A Pipeline for Continual Learning Without Catastrophic Forgetting in LLMs (arxiv.org)

Researchers introduce the Holographic Knowledge Manifold (HKM), a four‑phase continual‑learning pipeline that claims to eliminate catastrophic forgetting while keeping memory growth and compute costs low. Using a combination of fractal quantization, probabilistic entanglement, and what the authors call dynamic diffraction chipping, HKM compresses internal knowledge representations ~3× (reported 67% storage savings), supports over 1,020 incremental updates with only ~1% growth per update, and achieves “0%” forgetting on combined WikiText + FB15k experiments (scaled to 2,997 nodes). Benchmarks vs. a GEM baseline show large gains: 3× compression, 53% reduction in training time on consumer GPUs, and projected petabyte‑scale cost, energy, and carbon savings ($92.4M, 21.2% energy, 33% CO2 over five years). Code and data are published for reproducibility. Technically, HKM integrates new knowledge “holographically” so updates are distributed rather than overwriting model weights, enabling continual adaptation without full retraining—a potentially major efficiency win for public LLMs and fine‑tuning workflows. Results are promising but come from specific dataset and node‑scale tests; broader validation on large production LLMs (e.g., Llama‑3, Grok‑4) and real‑world multimodal tasks is still needed. The authors suggest extensions to multimodal fusion and quantum hardware and estimate fine‑tuning cost reductions of 60–80% if those gains generalize.