ENHANCED TRANSFORMER ARCHITECTURE WITH EPISTEMIC ENCODING AND SUB-QUADRATIC ATTENTION FOR IMPROVED VERACITY AND COMPUTATIONAL EFFICIENCY

원문 (중국어)

A computer-implemented transformer architecture for processing natural language input with enhanced computational efficiency and veracity verification is disclosed. The transformer generates enhanced embeddings by augmenting conventional word embeddings with semantic, positional, reliability, domain-specific feature vectors, epistemic encoding for knowledge attributes, and co-occurrence matrix analysis for semantic relationships. The transformer architecture implements selective attention processing using dynamic thresholds to determine token pair processing. Low-scoring token pairs are dropped from further processing, and high-scoring token pairs are passed directly to the output using a token bypass system. The medium-scoring token pairs are processed through the full transformer stack to determine their contextual role. This selective attention approach reduces computational complexity from quadratic to sub-quadratic time. A veracity verification system compares preliminary outputs generated by the transformer stack with a stored corpus of verified information. Semantic distance measurements are used to verify the accuracy of the generated response.