Memory retention mechanisms play a central role in determining the efficiency of computational architectures designed for processing extended sequences. Conventional methods for token management often impose fixed retention thresholds or rely on uniform attention weight distributions, leading to inefficient memory utilization and premature information loss in extended sequence modeling. Structured Token Retention (STR) introduces a probabilistic selection framework that dynamically adjusts token persistence based on contextual significance, ensuring that computational resources are allocated to semantically relevant elements. Computational Memory Paths (CMP) extend this framework through hierarchical memory allocation, refining retention efficiency through structured reallocation of token embeddings. Comparative assessments against baseline models demonstrate that STR and CMP improve token survival rates across long input sequences while reducing cumulative error propagation across processing layers. Experimental results further indicate reductions in computational overhead, improving inference speed without degrading contextual coherence. Token distribution analyses reveal that structured memory allocation prevents excessive redundancy in attention weight calculations, optimizing information retrieval efficiency in large-scale generative architectures. The integration of STR and CMP into an open-source model illustrates the adaptability of structured memory retention methodologies, highlighting their applicability in generative text processing, long-context comprehension, and scalable sequence modeling.

翻译：记忆保留机制在决定用于处理扩展序列的计算架构效率方面起着核心作用。传统的令牌管理方法通常施加固定的保留阈值或依赖于均匀的注意力权重分布，导致在扩展序列建模中出现低效的内存利用和过早的信息丢失。结构化令牌保留（STR）引入了一种概率选择框架，该框架基于上下文重要性动态调整令牌持久性，确保计算资源分配给语义相关的元素。计算记忆路径（CMP）通过分层内存分配扩展了此框架，通过令牌嵌入的结构化重新分配来优化保留效率。与基线模型的比较评估表明，STR 和 CMP 提高了长输入序列中的令牌存活率，同时减少了处理层间的累积误差传播。实验结果进一步表明计算开销有所减少，在不降低上下文连贯性的情况下提高了推理速度。令牌分布分析表明，结构化内存分配防止了注意力权重计算中的过度冗余，从而优化了大规模生成架构中的信息检索效率。将 STR 和 CMP 集成到开源模型中说明了结构化记忆保留方法的适应性，突显了它们在生成文本处理、长上下文理解和可扩展序列建模中的适用性。