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Finite precision in transformers can drastically alter memory operations, revealing a surprising hierarchy of expressivity based on attention mechanisms.
Recurrent neural networks can exhibit drastically different expressivity based on the arithmetic model used, revealing hidden capabilities tied to quantization methods.