Every idea extracted from recent arXiv mathematics papers — verified and unverified. Click an idea to open its full card; badges show the empirical verdict.
Replace the usual JL dimension choice with a task-specific dimension budget based on the paper's asymptotic ranking law. For retrieval, nearest-neighbor search, or compressed attention keys, choose m from a target Kendall correlation and automatically expand the projection when measured ranking quality falls below the target.
Paper: Exact Limits of Random Projections for Preserving Geometry: Distance Recovery, Nearest-Neighbor Rankings, and Covariance Shape in Gaussian ModelsarXiv:2609.02155
Prune redundant attention heads using separate similarity scores for sink behavior and content routing. Two heads are considered safely redundant only when their normalized content compositions are close in Aitchison distance and their sink-mass trajectories are also close, avoiding pruning decisions dominated by a shared sink token.
Replace a memoryless clipped recurrent output with a clipped observable plus a latent retained overshoot. The network exposes only a bounded output, but stores a fraction of the amount that would have exceeded the bound and feeds it into the next hidden-state update, allowing the model to represent persistent post-saturation effects without making the visible output unstable.
Replace fixed PCA-rank selection in a hidden layer with a renormalization-group-inspired gate over covariance eigenvalue bands. The gate retains modes whose effective quartic interaction remains unstable or strongly scale-dependent, while pruning bands that flow toward the Gaussian noise fixed point. Unlike top-eigenvalue truncation, this is designed for extensive-rank signal distributed throughout the bulk spectrum.
Choose the retained context length by balancing the statistical complexity of adding lag j against the squared prediction bias from discarding it. Unlike a fixed context window, the rule uses both the remaining-horizon spectrum and the estimated tail energy, allowing a model to expand or shrink its memory online.