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"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.",
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"description": "Reference placements list. Schema: {placements:[{shape,angle,vertex,grid}]}. (Stored as a JSON-encoded string.)",
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"source": {
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{
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"name": "cube1",
"description": "Cube reconstruction task: 502 records.",
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"name": "sample_id",
"description": "Stable level id, e.g. C001.",
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"source": {
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"extract": {
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"@id": "cube1/prompt_system",
"name": "prompt_system",
"description": "Verbatim system-prompt text.",
"dataType": "sc:Text",
"source": {
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"extract": {
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"description": "Verbatim user-prompt text.",
"dataType": "sc:Text",
"source": {
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"extract": {
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{
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"@id": "cube1/answer",
"name": "answer",
"description": "Reference six-face map. Schema: {faces:{TOP:{patternId,rotation},...}}. (Stored as a JSON-encoded string.)",
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"source": {
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{
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"description": "Cube goal-roll task: 502 records.",
"field": [
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"description": "Stable level id, e.g. C001.",
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"@id": "cube2/answer",
"name": "answer",
"description": "One known-valid roll sequence. Schema: {directions:['N'|'S'|'E'|'W',...]}. (Stored as a JSON-encoded string.)",
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{
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"name": "lamp",
"description": "Mechanical-lamp targeting task: 610 records.",
"field": [
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"name": "sample_id",
"description": "Stable level id, e.g. lamp-000.",
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{
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"extract": {
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],
"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,
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}
|