Synergistic Integration of Linear Logic and Recurrent Neural Networks for Enhanced Sequence Modeling

Abstract

This paper explores the fusion of linear logic and recurrent neural networks (RNNs) to create a novel sequence modeling architecture. The approach leverages the differentiable semantics of linear logic to inform the operation of RNNs, resulting in a system capable of sophisticated manipulation of hidden states. The paper presents the theoretical underpinnings of this Linear Logic Recurrent Neural Network (LLRNN) and discusses its potential to bridge the gap between symbolic reasoning and connectionist models. While preliminary, this work offers a new perspective on neural computation and highlights the utility of linear logic in designing advanced neural network structures