Contextual adaptation in token embeddings plays a central role in determining how well language models maintain coherence and retain semantic relationships over extended text sequences. Static embeddings often impose constraints on lexical flexibility, leading to suboptimal performance when faced with complex sentence structures or domain-specific terminology shifts. To address this limitation, a structured approach was developed for dynamically reconfiguring token embeddings through continuous geometric transformations, ensuring that representations evolved in response to evolving discourse structures. A manifold-based transformation mechanism was integrated to regulate lexical positioning, allowing embeddings to undergo controlled shifts while preserving linguistic relationships across varying textual contexts. Empirical evaluations demonstrated that embedding reconfiguration contributed to reductions in perplexity, improved lexical coherence, and enhanced sentence-level continuity, particularly in structured and domain-adaptive text generation tasks. Comparative analyses of embedding drift indicated that dynamically restructured representations maintained stronger contextual consistency, reducing misalignment in token dependencies while preserving fluency in language modeling outputs. Computational overhead assessments confirmed that while training complexity increased due to the iterative refinement of embeddings, inference remained efficient, ensuring practical feasibility for real-time generation. Evaluations across multiple datasets further demonstrated that dynamically modulated embeddings exhibited broader lexical diversity, reducing repetitive token patterns and enabling a more adaptable representation learning process.

翻译：词嵌入中的上下文适应在决定语言模型如何在扩展文本序列中保持连贯性和保留语义关系方面起着核心作用。静态嵌入通常对词汇灵活性施加限制，导致在面对复杂句子结构或领域特定术语转换时性能欠佳。为解决这一限制，我们开发了一种通过连续几何变换动态重构词嵌入的结构化方法，确保表示能随着话语结构的演变而演化。我们集成了一种基于流形的变换机制来调控词汇定位，允许嵌入在保持跨不同文本上下文的语言关系的同时，经历受控的偏移。实证评估表明，嵌入重构有助于降低困惑度、提高词汇连贯性并增强句子层面的连续性，特别是在结构化和领域自适应的文本生成任务中。对嵌入漂移的比较分析表明，动态重构的表示保持了更强的上下文一致性，减少了词元依赖关系中的错位，同时保持了语言建模输出的流畅性。计算开销评估证实，尽管由于嵌入的迭代优化导致训练复杂度有所增加，但推理过程仍然高效，确保了实时生成的实际可行性。跨多个数据集的进一步评估表明，动态调制的嵌入展现出更广泛的词汇多样性，减少了重复的词元模式，并实现了更具适应性的表示学习过程。