We introduce a novel framework that seamlessly integrates amortized Bayesian inference and active data acquisition, featuring adaptive strategies that can optimize for diverse, user-specified learning objectives at deployment.

We argue that Physics-Informed ML must evolve to meet the unique challenges of biological modeling, and that this evolution represents not a limitation, but a major opportunity, giving rise to Biology-Informed Machine Learning (BIML).

This paper investigates a principle and develops a method for intervening on a single, targeted agent in a multi-agent reinforcement learning to reach a collective objective consisting of the primary task goal and an additional desired outcome.