提出游戏世界过渡复杂度评估框架，量化环境预测难度。
Position: Profiling Game Worlds by Transition Complexity
- 用三类指标刻画环境转移规律：单步分支、交互不确定性、时空依赖范围。
- 定义可复现的测量协议与预算，支持跨基准对比。
- 适用于各类游戏和神经游戏引擎，推动成为标准评估指标。
游戏世界建模（GWM）与强化学习（RL）常因缺乏对底层转移预测难度的量化而混淆。本文提出过渡复杂度轮廓（TCP）：一组小型、可复现的度量，通过（i）内在单步分支性、（ii）可观测情况下的交互不确定性与对手影响、（iii）通过标准化探针曲线衡量的时间/空间依赖跨度，刻画环境（或游戏数据集）诱导的转移核。TCP报告包含显式参考分布、协议随机性及版本化测量预算（采样/重采样与固定探针计算），实现跨基准可比性。我们分析常见游戏类型与现代“神经游戏引擎”领域如何填充该评估图景，并呼吁将TCP作为标准基准元数据和GWM与RL论文的必选统计量。
原文摘要 · Abstract (English)
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) intrinsic one-step branching, (ii) interaction-induced uncertainty and opponent influence when observable, and (iii) temporal/spatial dependency span via standardized probe curves. TCP is reported with an explicit reference distribution, protocol stochasticity, and a versioned measurement budget (sampling/resampling and fixed probe compute), enabling comparable numbers across benchmarks. We outline how common game families and modern "neural game engine" domains populate this landscape and call for TCP to become standard benchmark metadata and a required statistic in GWM and RL papers.
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