English

WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models

Computer Vision and Pattern Recognition 2026-06-30 v1 Artificial Intelligence

Abstract

Despite rapid progress in interactive world models (IWMs), existing benchmarks evaluate action following only at trajectory level and ignore memory and interaction physics. We introduce WorldRoamBench, an open-world benchmark for long-horizon stability across four dimensions, each with tailored innovations: (i) Action: per-frame action metric bypassing cross-model semantic scale disparity and exposing failures hidden by trajectory; (ii) Vision: segment-based drift metric capturing non-monotonic mid-sequence collapse missed by start-vs-end comparisons; (iii) Physics: controllability-gated evaluation over mechanics, optics, and 3D consistency, scoring plausibility under faithful action execution; (iv) Memory: action-decoupled protocol evaluating scene memory via transition-localized 3D point-cloud reconstruction and subject memory via tracking-plus-VLM reasoning. The benchmark comprises 600+ test cases across Nature, Urban, and Indoor scenes in first/third-person views with WASD 10-60s continuous interaction. Evaluating 10+ open/closed-source models reveals none reliably satisfies all dimensions; even the best achieves only moderate scores. Advances on WorldRoamBench are steps toward IWMs that are stable, physically grounded, memory-faithful, and deployable in real-world applications.

Keywords

Cite

@article{arxiv.2606.31672,
  title  = {WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models},
  author = {Ting-Bing Xu and Jiacheng Sui and Zhe Gao and Kewei Shi and Wenjin Yang and Zhicheng Liu and Zhaoxu Sun and Mingchao Sun and Hongyu Pan and Fan Jiang and Mu Xu and Qi Fan and Yong Li and Baoquan Chen},
  journal= {arXiv preprint arXiv:2606.31672},
  year   = {2026}
}