🤖 AI Summary
To address context memory decay, factual inconsistency, and reduced coherence in large language models (LLMs) when processing long sequences, this paper proposes the Hierarchical Latent State Reweaving (HLSR) mechanism. HLSR systematically strengthens cross-layer long-range dependency modeling—without introducing additional parameters, external memory, or modifications to the attention architecture—by hierarchically reconstructing latent states and reweighting contextual representations in the hidden space. Its core innovations are a lightweight state fusion operator and structured analysis of attention distributions to guide latent state reweaving. Experiments demonstrate significant improvements: +12.7% recall accuracy on long-text generation and ambiguous query tasks, +23.4% retention rate for rare tokens, and −18.1% error rate in numerical reasoning, all with <5% increase in computational overhead.
📝 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.