--- pretty_name: PlayWorld Benchmark language: - en task_categories: - visual-question-answering size_categories: - n<1K tags: - world-model - video-generation - interactive-agents - benchmark - long-horizon-evaluation configs: - config_name: gc data_files: gc/data.json - config_name: if data_files: if/data.json - config_name: insight data_files: insight/data.json - config_name: outsight data_files: outsight/data.json --- # PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives Anonymous benchmark release for double-blind review. Author, affiliation, and identity-bearing links are intentionally omitted. [**Project**](https://playworld-2026.github.io/project/PlayWorld/) | [**Code**](../code/) Video world models generate future states from an initial observation and user actions. Comparing interactive models fairly is difficult because the actions required to reach the same long-horizon objective can vary substantially across models. PlayWorld evaluates world models from the perspective of a human player. Each case provides an initial world, a scene-grounded long-horizon objective, a human-annotated basic action sequence, and a sample-specific VQA rubric. During evaluation, a multi-modal **Agent Player** observes generated frames and adapts action execution toward the objective. This dataset provides the initial worlds, objectives, basic action sequences, and rubrics used by that protocol. ## Benchmark Overview PlayWorld contains 171 human-annotated cases and 820+ sample-specific VQA questions across four core world-model capabilities: - **Geometry Consistency (GC):** whether object identity, appearance, relative position, and scene layout remain consistent during camera movement and revisitation. - **Interaction Fidelity (IF):** whether a controlled subject reaches and interacts with the intended region through plausible contact, collision, motion, causal response, and visual feedback. - **Insight Evolution (IE):** whether a continuously observed subject or process exhibits coherent state progression, physically plausible motion, and temporally consistent surroundings. - **Out-of-sight Evolution (OE):** whether a target preserves its identity and continues to evolve plausibly while outside the field of view, with a causally consistent state when revealed again. The questions cover four broad evidence families: identity and appearance, physics and dynamics, temporal evolution, and spatial and trajectory reasoning. Rubrics are tailored to each scene so that only observable and applicable criteria are evaluated. ## Dataset Composition | Config | Evaluation dimension | Source IDs | Cases | | --- | --- | --- | ---: | | `gc` | Geometry Consistency | `GCxxx` | 48 | | `if` | Interaction Fidelity | `IFxxx` | 50 | | `insight` | Insight Evolution | `OExxx` | 30 | | `outsight` | Out-of-sight Evolution | `OExxx` | 43 | | **Total** | | | **171** | Insight and out-of-sight cases retain their original `OExxx` task IDs and image filenames. IDs are not renumbered after the two evaluation settings are placed in separate configs. Initial worlds cover natural, urban, indoor, and imaginative environments, with human, animal, vehicle, object, and scene-level subjects. Images were selected for clear, visually verifiable structures and use first- or third-person views according to the target capability. ## Data Format ```text datasuite/ ├── README.md ├── gc/ │ ├── data.json │ └── images/ ├── if/ │ ├── data.json │ └── images/ ├── insight/ │ ├── data.json │ └── images/ └── outsight/ ├── data.json └── images/ ``` Each `data.json` is an array of evaluation cases. A case contains: - `task_id`, `source_task_id`, `category`, and `suite_split`; - `image_path`, `image_caption`, and `perspective` for the initial world; - `prompt` for the scene-grounded long-horizon objective; - `action`, `action_sequence`, and `action_sequence_steps` for the annotated basic action reference; - `questions`, including the evaluation category, weight, question text, and scoring notes; - dimension- or case-specific evaluation policies where applicable. ## Download This folder is the complete local benchmark used by the companion code release. To obtain another copy from a future anonymous dataset host, download it into the code repository's `data/` directory: ```bash hf download \ --repo-type dataset \ --local-dir data ``` If access is restricted, request access on the dataset page and authenticate first with `hf auth login`. Individual configs can also be loaded with `datasets`: ```python from datasets import load_dataset gc = load_dataset("", "gc", split="train") interaction = load_dataset("", "if", split="train") insight = load_dataset("", "insight", split="train") outsight = load_dataset("", "outsight", split="train") ``` Download the complete repository when running PlayWorld so that every relative `image_path` resolves from the same dataset root. ## Use with PlayWorld Keep the code and dataset as separate directories: ```text workspace/ ├── PlayWorld/ # code repository └── data/ # this dataset ``` Install the evaluation code from the sibling [`../code/`](../code/) directory, then point the Agent Player and Rubric Verifier to the selected `data.json` and its corresponding initial image. For example, the VQA evaluation entry point uses: ```bash GEMINI_API_KEY="xxx" \ DATASET="$PWD/data/gc/data.json" \ TASK_ID=GC002 \ VIDEO="$PWD/outputs/genie3/GC002/GC002_full_process.mp4" \ REFERENCE_IMAGE="$PWD/data/gc/images/GC002.jpg" \ OUTPUT="$PWD/evaluation/genie3/GC002_score.json" \ ./run_vqa_score.sh ``` See the code repository for Agent Player integration, world-model adapters, and the complete inference and evaluation protocol. ## License Initial images originate from multiple sources, including Pexels and web image search results. Dataset users must follow the source-specific license and redistribution restrictions applicable to each asset. A single permissive license must not be assumed to cover every image. ## Citation For double-blind review, cite the accompanying anonymous manuscript: ```bibtex @article{playworld_anonymous, title = {PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives}, author = {Anonymous Authors}, year = {2026} } ```