Game-state variables are near-linearly decodable with architecture-dependent layer profiles

Zhang applies linear and MLP probing to frozen hidden states of IRIS (VQ-VAE + transformer) and DIAMOND (diffusion UNet) world models trained on Atari, finding the two probe types agree closely (delta R^2 <=0.06) [zhang-2026-what-do-world-models-learn-in-rl] IRIS decodes paddle position and score at R^2>0.99 at every layer (a flat profile), while DIAMOND concentrates decodable abstract state at its UNet bottleneck layer [zhang-2026-what-do-world-models-learn-in-rl] Shifting IRIS layer-5 hidden states along the probe-derived direction produces monotonic, strongly correlated output changes (r>=0.96), confirming functional use rather than mere decodability [zhang-2026-what-do-world-models-learn-in-rl] Caveat: a solo-author preprint with no confirmed peer review and unusually clean results (R^2>0.99, r>=0.96) warranting independent replication [zhang-2026-what-do-world-models-learn-in-rl]