全息知识流形：一种实现大语言模型持续学习中无灾难性遗忘的新型流程
Holographic Knowledge Manifolds: A Novel Pipeline for Continual Learning Without Catastrophic Forgetting in Large Language Models
AI总结：
全息知识流形通过分形量化等技术实现大语言模型持续学习中零灾难性遗忘，提升知识压缩效率并降低训练成本。
摘要：
我们引入了全息知识流形（HKM），一种四阶段流程，能够在AI知识表示中实现零灾难性遗忘，同时保持最小的记忆增长和高效率。利用分形量化、概率纠缠和动态衍射切割，HKM通过3倍压缩知识子基质，实现100%的全息整合，并支持超过1,020次更新，每次增量仅增长1%。在结合WikiText和FB15k数据集（扩展到2,997个节点）的实验中，我们展示了行业领先的性能：0%遗忘（超越GEM基线无限改进），3倍压缩，以及在消费级GPU硬件上训练时间减少53%。假设成本分析预测在PB规模下5年内节省9240万美元，能耗降低21.2%，碳足迹降低33%。这项工作假设为公共大语言模型（LLMs）带来了一种范式转变，使
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
