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Autonomous materials engineering has transformed computational and data-driven discovery through self-driving laboratories, Bayesian optimization, and machine learning-guided pipelines that integrate high-throughput experimentation with predictive modeling. These systems excel at accelerating positive-outcome trajectories in materials design, from inorganic synthesis to metal-organic frameworks and functional thin films. Yet an epistemic asymmetry persists: negative knowledge—outcomes from failed reactions, suboptimal parameter spaces, unproductive compositional regions, and non-reproducible pathways—remains systematically suppressed within the archival infrastructures that underpin these ecosystems. This suppression arises not from deliberate omission but from fragmented governance mechanisms that prioritize publication-ready results, siloed data repositories, and optimization objectives indifferent to archival completeness. The present conceptual analysis synthesizes the state of autonomous experimentation, data-driven screening, and FAIR-compliant data stewardship to expose how current pipelines inadvertently amplify positive bias and erode long-term discovery efficiency. We introduce the NeGATE (Negative Epistemic Governance and Archival Transparency Ecosystem) Framework, an original systems architecture that reframes negative knowledge as an active, resonant component of the discovery loop rather than residual noise. NeGATE organizes knowledge flows across four interdependent layers—ingestion, inference, steering, and governance—while embedding computational logics that maintain traceability of suppressed signals. By foregrounding representation–inference interactions and feedback dynamics, the framework reveals infrastructure-level trade-offs that govern epistemic completeness in autonomous materials engineering. Its implications extend to the design of next-generation discovery platforms, where archival governance becomes a core computational primitive rather than a post-hoc administrative concern.