ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
Kwakkel, Warren E
2 Pith papers cite this work. Polarity classification is still indexing.
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Frontier AI safety policies have a structural coordination gap caused by diffuse benefits and concentrated costs, which can be addressed by adapting precommitment and shared response protocols from other high-risk domains.
citing papers explorer
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No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
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The coordination gap in frontier AI safety policies
Frontier AI safety policies have a structural coordination gap caused by diffuse benefits and concentrated costs, which can be addressed by adapting precommitment and shared response protocols from other high-risk domains.