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University of California
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Looping discrete embeddings with continuous hidden states enables near-perfect accuracy in multi-hop reasoning with fewer training steps than traditional methods.
Forget scaling laws – the real bottleneck in associative memory isn't storage, it's retrieval: forcing a single "winner" costs you a logarithmic factor in capacity compared to allowing a ranked list.
