提出新型知识管理框架，让大模型持续学习不遗忘且内存增长极低。
Holographic Knowledge Manifolds: A Novel Pipeline for Continual Learning Without Catastrophic Forgetting in Large Language Models
- 用分形量化与全息整合压缩知识，实现3倍压缩和67%存储节省。
- 支持超1000次更新，每次仅增长1%，训练时间减少53%。
- 适合需长期演进的大模型应用，如智能客服、通用AI助手。
我们提出全息知识流形（HKM）——一种四阶段流程，可在保持极低内存增长与高效率的前提下，实现人工智能知识表示中零灾难性遗忘。通过分形量化、概率纠缠与动态衍射切片技术，HKM将知识底座压缩3倍，节省67%存储空间，实现100%全息集成，并支持超过1,020次更新，每次增量仅增加1%内存。在合并的WikiText与FB15k数据集（扩展至2,997个节点）上实验表明：遗忘率0%（远超GEM基线），压缩比3倍，消费级显卡上训练时间减少53%。假设性成本分析显示，在千兆字节规模下五年可节省9240万美元，能耗降低21.2%，碳足迹下降33%。该工作预示公共大模型的新范式，实现无需重训的“永恒”适应。未来拓展至多模态融合与量子硬件，或使Llama-3、Grok-4等模型微调成本降低60%-80%。代码、数据集与完整结果公开可用，支持复现。
原文摘要 · Abstract (English)
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.
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