Holographic Knowledge Manifolds: A Novel Pipeline for Continual Learning Without Catastrophic Forgetting in Large Language Models

2025/09/03 by Arndt, Justin
Computer Science · Earth and Planetary Sciences · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Geophysical and Geoelectrical Methods #Machine Learning (cs.LG) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2509.10518

openalex publication_date 2025/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce the Holographic Knowledge Manifold (HKM), a four-phase pipeline that achieves zero catastrophic forgetting in AI knowledge representation while maintaining minimal memory growth and high efficiency. Leveraging fractal quantization, probabilistic entanglement, and dynamic diffraction chipping, HKM compresses knowledge substrates by 3x with 67% storage savings, integrates holographically at 100%, and supports over 1,020 updates with 1% growth per increment. In experiments on combined WikiText and FB15k datasets (scaled to 2,997 nodes), we demonstrate industry-leading performance: 0% forgetting (infinite improvement over GEM baselines), 3x compression, and 53% training time reduction on consumer GPU hardware. Hypothetical cost analyses project 92.4M savings over 5 years at petabyte scale, with 21.2% energy reduction and 33% lower carbon footprint. This work hypothesizes a paradigm shift for public large language models (LLMs), enabling "eternal" adaptation without retraining. Future extensions to multimodal fusion and quantum hardware could further democratize scalable AI, potentially reducing fine-tuning costs by 60-80% for models like Llama-3 or Grok-4. Code, datasets, and full results are publicly available for reproducibility.

Citations

• QLoRA: Efficient Finetuning of Quantized LLMs
• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• FP8 Formats for Deep Learning
• LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
• Carbon Emissions and Large Neural Network Training
• Scaling Laws for Neural Language Models
• Green AI
• Energy and Policy Considerations for Deep Learning in NLP
• Three scenarios for continual learning
• Gradient Episodic Memory for Continual Learning
• Holographic Embeddings of Knowledge Graphs
• Deep Unsupervised Learning using Nonequilibrium Thermodynamics
• Catastrophic Forgetting, Rehearsal and Pseudorehearsal

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• Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference
• FengHuang: Next-Generation Memory Orchestration for AI Inferencing