Structural Latency Perturbation in Large Language Models Through Recursive State Induction

TL;DR

This paper introduces a recursive state induction method to dynamically reduce inference latency in large language models, maintaining generative quality while improving efficiency and power consumption.

Contribution

It presents a novel structured latency perturbation mechanism based on recursive state induction that reduces inference latency without altering model architecture.

Findings

Latency reduction across various sequence lengths

Maintains token retention and memory utilization

Improves power efficiency during extended text generation

Abstract

Computational efficiency has remained a critical consideration in scaling high-capacity language models, with inference latency and resource consumption presenting significant constraints on real-time applications. The study has introduced a structured latency perturbation mechanism that modifies computational pathways through recursive state induction, enabling dynamic suppression of redundant activations while preserving generative fidelity. A formal mathematical framework has been established to describe recursive perturbations, ensuring that modifications remain adaptive rather than statically imposed. Experiments have demonstrated that applying recursive state adjustments reduces inference latency across varying sequence lengths, with longer text generations benefiting from cumulative efficiency improvements. Comparative evaluations against structured pruning and quantization have indicated that latency gains can be achieved without compromising token retention or memory utilization. The analysis of computational overhead has suggested that selectively suppressing redundant activations contributes to improved power efficiency, particularly in scenarios requiring extended text generation. An assessment of linguistic stability has shown that token-level consistency remains largely intact under controlled perturbation thresholds, reinforcing the viability of structural latency modifications as an alternative to weight-centric optimization techniques. The results have supported the hypothesis that recursive state induction offers an effective method for reducing computational complexity without requiring architectural modifications or external augmentation.