Optimal L2 Regularization in High-dimensional Continual Linear Regression

This project is a research-centric codebase accompanying a recent ArXiv paper. Its value lies in theoretical insights rather than software utility. A score of 2 reflects its status as a reference implementation for academic reproducibility. While it offers a closed-form expression for generalization loss—a significant feat in high-dimensional statistics—it lacks a software moat, as the findings are public and easily reimplemented. The high fork-to-star ratio (6:0) within 4 days suggests immediate peer interest or internal lab use, indicating high academic velocity. Frontier labs are unlikely to compete directly as they focus on empirical scaling of non-linear transformers rather than linear regression theory, though the underlying principles of optimal regularization are relevant to their internal 'forgetting' research. The project is safe from platform domination but serves a very narrow, specialized niche.