Position: Profiling Game Worlds by Transition Complexity

Researchers propose the Transition Complexity Profile (TCP), a set of metrics to quantify the difficulty of transition prediction in game world modeling and reinforcement learning, aiming to standardize benchmark comparisons. The framework characterizes environments by one-step branching, interaction-induced uncertainty, and temporal/spatial dependency span, with explicit reference distributions and versioned measurement budgets.

arXiv:2608.18079v1 Announce Type: new 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 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.