A bidirectional component that converts human language inputs directly into high-dimensional semantic vectors and reconstructs human-interpretable outputs from those vectors, bypassing traditional tokenization. Unlike a tokenizer—which segments text into discrete linguistic units—the Neuralator enables concept-native processing by preserving semantic relationships in compressed vector form.
Sometimes spelled: Neurolator
In contrast to BERT’s tokenizer, the LN system uses a Neuralator to encode and decode conceptualinformation without relying on syntactic fragmentation.