
"jsonPath": "$.answer"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "cube2",
"name": "cube2",
"description": "Cube goal-roll task: 502 records.",
"field": [
{
"@type": "cr:Field",
"@id": "cube2/sample_id",
"name": "sample_id",
"description": "Stable level id, e.g. C001.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube2-test-jsonl"
},
"extract": {
"jsonPath": "$.__sample_id__"
}
}
},
{
"@type": "cr:Field",
"@id": "cube2/prompt_system",
"name": "prompt_system",
"description": "Verbatim system-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube2-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.system"
}
}
},
{
"@type": "cr:Field",
"@id": "cube2/prompt_user",
"name": "prompt_user",
"description": "Verbatim user-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube2-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.user"
}
}
},
{
"@type": "cr:Field",
"@id": "cube2/answer",
"name": "answer",
"description": "One known-valid roll sequence. Schema: {directions:['N'|'S'|'E'|'W',...]}. (Stored as a JSON-encoded string.)",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cube2-test-jsonl"
},
"extract": {
"jsonPath": "$.answer"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "lamp",
"name": "lamp",
"description": "Mechanical-lamp targeting task: 610 records.",
"field": [
{
"@type": "cr:Field",
"@id": "lamp/sample_id",
"name": "sample_id",
"description": "Stable level id, e.g. lamp-000.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "lamp-test-jsonl"
},
"extract": {
"jsonPath": "$.__sample_id__"
}
}
},
{
"@type": "cr:Field",
"@id": "lamp/prompt_system",
"name": "prompt_system",
"description": "Verbatim system-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "lamp-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.system"
}
}
},
{
"@type": "cr:Field",
"@id": "lamp/prompt_user",
"name": "prompt_user",
"description": "Verbatim user-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "lamp-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.user"
}
}
},
{
"@type": "cr:Field",
"@id": "lamp/answer",
"name": "answer",
"description": "Reference per-joint angles. Schema: {actions:[{joint,angle},...]}. (Stored as a JSON-encoded string.)",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "lamp-test-jsonl"
},
"extract": {
"jsonPath": "$.answer"
}
}
}
]
},
{
"@type": "cr:RecordSet",
"@id": "cutrope",
"name": "cutrope",
"description": "Cut the Rope video-to-command task: 272 records.",
"field": [
{
"@type": "cr:Field",
"@id": "cutrope/sample_id",
"name": "sample_id",
"description": "Stable level id, e.g. rope-000.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cutrope-test-jsonl"
},
"extract": {
"jsonPath": "$.__sample_id__"
}
}
},
{
"@type": "cr:Field",
"@id": "cutrope/prompt_system",
"name": "prompt_system",
"description": "Verbatim system-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cutrope-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.system"
}
}
},
{
"@type": "cr:Field",
"@id": "cutrope/prompt_user",
"name": "prompt_user",
"description": "Verbatim user-prompt text.",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cutrope-test-jsonl"
},
"extract": {
"jsonPath": "$.prompt.user"
}
}
},
{
"@type": "cr:Field",
"@id": "cutrope/answer",
"name": "answer",
"description": "Reference 3-star command script. Schema: {commands,reason,confidence}. (Stored as a JSON-encoded string.)",
"dataType": "sc:Text",
"source": {
"fileObject": {
"@id": "cutrope-test-jsonl"
},
"extract": {
"jsonPath": "$.answer"
}
}
}
]
}
],
"citeAs": "@dataset{simverse_anonymous_2026,\n title = {SimVerse: A Multi-Task Benchmark for Multimodal Reasoning on Interactive Simulation Puzzles},\n author = {Anonymous Authors (under double-blind review)},\n year = {2026},\n url = {https://huggingface.co/datasets/SimVer-ano/simverse2026}\n}",
"rai:hasSyntheticData": true,
"rai:hasPersonalInformation": false,
"rai:hasSensitivePersonalInformation": false
}