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In the evolving landscape of computational and data-driven materials engineering, embedding spaces serve as foundational representations that encode material properties, structures, and behaviors into vectorial forms amenable to machine learning workflows. These spaces facilitate high-throughput screening, inverse design, and autonomous discovery by bridging atomic-scale simulations with macroscopic predictions. However, inherent directional biases—termed latent anisotropy—emerge from the interplay of data modalities, architectural choices in neural networks, and inference dynamics, potentially skewing discovery pathways toward certain material classes or property regimes. This conceptual manuscript identifies a critical gap in understanding how such biases propagate through materials informatics pipelines, influencing the epistemic reliability of AI-assisted materials research. We introduce the Anisotropic Representation Cascade (ARC) framework, which conceptualizes embedding spaces as multi-layered systems where directional preferences arise from representation encoding, propagation through graph-based architectures, and feedback in closed-loop systems. By integrating insights from uncertainty quantification and multimodal data fusion, ARC elucidates trade-offs in computational steering logics that balance exploration breadth with directional fidelity. Implications extend to enhancing robustness in foundation models for materials science, fostering more equitable navigation of chemical spaces, and informing infrastructure designs that mitigate bias amplification in simulation-experiment couplings. This work underscores the need for interpretive tools in data-driven paradigms to ensure unbiased acceleration of materials innovation.