立场论文：通过过渡复杂度刻画游戏世界

AI 摘要

这篇立场论文提出过渡复杂度剖面（TCP），用于量化游戏环境或数据集的过渡预测难度，包括单步分支、交互不确定性和依赖跨度。作者认为当前GWM和RL研究缺乏此类测量，呼吁将TCP作为标准基准元数据和必报统计量，以促进跨基准的可比性。

正文节选

Position: Profiling Game Worlds by Transition Complexity Abstract Game world modeling (GWM) and reinforcement learning (RL) are often confounded because research papers rarely quantify how difficult the underlying transition prediction problem is at the declared interface (pixels/tokens/latents with finite history). We propose the Transition Complexity Profile (TCP): a small, reproducible set of metrics that characterizes an environment’s (or gameplay dataset’s) induced transition kernel by (i)

发布时间：2026-08-21 12:00

抓取时间：2026-08-20 12:13

来源机构：arXiv