Structural Latency Perturbation in Large Language Models Through Recursive State Induction

• URL: http://arxiv.org/abs/2502.00758v1
• Date: Sun, 02 Feb 2025 11:45:35 GMT
• Title: Structural Latency Perturbation in Large Language Models Through Recursive State Induction
• Authors: Michael Mangrum, Jonathan Pemberton, Benedict Wetherby, Philip Montague,
• Abstract summary: This study introduces a structured latency perturbation mechanism that modifies computational pathways through recursive state induction.<n>Experiments have demonstrated that applying recursion state adjustments reduces inference latency across varying sequence lengths.<n>The analysis of computational overhead has suggested that selectively suppressing activations contributes to improved power efficiency.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Model Hemorrhage and the Robustness Limits of Large Language Models [119.46442117681147]
  Large language models (LLMs) demonstrate strong performance across natural language processing tasks, yet undergo significant performance degradation when modified for deployment. We define this phenomenon as model hemorrhage - performance decline caused by parameter alterations and architectural changes.
  arXiv  Detail & Related papers  (2025-03-31T10:16:03Z)
• Fast Training of Recurrent Neural Networks with Stationary State Feedbacks [48.22082789438538]
  Recurrent neural networks (RNNs) have recently demonstrated strong performance and faster inference than Transformers. We propose a novel method that replaces BPTT with a fixed gradient feedback mechanism.
  arXiv  Detail & Related papers  (2025-03-29T14:45:52Z)
• A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perception [81.30687085692576]
  We introduce an optimization algorithm named as Probabilistic Symbol Perception (PSP), which makes a smooth transition between induction and deduction. Experiments demonstrate the promising results.
  arXiv  Detail & Related papers  (2025-02-18T14:59:54Z)
• Structured Convergence in Large Language Model Representations via Hierarchical Latent Space Folding [0.0]
  Token representations in high-dimensional latent spaces often exhibit redundancy, limiting computational efficiency and reducing structural coherence across model layers. This paper introduces a structured transformation mechanism that enforces a multi-scale organization within learned embeddings. Empirical evaluation demonstrates a reduction in representational variance across layers, contributing to more stable perplexity distributions and enhancing predictive confidence in text generation.
  arXiv  Detail & Related papers  (2025-02-13T04:01:54Z)
• Latent Convergence Modulation in Large Language Models: A Novel Approach to Iterative Contextual Realignment [0.0]
  A structured modulation mechanism was introduced to regulate hidden state transitions. Lattice adjustments contributed to reductions in perplexity fluctuations, entropy variance, and lexical instability.
  arXiv  Detail & Related papers  (2025-02-10T09:46:33Z)
• Contextual Memory Reweaving in Large Language Models Using Layered Latent State Reconstruction [0.0]
  Token dependencies degrade as sequence length increases, leading to a decline in coherence and factual consistency.<n>A structured approach is introduced to mitigate this issue through the reweaving of latent states captured at different processing layers.<n>The proposed Contextual Memory Reweaving framework incorporates a Layered Latent State Reconstruction mechanism.
  arXiv  Detail & Related papers  (2025-02-04T06:25:20Z)
• Contextual Morphogenesis in Large Language Models: A Novel Approach to Self-Organizing Token Representations [0.0]
  contextual morphogenesis establishes a self-organizing mechanism that restructures token boundaries based on learned contextual dependencies.<n> Empirical evaluations demonstrate that dynamically adjusted tokenization contributes to reductions in perplexity while maintaining representational stability.<n> Comparative assessments across different linguistic corpora suggest that adaptive tokenization preserves interpretability while improving alignment with contextual cues.<n>The effectiveness of contextual morphogenesis in refining structural stability and predictive performance highlights its viability as an alternative to traditional tokenization methods.
  arXiv  Detail & Related papers  (2025-02-01T03:50:46Z)
• Context-Preserving Tensorial Reconfiguration in Large Language Model Training [0.0]
  Context-Preservingial Reconfiguration (CPTR) enables dynamic complexity of weight tensors through structured factorization and adaptive contraction.<n> Empirical evaluations demonstrate that CPTR improves coherence retention across extended sequences.<n>Performance comparisons reveal that CPTR-enhanced models exhibit greater computational efficiency and reduced memory consumption.
  arXiv  Detail & Related papers  (2025-02-01T00:55:19Z)
• Structured Context Recomposition for Large Language Models Using Probabilistic Layer Realignment [0.0]
  This paper introduces a probabilistic layer realignment strategy that dynamically adjusts learned representations within transformer layers.<n>It mitigates abrupt topic shifts and logical inconsistencies, particularly in scenarios where sequences exceed standard attention window constraints.<n>While SCR incurs a moderate increase in processing time, memory overhead remains within feasible limits, making it suitable for practical deployment in autoregressive generative applications.
  arXiv  Detail & Related papers  (2025-01-29T12:46:42Z)
• Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis [0.0]
  The organization of latent knowledge within large-scale models poses unique challenges when addressing overlapping representations and optimizing contextual accuracy.<n>A framework was proposed to restructure these redundancies through advanced clustering techniques and dynamic thresholding.<n> Evaluations revealed improved memory efficiency and faster inference times, alongside better alignment in latent knowledge clusters that enhanced interpretability.
  arXiv  Detail & Related papers  (2025-01-23T11:34:04Z)
• HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression [69.36555801766762]
  We propose a hardware-aware tensor decomposition framework, dubbed HEAT, that enables efficient exploration of the exponential space of possible decompositions. We experimentally show that our hardware-aware factorized BERT variants reduce the energy-delay product by 5.7x with less than 1.1% accuracy loss.
  arXiv  Detail & Related papers  (2022-11-30T05:31:45Z)
• Confident Adaptive Language Modeling [95.45272377648773]
  CALM is a framework for dynamically allocating different amounts of compute per input and generation timestep. We demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $times 3$ -- while provably maintaining high performance.
  arXiv  Detail & Related papers  (2022-07-14T17:00:19Z)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)
• Untangling tradeoffs between recurrence and self-attention in neural networks [81.30894993852813]
  We present a formal analysis of how self-attention affects gradient propagation in recurrent networks. We prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies. We propose a relevancy screening mechanism that allows for a scalable use of sparse self-attention with recurrence.
  arXiv  Detail & Related papers  (2020-06-16T19:24:25Z)
• Age-Based Coded Computation for Bias Reduction in Distributed Learning [57.9123881133818]
  Coded computation can be used to speed up distributed learning in the presence of straggling workers. Partial recovery of the gradient vector can further reduce the computation time at each iteration. Estimator bias will be particularly prevalent when the straggling behavior is correlated over time.
  arXiv  Detail & Related papers  (2020-06-02T17:51:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.