[R] K-Splanifolds: Advancing General Purpose Regression with Linear-Time Parametric Spline Manifolds
New research shows LLMs create geometric structures, and this algorithm stores them 10x more efficiently.
Deep Dive
Independent researcher 1ncehost developed K-Splanifolds, a linear-time parametric spline manifold algorithm. It directly encodes geometric representations found in LLMs, unlike standard Multi-Layer Perceptrons (MLPs). Experiments show it can create similar representations using just 1/10th the memory. This positions K-Splanifolds as a potential, more efficient replacement for the MLP decoders within large language models, aiming to reduce model size and computational cost.
Why It Matters
Could dramatically shrink LLM file sizes and inference costs, making powerful AI models more accessible and efficient to run.