README.md

---
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 <anonymous-dataset-repository> \
  --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("<anonymous-dataset-repository>", "gc", split="train")
interaction = load_dataset("<anonymous-dataset-repository>", "if", split="train")
insight = load_dataset("<anonymous-dataset-repository>", "insight", split="train")
outsight = load_dataset("<anonymous-dataset-repository>", "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}
}
```