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PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives

Research Multimodal & Generative

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Merged summary

TL;DR - PlayWorld benchmarks video world models using multimodal agents pursuing long-horizon objectives rather than fixed action sequences. Tests across nine models expose persistent weaknesses in spatial consistency and state evolution.

  • Provides 171 objective-driven interactive scenarios.
  • Evaluates geometry consistency, interaction fidelity, and visible and out-of-sight evolution.
  • Also measures basic video quality and action controllability.
  • Agent players enable fairer comparisons when different models require different action sequences to reach the same goal.

Sources (1)

PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives

arXiv cs.CV Kaixin Ding, Xi Chen, Minghong Cai, Zhiyuan Xu, Yiyang Wang, Yuxiang Lu, Junyi Li, Shuyang Chen, Yuan Gao, Xin Tao, Pengfei Wan, Hengshuang Zhao 2026-08-13 arXiv:2608.13552
Public signals Hugging Face upvotes 46
Providers: Hugging Face · Upvotes 46 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-15 14:32:53.000782 UTC

TL;DR - PlayWorld benchmarks video world models using multimodal agents pursuing long-horizon objectives rather than fixed action sequences. Tests across nine models expose persistent weaknesses in spatial consistency and state evolution.

  • Provides 171 objective-driven interactive scenarios.
  • Evaluates geometry consistency, interaction fidelity, and visible and out-of-sight evolution.
  • Also measures basic video quality and action controllability.
  • Agent players enable fairer comparisons when different models require different action sequences to reach the same goal.
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