Architectures for Adaptive Scientific Reasoning Under Uncertainty
Scientific intelligence increasingly depends on systems that reason from interventions rather than merely fit observations. This review synthesizes conceptual foundations from model-based reinforcement learning, active inference, causal inference, information theory, and perturbation biology into a unified architecture-level view of adaptive scientific reasoning under uncertainty.