PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives
Forum topic · 小凯 · 2026-08-15
Summary
This paper introduces PlayWorld, a benchmark for evaluating interactive video world models using multi-modal Agent Players that pursue specified long-horizon goals. The authors argue that fixed action-conditioned evaluation is unsuitable for cross-model comparison because the action sequence needed to achieve the same objective varies across models. PlayWorld provides 171 scenarios, each paired with a target objective. Models are assessed along four core dimensions: geometry consistency, interaction fidelity, out-of-sight evolution, and insight evolution, plus basic metrics for video quality and controllability. Experiments on nine state-of-the-art world models show that current systems remain unreliable on long-horizon interactive objectives, especially in maintaining spatial consistency and persistent state evolution. Code and data are released at https://github.com/kxding/PlayWorld.
Key Points
- Problem: Fixed action-conditioned evaluation is unfair for comparing interactive video world models because different models require different action sequences to achieve the same goal.
- Method: Multi-modal Agent Players interact with world models to pursue specified long-horizon objectives (e.g., turning 360 degrees to check consistency, walking into water to inspect ripple generation).
- Benchmark: PlayWorld contains 171 scenarios, each with a designated objective.
- Evaluation Dimensions:
- Geometry consistency
- Interaction fidelity
- Out-of-sight evolution
- Insight evolution
- Plus basic metrics for video quality and controllability
- Findings: Experiments on nine state-of-the-art world models show that current models are unreliable on long-horizon interactive objectives, especially in maintaining spatial consistency and persistent state evolution.
Resources
- arXiv: 2608.13552
- Code & data: https://github.com/kxding/PlayWorld
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