arXiv:2610.28099v1 Announce Type: new Abstract: Reliable microstructural parameter estimation in diffusion MRI (dMRI) critically depends on the acquisition protocol. Yet, common protocol design criteria may not fully reflect estimator bias, parameter constraints, and nonlinear fitting behavior in complex inverse problems. Here, we introduce Protocol Optimization via Loss Assessing Parameter Recovery (POLAR), an end-to-end framework that searches the continuous acquisition space and directly minimizes multiparameter recovery error through noisy signal simulation and the chosen model fitting. We demonstrate POLAR spanning distinct microstructural environments: the Cylinder-Zeppelin model for white matter, Neurite Exchange Imaging (NEXI) for gray matter, and Cellular Exchange Imaging (CEXI) for permeable cells. Across independent held-out parameter sets, POLAR consistently outperformed model-specific reference designs, reducing RMSE by up to $50\%$ for Cylinder-Zeppelin and $74\%$ for CEXI, while reducing NEXI exchange time bias by $70\%$. These gains across distinct biophysical models, acquisition settings, and reference design strategies demonstrate the benefit of optimizing acquisition protocols through the downstream parameter-estimation problem. POLAR provides a flexible, model-specific and estimator-aware framework for designing dMRI protocols tailored to the microstructural parameters and experimental conditions of interest.