JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference

We introduce JADAI, a unified amortized framework for sequential experiment design and Bayesian inference that learns how to choose experimental settings that shape the observation process and how to reduce posterior uncertainty over the parameters as data accumulates. JADAI trains a policy, a history encoder, and a posterior estimator jointly and achieves superior or competitive performance on adaptive design benchmarks.