Structural Reformation of Large Language Model Neuron Encapsulation for Divergent Information Aggregation

TL;DR

This paper introduces a modular neuron encapsulation framework for large language models, improving language understanding, generation diversity, and logical consistency by promoting specialized neuron roles within the architecture.

Contribution

It presents a novel structured encapsulation method that enhances language model performance and interpretability through modular neuron organization.

Findings

Improved perplexity scores and lexical variability.

Greater divergence in cross-layer activations indicating specialization.

Reduced redundancy and contradictory outputs in generated text.

Abstract

Structured neuron encapsulation introduces a modular framework that enables more effective aggregation and specialization of information within deep learning architectures. A model modified through this framework demonstrated improved perplexity scores, greater lexical variability, and enhanced consistency in logical reasoning, suggesting that structured parameter distribution contributes to more efficient language representation. Statistical analyses of generated text highlighted a wider range of sentence structures and reduced redundancy in token selection, indicating that encapsulation fosters more adaptable language generation. A detailed evaluation of attention weight distributions revealed that the experimental model exhibited greater divergence in cross-layer activations, supporting the hypothesis that encapsulated neurons assume specialized processing roles. Logical consistency assessments further demonstrated that modular architectures mitigate contradictory outputs, reducing internal conflicts in inferred relationships between linguistic constructs. Computational trade-offs were analyzed, with results showing a minor increase in processing overhead, though improvements in parameter efficiency and structured decision-making compensated for the additional complexity. The mathematical formulation of the encapsulation mechanism confirmed that modular aggregation maintains stable convergence properties while promoting distinct functional roles for different neuron clusters.