Reliable estimation of treatment effects from observational data is crucial in fields like medicine, yet challenging when the unconfoundedness assumption is violated. We leverage arbitrary (potentially high-dimensional) instruments to estimate bounds on the conditional average treatment effect (CATE). Our contributions are three-fold: (1) We propose a novel approach for partial identification by mapping instruments into a discrete representation space that yields valid CATE bounds, essential for reliable decision-making. (2) We derive a two-step procedure that learns tight bounds via neural partitioning of the latent instrument space, thereby avoiding instability from numerical approximations or adversarial training and reducing finite-sample variance. (3) We provide theoretical guarantees for valid bounds with reduced variance and demonstrate effectiveness through extensive experiments. Overall, our method offers a new avenue for practitioners to exploit high-dimensional instruments (e.g., in Mendelian randomization).