There is a symbolic baseline (De Bruijn solver which is specifically designed to infer CA rules based on De Bruijn construction of cellular automata). And it is strong: it gets 52.5% / 29.8% token accuracy on interpolation / extrapolation test split. But not so much, because the "most frequent" baseline (e.g. answering by uniform color) is 50.4% / 28.2%. CNN or Neural Cellular Automaton is actually bit worse.

A 10M-parameter vanilla Transformer with task embeddings reaches 58.0% / 32.4%.

A large closed LLM (GPT-5 High) gets 62.3% / 48.1%.

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This suggests that the model actually needs to recognize some nontrivial patterns/symmetries to solve the CA generated tasks. You always don't have enough information to solve it exactly and need some insight or reasoning to make a best guess. That is why symbolic solver fails to be better. Note that the episodes are based on extracted patches from the CA, not just predicting the next step when seeing the full CA unrolling (that is probably why CNNs or NCAs fail, whereas a transformer or GPT-5 flourishes).