GraphCast and Aurora share CKA geometry with a depth-wise scale shift

Craig et al. compute Centered Kernel Alignment between AI weather models and find forecast skill correlates with cross-model representational alignment [craig-etal-2026-the-physics-of-ai-weather-models] GraphCast and Aurora represent the atmosphere similarly despite differing architectures and capacity [craig-etal-2026-the-physics-of-ai-weather-models] They propose a "particle description" in which latent variables move under gradient flow toward a minimum of a learned free-energy functional [craig-etal-2026-the-physics-of-ai-weather-models] Consistent with this, processor layers shift from large-spatial-scale changes early to small-spatial-scale changes with depth [craig-etal-2026-the-physics-of-ai-weather-models]