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Computational materials engineering has evolved into a data-intensive discipline where high-throughput computation, representation learning, and autonomous discovery systems enable systematic exploration of vast chemical spaces. Central to this evolution is the recognition that model priors—inductive biases, architectural assumptions, and regularization structures embedded in machine learning pipelines—actively reshape the effective searchable materials space rather than merely operating within it. Despite advances in materials informatics, graph neural networks, and closed-loop experimentation, the systemic influence of these priors on screening frontiers remains conceptually underexplored. This article presents the Priors-Adaptive Frontier Reshaping (PAFR) Framework, an original systems-level conceptualization that formalizes how priors modulate data-to-discovery pipelines through layered interactions between representation spaces, inference dynamics, and feedback loops. By integrating insights from multimodal datasets, uncertainty quantification, and simulation–experiment coupling, the framework elucidates computational workflow dynamics and epistemic risk structures that govern algorithmic screening efficiency. The PAFR Framework offers interpretive guidance for designing more robust infrastructures in materials discovery, highlighting trade-offs in prior selection, search space expansion, and steering logics. These insights advance a deeper understanding of representation–inference interactions in data-driven materials engineering.