GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning

In three linesGRASP enables multi-source transfer learning with O(1) memory instead of O(K) by sequentially merging source models. Using parameter-wise gradient alignment and iterative fine-tuning, it achieves 93.5% mean accuracy on continual learning benchmarks (Yearbook, CLEAR-10/100) versus 71.7% for ensembles, while remaining production-deployable.

Summary generated by Claude — human-verified