Structured Context Recomposition for Large Language Models Using Probabilistic Layer Realignment

TL;DR

This paper tackles degraded long-range contextual coherence in autoregressive transformers during extended generation. It introduces Structured Context Recomposition (SCR), a probabilistic layer realignment technique that realigns transformer representations across layers via a differentiable weighting function, e.g., $T(h) = W_p \cdot h + \epsilon$ and the recursive update $\hat{h}_l = \alpha_l h_l + (1 - \alpha_l) h_{l-1}$. Empirical results show improved contextual consistency and reduced semantic drift across long sequences, with a moderate increase in runtime but feasible memory overhead compared to memory-augmented or retrieval-based methods. The approach provides an internally coherent alternative to external memory or retrieval, with potential extensions to multi-turn, document-level reasoning and hybrid SCR-plus-retrieval frameworks. This work establishes a foundation for more robust long-range contextual modeling in generative LLMs while maintaining practical resource use.

Abstract

Extended sequence generation often leads to degradation in contextual consistency due to the inability of conventional self-attention mechanisms to effectively retain long-range dependencies. Existing approaches, including memory compression and retrieval-augmented conditioning, introduce computational trade-offs that either increase inference latency or impose additional storage overhead. Structured Context Recomposition (SCR) introduces a probabilistic layer realignment strategy that dynamically adjusts learned representations within transformer layers, ensuring that semantically relevant embeddings persist throughout extended transformations. The proposed method enhances coherence retention through a recursive weighting function that redistributes representational emphasis based on inferred contextual relevance rather than relying on fixed token-level attention scores. Empirical results indicate that probabilistic realignment mitigates abrupt topic shifts and logical inconsistencies, particularly in scenarios where sequences exceed standard attention window constraints. Sequence-level entropy analysis further reveals that SCR moderates representational variability without introducing excessive output regularization, allowing models to sustain generative diversity while preserving contextual alignment. Attention head deviation measurements confirm that hierarchical reweighting contributes to smoother token dependency transitions across transformer layers, reinforcing the stability of multi-turn interactions and document-level reasoning. Computational resource assessments show that while SCR incurs a moderate increase in processing time, memory overhead remains within feasible limits, making it suitable for practical deployment in autoregressive generative applications.

Figures (4)

• Figure 1: Coherence Retention Scores Across Context Shifts
• Figure 2: Inference Latency Across Sequence Lengths
• Figure 3: Token Entropy Variability Across Generated Sequences
• Figure 4: Attention Head Deviation Across Transformer Layers