Phase-Locked Dynamic Sampling and Frequency-Domain Adaptation: Attractor-Guided Inference and Spectral Sparsity in LLMs

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Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-18
DOI
https://doi.org/10.5281/zenodo.22826288
Primary Topic
Speech Recognition and Synthesis
Type
preprint
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preprint

Phase-Locked Dynamic Sampling and Frequency-Domain Adaptation: Attractor-Guided Inference and Spectral Sparsity in LLMs

Yana Shlyakhova
Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
preprint

Phase-Locked Dynamic Sampling and Frequency-Domain Adaptation: Attractor-Guided Inference and Spectral Sparsity in LLMs

Yana Shlyakhova
preprint en

Abstract

Large Language Models (LLMs) frequently suffer from reasoning degradation during complex autoregressive generation and parameter redundancy during fine-tuning. In this paper, we propose two synergistic systems: Phase-Locked Dynamic Sampling (PLDS) and Dynamic F-LoRA (Fourier-LoRA). PLDS acts as an inference controller that models internal hidden state activation trajectories via a non-linear chaotic attractor (Rossler system) and dynamically modulates the sampling temperature T(t) based on the derivative of phase divergence velocity St = |d(delta_phi)/dt|. Empirical evaluations show that PLDS achieves a +2.7% Pass@1 improvement on the GSM8K benchmark (reaching 79.1%). Dynamic F-LoRA provides parameter-efficient fine-tuning in the frequency domain (2D-DCT), guided by a Gumbel-Sigmoid Straight-Through Estimator (STE) gating mechanism. We reveal a critical spectral phenomenon: pruning 75-85% of high-frequency parameters (with an optimal threshold at 80%) via explicit sparsity regularization (lambda_sparsity) functions as an effective low-pass spectral filter, suppressing structural noise and overfitting. "The official PyTorch implementation and benchmark code are publicly available at https://github.com/yana-shlyakhova/plds-f-lora-llm "

Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
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