Datasets:

SimVer-ano
/

simverse2026

⚠️ Anonymized for double-blind review. Author, organization, contact, citation, and repository fields are placeholders until the review concludes.

This datasheet follows the structure proposed by Gebru et al. (2018), Datasheets for Datasets (arXiv:1803.09010). It documents the SimVerse multimodal-LLM benchmark dataset.

1. Motivation

For what purpose was the dataset created?

SimVerse was created to evaluate multimodal large language models (LLMs) on interactive simulation puzzles — tasks that combine visual perception with multi-step reasoning under physical or geometric constraints. Existing multimodal benchmarks emphasize either single-step visual question answering or long-form free-text generation; SimVerse fills a gap by requiring structured, programmatically-verifiable outputs (placements, action sequences, command scripts) over five disjoint puzzle types that share a uniform prompt skeleton and output contract for cross-task comparability.

Who created the dataset and on behalf of which entity?

Anonymized for double-blind review. To be filled in post-acceptance.

Who funded the creation of the dataset?

Anonymized for double-blind review.

Any other comments?

The dataset is paired with an open-source code release (linked from the dataset card after the review period) that includes the generators, prompt builders, parsers, validators, and runner scripts for each task.

2. Composition

What do the instances that comprise the dataset represent?

Each instance is one puzzle level drawn from one of five tasks. A level bundles:

A natural-language prompt (system + user) the benchmark presents to a model
One or more visual assets (images, or in cutrope a short video)
Structured fields describing the level's parameters (board size, segment lengths, target coordinates, etc.)
A reference solution under a task-specific schema

Config	Task	Records	Modality	Output schema
`voi`	Text-VOI shape placement (XOR rasterization)	600	target image + per-shape images	`{placements:[{shape, angle, vertex, grid}]}`
`cube1`	Cube reconstruction (six-face map)	502	blank cross-net + path-imprint	`{faces:{TOP,BOTTOM,FRONT,BACK,LEFT,RIGHT}}`
`cube2`	Cube goal-roll (top-face directions)	502	initial cross-net + target image	`{directions:["N","S","E","W"]}`
`lamp`	Mechanical-arm lamp targeting	610	rendered workspace image	`{actions:[{joint, angle}]}`
`cutrope`	Cut the Rope video → command script	272	short MP4 gameplay clip	`{commands, reason, confidence}`

Total: 2,486 records.

How many instances are there in total?

2,486 records, broken down above.

Does the dataset contain all possible instances or is it a sample?

Each task has a programmatic generator that can produce arbitrarily many puzzles. The shipped 2,486 records are a curated subset sampled to span the difficulty distribution each generator's design parameters allow. The generators are publicly released so users can produce additional instances for held-out evaluation if they wish.

What data does each instance consist of?

Per-record JSON with fields including:

__sample_id__ — stable level id
prompt.system — verbatim system-prompt text shown to the model
prompt.user — verbatim user-prompt text (filled-in 9-section skeleton)
answer — reference solution under the task-specific schema (see table above)
task-native structured fields (e.g. arm.segments, inventory, roll_sequence)
images_relative_to_config / video_relative_to_config — paths to media assets

Media files (PNG images, MP4 videos) live in sibling directories referenced by the JSON.

Is there a label or target associated with each instance?

Yes. The answer field carries a reference solution. For closed-form tasks (VOI, cube1, lamp) the answer is uniquely determined; for open-ended tasks (cube2, cutrope) the answer is one of many valid solutions and validators score by simulation outcome rather than string equality.

Is any information missing from individual instances?

cube1 deliberately includes ? sentinel patterns when puzzle constraints under-determine a face. This is documented behavior, not missing data.

Are relationships between individual instances made explicit?

Each task's records are independent (no cross-record relationships). Records within a task share visual style, output schema, and prompt skeleton, but are otherwise independent puzzles.

Are there recommended data splits?

The dataset is held-out evaluation only — there is no train split. The HuggingFace YAML frontmatter declares a single test split per config.

Are there any errors, sources of noise, or redundancies in the dataset?

Records are programmatically validated by their respective engines before shipping, so every shipped record is provably solvable. Two known minor caveats:

cube1's ? sentinel pattern (intentional, documented).
For open-ended tasks, the embedded reference solution is one of many valid solutions; downstream tooling that scores via reference equality rather than simulation will systematically miss credit for divergent strategies.

Is the dataset self-contained, or does it link to or otherwise rely on external resources?

The dataset is self-contained: every record carries the literal prompt, the reference answer, and either embedded paths to bundled media (images / videos) or all the structured fields needed to reproduce the task. No external API access, no external dataset dependencies, no licenced external content.

cutrope videos are derived from the open-source yell0wsuit/cuttherope-h5dx port (MIT-licensed); the generated MP4 files are shipped within this dataset.

Does the dataset contain data that might be considered confidential?

No. Fully synthetic; no proprietary or confidential content.

Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?

No. All visual content is abstract puzzle imagery (geometric shapes, cube nets, mechanical-arm renderings, gameplay clips of a children's puzzle game).

Does the dataset relate to people?

No.

Does the dataset identify any subpopulations?

No (the dataset does not contain people).

Is it possible to identify individuals, either directly or indirectly?

No.

Does the dataset contain data that might be considered sensitive?

No.

Any other comments?

None.

3. Collection Process

How was the data associated with each instance acquired?

All five tasks use deterministic programmatic generators (Python and JavaScript). Generators produce a level definition + a reference solution simultaneously, so the answer is not annotated post-hoc but co-produced with the puzzle.

