Meta Additive Model Paper

- A new paper titled “Meta Additive Model” from Xuelin Zhang and colleagues proposes an auto‑weighting technique to make sparse learning models more interpretable and deployable. - The paper is posted on arXiv as arXiv:2604.20111 and describes automatic per‑feature weighting that reportedly improves predictive accuracy while preserving model sparsity. - The work feeds into current interest in interpretable sparse methods and could affect feature‑efficient models across applications. (x.com)