ArrowSpace successfully processed 100,000-dimensional biomarker data from the Dorothea dataset, scaling to 800 samples in 41-110 seconds with over 600× memory compression. The system achieved 5-22% graph density through parameter sweeps, with TauMode computation being the critical bottleneck at 62% of runtime. Johnson-Lindenstrauss projection and k-NN connectivity emerged as key factors controlling build time and graph density respectively.
A novel 3-SAT solver using spectral matrix decomposition and continuous relaxation achieves polynomial-time complexity (O(N³)) on problems with 10,000+ variables, reaching 137ms execution time by computing principal eigenvectors of constraint covariance matrices and projecting solutions to discrete space via sign-mapping rather than traditional backtracking.