Context-Preserving Tensorial Reconfiguration in Large Language Model Training

TL;DR

This paper introduces Context-Preserving Tensorial Reconfiguration (CPTR), a novel method for reorganizing neural network weights to improve long-range dependency handling in language models with enhanced efficiency and stability.

Contribution

The paper presents CPTR, a new tensor reorganization technique that improves long-range context integration in language models without adding significant computational complexity.

Findings

CPTR reduces perplexity on long-sequence tasks.

Models with CPTR show improved recall accuracy.

Enhanced models exhibit greater computational efficiency and stability.

Abstract

Handling long-range dependencies in neural architectures has remained a persistent challenge due to computational limitations and inefficient contextual retention mechanisms. Tensorial operations have provided a foundation for restructuring model representations, yet conventional architectures have struggled to incorporate such techniques without introducing excessive complexity. A novel approach, Context-Preserving Tensorial Reconfiguration (CPTR), enables dynamic reorganization of weight tensors through structured factorization and adaptive contraction, allowing for enhanced contextual integration without substantial computational overhead. Empirical evaluations demonstrate that CPTR improves coherence retention across extended sequences, leading to measurable reductions in perplexity and improved recall accuracy for long-context tasks. Performance comparisons reveal that CPTR-enhanced models exhibit greater computational efficiency and reduced memory consumption while maintaining competitive language generation fluency and accuracy. Gradient stability metrics further validate the improved training efficiency, revealing more controlled variance in weight updates. Comparative studies across baseline and CPTR-enhanced models confirm that tensorial reconfiguration contributes to more stable and computationally efficient language modeling. The findings support the potential of CPTR in refining contemporary neural architectures for tasks requiring long-range contextual understanding and efficient memory utilization.