Contextual Memory Reweaving in Large Language Models Using Layered Latent State Reconstruction

TL;DR

This paper introduces a layered latent state reconstruction framework to improve memory retention in large language models, enhancing coherence and factual consistency in extended sequences without external memory modules.

Contribution

It presents a novel Contextual Memory Reweaving approach that systematically integrates multi-layer latent states to bolster long-term memory in language models.

Findings

Improved recall accuracy for long sequences

Enhanced retention of rare tokens and numerical reasoning

Maintained computational efficiency with added processing

Abstract

Memory retention challenges in deep neural architectures have ongoing limitations in the ability to process and recall extended contextual information. Token dependencies degrade as sequence length increases, leading to a decline in coherence and factual consistency across longer outputs. A structured approach is introduced to mitigate this issue through the reweaving of latent states captured at different processing layers, reinforcing token representations over extended sequences. The proposed Contextual Memory Reweaving framework incorporates a Layered Latent State Reconstruction mechanism to systematically integrate past contextual embeddings without introducing external memory modules. Experimental results demonstrate improvements in recall accuracy across a range of sequence lengths, with notable gains in the retention of rarely occurring tokens and numerical reasoning consistency. Further analysis of computational efficiency indicates that the additional processing overhead remains within acceptable thresholds, enabling scalability across different model sizes. Evaluations in long-form text generation and ambiguous query resolution highlight the capacity of memory reweaving to enhance continuity and reduce inconsistencies over extended outputs. Attention weight distributions reveal more structured allocation patterns, suggesting that reweaved latent states contribute to improved contextual awareness. The findings establish a framework for refining memory retention mechanisms in language models, addressing long-standing challenges in handling complex, multi-step reasoning tasks.