--- license: cc-by-nc-4.0 pretty_name: Object Permanence Benchmark language: - en task_categories: - video-to-video tags: - object-permanence - world-model - video-prediction - benchmark - model-outputs size_categories: - 1K

The **300-question exam** of WROP (World Reasoning with Object Permanence) and **14 video models' answers** to it: our own model **PWM-WROP**, five open-source baselines and eight hosted commercial models. Scores and the human-preference leaderboard live on the project page; this repository holds the questions and the raw generations only. ## Abstract Object permanence is the hallmark of human cognitive priors. Recent studies show that video models, also often referred as world models, have also shown emerged reasoning abilities, making them ideal candidates for building human-like multimodal intelligence. Do video models have emerged object permanence in them? If not, could we train them with a core-cognition inspired dataset? We introduce WROP (World Reasoning with Object Permanence), a data infrastructure of 150 hand-designed cognitive science inspired tasks, divided into six cognitive categories. We build Blender generators that randomize speed, lighting, camera angle, and other nuisance parameters while preserving each task's cognitive structure, yielding 10,000+ samples per task. We release a 1.5M-sample training corpus and a 300-question exam. On this exam we evaluate 14 video models, 4 continuation, 3 reference-to-video, 7 edit, and PWM-WROP, our 16B world model. In a blind pairwise Elo study, PWM-WROP ranks third overall, behind a statistical tie between two commercial reference-to-video systems, and first among continuation models. We release the data, exam, model answers, scores, weights, and PWM, our training stack, a native-PyTorch implementation of the model on AWS Trainium2. ## At a glance | Property | Value | |---|---| | Questions | **300** (150 tasks × 2 samples, `_0000` / `_0001`) | | Models | **14** - 1 ours, 5 open-source, 8 commercial | | Answers | **4,197** mp4 (every model answered all 300; `runway-aleph-v2v` 297) | | Question format | Five files per question, same contract as the training corpus | | Reference clips | 1280 × 720, 24 fps, 60 input frames → 60 target frames | | Size | 199 MB (`eval/`) + 4.4 GB (`results/`) | ## Layout ``` . ├── LICENSE ├── eval/ # the exam — 300 questions, 1,500 files │ └── /_task/ │ ├── _0000/ │ │ ├── input_video.mp4 # the question: 60 frames the model sees │ │ ├── target_video.mp4 # the reference answer: 60 frames that follow │ │ ├── prompt.txt # the continuation instruction │ │ ├── trajectory.npz # per-frame ground truth for every body │ │ └── metadata.json │ └── _0001/ # same five files └── results/ # the answers — 4,197 mp4 ├── pwm-wrop/_task/_0000.mp4 ├── ltx-2.3-dev-v2v/… └── … # 14 model directories ``` ## Usage ```bash huggingface-cli download Hokin/object-permanence-benchmark --repo-type dataset --local-dir ./object-permanence-benchmark ``` Each answer `results//_task/.mp4` is that model's continuation of `eval//_task//input_video.mp4`; the reference answer is the matching `target_video.mp4`. ## License [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — attribution: **Hokin Deng**. Non-commercial use only. For commercial licensing contact hokinxqdeng@gmail.com. ## Citation *Training Object Permanence in World Models* — [arXiv:2609.28654](https://arxiv.org/abs/2609.28654). ```bibtex @misc{zhang2026trainingobjectpermanenceworld, title = {Training Object Permanence in World Models}, author = {Haotian Zhang and Fengyuan Yu and Dezhi Luo and Haoran Sun and Zehong Zhao and Qingying Gao and Yihan Li and Siyuan An and Huayi Qin and Yilan Zhang and Zhengze Jiang and Pinyuan Feng and Renrui Zhang and Ziyu Guo and Letian Wang and Mengyue Yang and Kangfu Mei and Maijunxian Wang and Ran Ji and Vikash Kumar and Freda Shi and Chandra Sripada and Vincent C. Muller and Philip Torr and Alan Yuille and Nikolaus Kriegeskorte and Felix Juefei-Xu and Lvmin Zhang and Jieneng Chen and Yilun Du and Hokin Deng}, year = {2026}, eprint = {2609.28654}, archivePrefix = {arXiv}, primaryClass = {cs.AI}, url = {https://arxiv.org/abs/2609.28654} } ```