Structural Perturbation in Large Language Model Representations through Recursive Symbolic Regeneration

TL;DR

This paper introduces a symbolic perturbation method using recursive regeneration to influence large language model representations, enabling controlled variations in attention, lexical diversity, and domain adaptation without retraining.

Contribution

It presents a novel symbolic perturbation technique that modifies model behavior at the symbolic level through recursive regeneration, distinct from traditional fine-tuning.

Findings

Symbolic perturbations affect attention weight distributions and token dependencies.

Recursive regeneration alters long-range token dependencies and topic coherence.

Symbolic modifications enhance domain adaptability and controllability in text generation.

Abstract

Symbolic perturbations offer a novel approach for influencing neural representations without requiring direct modification of model parameters. The recursive regeneration of symbolic structures introduces structured variations in latent embeddings, leading to controlled shifts in attention dynamics and lexical diversity across sequential generations. A comparative analysis with conventional fine-tuning techniques reveals that structural modifications at the symbolic level induce distinct variations in contextual sensitivity while maintaining overall model fluency and coherence. Shifts in attention weight distributions highlight the role of symbolic modifications in adjusting token dependencies, influencing response variability, and refining long-form text generation. Experimental findings suggest that symbolic perturbations can enhance adaptability in domain-specific applications, allowing modifications in model behavior without retraining. Evaluations of semantic drift indicate that recursive regeneration alters long-range token dependencies, affecting topic coherence across extended text sequences. Results from lexical variability assessments further support the conclusion that symbolic-level modifications introduce interpretable variations in generated responses, potentially enabling more controlled stylistic adjustments in automated text generation.