VOI: a polygon-rasterization XOR generator chooses base shapes, picks angles and vertices, and verifies the XOR-union matches a randomly-seeded target.
cube1 / cube2: a Python cube-state simulator records roll sequences and bottom-face imprints.
lamp: a forward-kinematics generator samples joint angles, places obstacles to constrain feasible solutions, and checks the bulb-target distance against the light radius.
cutrope: built on top of the open-source yell0wsuit/cuttherope-h5dx HTML5 port; the SimVerse code records deterministic gameplay clips and authors matching command scripts.

Visual rendering is done by each task's frontend (HTML/JS) or a headless renderer.

What mechanisms or procedures were used to collect the data?

Generators were run as one-off scripts on author-provisioned commodity hardware. No human annotators, no crowdsourcing, no scraping. Determinism was enforced by fixed random seeds.

If the dataset is a sample from a larger set, what was the sampling strategy?

For each task, generators sweep difficulty parameters (move count, segment count, distractor count) and the shipped records are filtered to span the parameter ranges roughly uniformly within hand-tuned tier definitions. For example, cube1 records are bucketed into 5 difficulty tiers of ~100 records each.

Who was involved in the data collection process and how were they compensated?

The dataset's authors. No external contributors, no compensated annotators (anonymized for review).

Over what timeframe was the data collected?

The shipped subset was generated and curated during 2025–2026.

Were any ethical review processes conducted?

Not applicable — the dataset is fully synthetic and contains no human-subject data.

4. Preprocessing / cleaning / labeling

Was any preprocessing/cleaning/labeling of the data done?

Yes:

Schema migration (migrate_dataset.py per task): legacy task-native answer fields were lifted into the unified v1 answer envelope (e.g. lamp's flat [-60, -135, ...] array → {actions:[{joint, angle}]}).
Prompt embedding (populate_prompts.py per task): the literal system + user prompt strings the benchmark presents are embedded into each record so HF downloads need no auxiliary code.
For cutrope, an additional build_data.py step precomputes per-level prompt_level metadata (object counts, canvas dimensions) from the source level files.

Both transformations are idempotent and reproducible from the accompanying code.

Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?

Yes — pre-migration formats are preserved in each record under legacy_answer (and for cutrope the original reference_solution string is kept alongside the v1 answer). These are scheduled for removal one release cycle after v1.0.

Is the software used to preprocess/clean/label the instances available?

Yes. The full preprocessing pipeline is open source in the accompanying repository (linked from the dataset card, post-acceptance).

5. Uses

Has the dataset been used for any tasks already?

It is being used for benchmark experiments described in the accompanying paper (anonymized).

Is there a repository that links to any or all papers or systems that use the dataset?

To be filled in post-acceptance.

What (other) tasks could the dataset be used for?

Fine-grained ablations on visual chain-of-thought prompting strategies
Studying cross-task transfer in multimodal LLMs
Diagnostic evaluation of spatial reasoning, multi-step planning, or video understanding in isolation
Probing whether a model's prompt-following improves with the FINAL_JSON output contract

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?

Synthetic visual style per task: models that overfit to specific render styles may show inflated SimVerse scores that do not transfer to real-world spatial-reasoning tasks. Users should not interpret SimVerse performance as a real-world capability predictor.
Open-ended scoring: cube2 and cutrope require simulation-based scoring; downstream uses that apply naive string-equality scoring will systematically under-credit divergent valid strategies.
English-only prompts: the dataset does not test multilingual reasoning.
No train split: do not use SimVerse for training; it is intended as held-out evaluation.

Are there tasks for which the dataset should not be used?

Should NOT be used as training data for production multimodal LLMs.
Should NOT be used as a stand-in for real-world spatial reasoning capability assessment.
Should NOT be used to evaluate models on languages other than English.

6. Distribution

Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created?

Yes. The dataset is publicly distributed.

How will the dataset will be distributed?

Via Hugging Face Hub as a Dataset repository under the Datasets API (one HuggingFace dataset, five configs). The dataset can also be downloaded directly with huggingface-cli download or via direct URLs.

When will the dataset be distributed?

The dataset will be made publicly available before the camera-ready deadline of the corresponding NeurIPS 2026 submission, in accordance with NeurIPS Datasets & Benchmarks track requirements.

Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)?

Yes — MIT License. Full license text in LICENSE at the dataset root.

Have any third parties imposed IP-based or other restrictions on the data associated with the instances?

The cutrope subset is built on top of the yell0wsuit/cuttherope-h5dx HTML5 port, which is itself MIT-licensed. The original Cut the Rope game IP is owned by ZeptoLab; the upstream port and SimVerse's derived gameplay clips are non-commercial educational/research uses consistent with the upstream MIT license.

Do any export controls or other regulatory restrictions apply to the dataset or to individual instances?

None known.

7. Maintenance

Who will be supporting/hosting/maintaining the dataset?

To be filled in post-acceptance. During review, the dataset is hosted as an anonymous account on Hugging Face Hub.

How can the owner/curator/manager of the dataset be contacted?

Anonymized for review. To be filled in post-acceptance.

Is there an erratum?

Not at this version. Errata, if any, will be tracked via the dataset's commit history on the Hugging Face Hub.

Will the dataset be updated?

The dataset uses semantic versioning. Patch updates fix data errors; minor updates add levels or fields; major updates change schema (with one release cycle of legacy_* field overlap). Updates are pushed as new tagged commits on Hugging Face.

If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances?

Not applicable — no person data.

Will older versions of the dataset continue to be supported/hosted/maintained?

Yes. Hugging Face's commit history preserves all historical versions; users can pin to a specific revision via the revision= argument of datasets.load_dataset(...).

If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?

Yes. The accompanying open-source repository (linked post-acceptance) includes the full generator pipeline; contributors can fork, generate additional puzzles, and submit pull requests or publish derivative datasets.

License

MIT — see LICENSE at the dataset root.