Transposition-Enhanced Representation Learning: A Novel Lightweight Architecture Beyond Attention Mechanisms
Abstract In this paper, I introduce a fundamentally new approach to sequence representation learning, utilizing a transposition-based mechanism instead of traditional attention methods. My proposed architecture first encodes input text into vector embeddings and then applies a Transposition Layer, enabling the model to learn inter-token relationships both locally and globally without relying on self-attention. Unlike attention, which processes sequences holistically and often requires heavy computation, my method emphasizes lightweight matrix operations while maintaining rich contextual understanding. Early experiments on sample datasets demonstrate that transposition-enhanced embeddings yield structured, powerful feature spaces, indicating promising directions for efficient and scalable AI model design. ------ 1. Introduction In recent years, attention-based architectures, particularly Transformers, have dominated the field of natur...
