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In the evolving landscape of computational and data-driven materials engineering, the exploration of compositional spaces has become central to accelerating materials discovery. Traditional approaches often assume uniformity in these spaces, treating them as isotropic domains where data points are evenly distributed and equally informative. However, real-world datasets exhibit inherent density gradients, where regions of high data concentration contrast with sparse zones, influencing the reliability of machine learning predictions and high-throughput screening outcomes. This non-uniformity arises from biases in experimental sourcing, computational feasibility constraints, and intrinsic material stability landscapes, leading to epistemic risks in inverse design and autonomous discovery pipelines. To address this conceptual gap, we introduce the Density-Gradient Adaptive Screening (DGAS) Framework, a novel interpretive structure that integrates gradient-aware representation learning with adaptive sampling logics to navigate these heterogeneous spaces. The framework conceptualizes compositional domains as multi-layered manifolds with varying informational densities, incorporating feedback mechanisms between data ingestion, model inference, and discovery steering. By formalizing density gradients as dynamic modulators of uncertainty propagation, DGAS offers systems-level insights into optimizing closed-loop experimentation and multimodal dataset curation. Implications extend to foundation models in materials science, enhancing simulation-experiment coupling and reducing extrapolation errors in underrepresented compositional regimes. This work underscores the need for gradient-centric paradigms in materials informatics, fostering more robust and efficient pathways toward next-generation materials.