Every idea extracted from recent arXiv mathematics papers — verified and unverified. Click an idea to open its full card; badges show the empirical verdict.
Search for a compact symbolic optimizer instead of selecting among fixed AdamW-like formulas. Encode optimizer programs as token sequences, learn a continuous variational representation of those sequences, and use a Gaussian-process Bayesian optimizer to propose promising update rules based on short neural-network training rollouts.
Replace an unconstrained nonlinearity on hyperbolic pairwise similarities with a function from the paper's exact Lorentz–Gram preserver family. The transformed similarity matrix remains realizable as Lorentz inner products of future-directed unit timelike vectors, allowing a network to sharpen or smooth hyperbolic neighborhoods without introducing geometrically impossible pairwise relations.