The organization of latent knowledge within large-scale models poses unique challenges when addressing overlapping representations and optimizing contextual accuracy. Conceptual redundancies embedded across layers often result in inefficiencies that affect both computational demands and task-specific outcomes. A framework was proposed to restructure these redundancies through advanced clustering techniques and dynamic thresholding, ensuring that critical semantic relationships are preserved while removing unnecessary overlaps. Evaluations revealed improved memory efficiency and faster inference times, alongside better alignment in latent knowledge clusters that enhanced interpretability. Improvements in error rates and adversarial robustness suggest that restructuring redundancies has broader implications for increasing model reliability across diverse applications. Comparative analyses highlighted reductions in resource consumption and notable gains in performance, particularly in translation and summarization tasks. Energy metrics demonstrated significant savings during training phases, further validating the practicality of the approach for real-world deployments. Representational fidelity was also enhanced, with latent space evaluations indicating better cluster alignment and higher semantic consistency. The methodology bridges a key gap in model optimization through directly addressing redundancies at the structural level. Its application opens avenues for scalable, efficient, and contextually aware systems that can adapt to complex, domain-specific tasks without compromising on performance.

翻译：大规模模型中潜在知识的组织在处理重叠表征和优化上下文准确性方面提出了独特挑战。跨层嵌入的概念冗余通常会导致效率低下，影响计算需求和特定任务结果。本文提出了一个框架，通过先进的聚类技术和动态阈值处理来重构这些冗余，确保在消除不必要重叠的同时保留关键语义关系。评估结果表明，该方法提高了内存效率、加快了推理速度，并改善了潜在知识簇的对齐性，从而增强了可解释性。错误率和对抗鲁棒性的改进表明，冗余重构对于提升模型在不同应用中的可靠性具有更广泛的意义。对比分析突显了资源消耗的减少和性能的显著提升，特别是在翻译和摘要任务中。能量指标显示训练阶段实现了显著节能，进一步验证了该方法在实际部署中的实用性。表征保真度也得到了增强，潜在空间评估表明簇对齐更优、语义一致性更高。该方法通过在结构层面直接处理冗余，弥合了模型优化的关键缺口。其应用为开发可扩展、高效且具备上下文感知能力的系统开辟了道路，这些系统能够适应复杂的领域特定任务，同时不牺牲性能。