Abstract
Recurrent neural architectures such as LSTM and GRU remain widely used in sequence modeling, but they continue to face two core limitations: redundant gate-specific parameters and reduced ability to retain information across long temporal distances. This paper introduces the Quantum-Leap LSTM (QL-LSTM), a recurrent architecture designed to address both challenges through two independent components. The Parameter-Shared Unified Gating mechanism replaces all gate-specific transformations with a single shared weight matrix, reducing parameters by approximately 48 percent while preserving full gating behavior. The Hierarchical Gated Recurrence with Additive Skip Connections component adds a multiplication-free pathway that improves long-range information flow and reduces forget-gate degradation. We evaluate QL-LSTM on sentiment classification using the IMDB dataset with extended document lengths, comparing it to LSTM, GRU, and BiLSTM reference models. QL-LSTM achieves competitive accuracy while using substantially fewer parameters. Although the PSUG and HGR-ASC components are more efficient per time step, the current prototype remains limited by the inherent sequential nature of recurrent models and therefore does not yet yield wall-clock speed improvements without further kernel-level optimization.
Abstract (translated)
循环神经网络架构，如LSTM和GRU，在序列建模中依然广泛使用，但它们仍然面临着两个核心限制：冗余的门特异参数以及在长时间跨度内保持信息的能力减弱。本文介绍了Quantum-Leap LSTM（QL-LSTM），这是一种通过两个独立组件设计来解决上述挑战的循环架构。参数共享统一门控机制将所有门特异转换替换成一个共用权重矩阵，减少了约48%的参数量，同时保留了完整的门控行为。带有加性跳过连接的层次化门控递归组件添加了一个无需乘法运算的路径，该路径改善了长距离信息流动并减轻了忘记门退化的现象。 我们使用IMDB数据集上的情感分类任务，在扩展文档长度的情况下对QL-LSTM进行了评估，并将其与LSTM、GRU和BiLSTM基准模型进行比较。QL-LSTM在参数数量显著减少的同时实现了竞争性的准确度。尽管PSUG（参数共享统一门控机制）和HGR-ASC（带有加性跳过连接的层次化门控递归组件）在每个时间步骤中更加高效，但由于当前原型仍然受到循环模型固有的序列性质限制，在没有进一步内核级优化的情况下，目前还无法实现实际运行速度的提升。
URL
https://arxiv.org/abs/2512.06582
