While direct observation remains the most effective way to uncover astronomical phenomena, computationally heavy AI models now automate many data-intensive tasks. To maximize these tools while preserving physical intuition, we advocate for computationally-driven knowledge discovery—a growing paradigm combining human scientific vision with modern AI. We illustrate this framework using Constrained Diffusion Decomposition (CDD), which offers multi-scale insights into high dynamical range astronomical data, and Adjacent Correlation Analysis (ACA), which analyzes local variations to uncover physical dependencies obscured by standard global analyses. These methods are both tools for direct data visualization, and a foundation for downstream AI-based analysis. We demonstrate that this transparent approach provides unique insights into complex density structures within galaxies and the interstellar medium (ISM) that remain unachievable with the most advanced, opaque AI models. Finally, as the widespread use of large language models demands a fundamental shift in evaluating scientific productivity, human-guided discovery models offer a necessary and meaningful path forward.
