Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning

TL;DR

This paper introduces Contextual Compression Encoding (CCE), a multi-layered parameter pruning framework that significantly reduces model size and computational demands while preserving performance in large language models.

Contribution

The paper presents a novel multi-stage encoding method for structured pruning of large language models, balancing efficiency gains with retention of linguistic capabilities.

Findings

CCE achieves higher compression ratios in middle layers.

Models compressed with CCE maintain accuracy across tasks.

Significant reductions in energy and inference latency.

Abstract

Context-aware compression techniques have gained increasing attention as model sizes continue to grow, introducing computational bottlenecks that hinder efficient deployment. A structured encoding approach was proposed to selectively eliminate redundant parameter groups while ensuring that representational fidelity was preserved across multiple layers. Contextual Compression Encoding (CCE) introduced a multi-stage encoding mechanism that dynamically restructured parameter distributions, allowing for significant reductions in memory footprint and computational complexity. Experimental evaluations demonstrated that models compressed through CCE retained linguistic expressivity and coherence, maintaining accuracy across a range of text generation and classification tasks. Layer-wise analysis revealed that middle-network layers exhibited higher compression ratios, aligning with the observation that self-attention and feed-forward transformations contained redundancies that could be reorganized without impairing functional capacity. Comparisons against conventional quantization and pruning methods confirmed that CCE provided a more balanced trade-off between efficiency and model retention, achieving reductions in energy consumption and inference latency without requiring extensive retraining. Computational efficiency improvements were particularly evident in deployment scenarios involving resource-constrained environments, where reductions in memory usage enabled more scalable implementations. Further analyses of internal network behavior showed that compressed models exhibited stable activation distributions and adapted dynamically to input variations, reinforcing the viability of structured compression strategies for optimizing large-scale architectures.