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The rapid evolution of computational and data-driven materials engineering has transformed materials discovery from traditional trial-and-error approaches to sophisticated AI-integrated pipelines. Within this paradigm, learned embeddings serve as foundational representations that encode complex material properties, structures, and behaviors into latent spaces amenable to machine learning algorithms. However, these embeddings, while powerful for predictive modeling and high-throughput screening, introduce epistemic limits that challenge the fidelity of computational design systems. This manuscript explores the disconnect between representational abstractions and physical reality, emphasizing how embedding-induced biases, dimensionality reductions, and generalization assumptions constrain the reliability of AI-guided materials innovation. We introduce a novel conceptual framework, the Epistemic Representation Cascade (ERC), which dissects the multi-layered interactions between data infrastructures, learning architectures, and discovery workflows to reveal inherent epistemic risks. By integrating insights from materials informatics and representation learning, the ERC highlights feedback mechanisms that amplify or mitigate these limits, offering systems-level guidance for enhancing interpretability and robustness in autonomous design ecosystems. Implications extend to closed-loop experimentation and inverse design, advocating for infrastructure-aware strategies that prioritize epistemic alignment over mere predictive accuracy. This work underscores the need for balanced computational steering in materials AI, fostering more trustworthy pathways for next-generation materials engineering.

In the rapidly evolving field of computational materials engineering, data-driven approaches have transformed the discovery and design of novel materials by leveraging machine learning and high-throughput computations to navigate vast chemical spaces. Traditional methodologies often rely on Euclidean distance metrics to quantify similarities between materials in latent representations, facilitating tasks such as property prediction, inverse design, and autonomous experimentation. However, this assumption overlooks the inherent non-linearities and topological complexities of material spaces, where properties like electronic bandgaps, mechanical strengths, and thermodynamic stabilities emerge from intricate atomic interactions that do not conform to flat geometries. This conceptual gap leads to inefficiencies in representation learning, biased uncertainty quantification, and suboptimal steering in discovery pipelines. Here, we introduce a novel interpretive framework that critiques Euclidean metrics through a manifold-based lens, emphasizing geodesic distances and curvature-aware embeddings to better capture the epistemic structure of materials data. By integrating insights from graph neural networks, multimodal datasets, and closed-loop systems, this framework reveals computational trade-offs in data infrastructures and enhances the interpretability of AI-guided workflows. Implications extend to improved coupling of simulations and experiments, fostering more robust foundation models for materials science and accelerating innovation in energy, electronics, and structural applications without empirical validation.