Abstract
This review aims to conduct a comparative analysis of liquid neural networks (LNNs) and traditional recurrent neural networks (RNNs) and their variants, such as long short-term memory networks (LSTMs) and gated recurrent units (GRUs). The core dimensions of the analysis include model accuracy, memory efficiency, and generalization ability. By systematically reviewing existing research, this paper explores the basic principles, mathematical models, key characteristics, and inherent challenges of these neural network architectures in processing sequential data. Research findings reveal that LNN, as an emerging, biologically inspired, continuous-time dynamic neural network, demonstrates significant potential in handling noisy, non-stationary data, and achieving out-of-distribution (OOD) generalization. Additionally, some LNN variants outperform traditional RNN in terms of parameter efficiency and computational speed. However, RNN remains a cornerstone in sequence modeling due to its mature ecosystem and successful applications across various tasks. This review identifies the commonalities and differences between LNNs and RNNs, summarizes their respective shortcomings and challenges, and points out valuable directions for future research, particularly emphasizing the importance of improving the scalability of LNNs to promote their application in broader and more complex scenarios.
Abstract (translated)
这篇评论旨在对液态神经网络（LNN）和传统的循环神经网络（RNN）及其变体，如长短期记忆网络（LSTM）和门控循环单元（GRU），进行比较分析。该研究的核心维度包括模型准确性、内存效率以及泛化能力。通过系统回顾现有的研究成果，本文探讨了这些神经网络架构在处理序列数据时的基本原理、数学模型、关键特性及其内在挑战。研究表明，作为新兴的生物启发式连续时间动态神经网络，LNN在处理噪声和非平稳数据方面表现出显著潜力，并且能够实现出域（OOD）泛化。此外，一些LNN变体在参数效率和计算速度上优于传统RNN。然而，由于其成熟的生态系统以及跨各种任务的成功应用，RNN仍然是序列建模的基石。这篇评论确定了LNN与RNN之间的共性和差异，总结了它们各自的缺点和挑战，并指出未来研究的重要方向，尤其强调提高LNN可扩展性的重要性，以促进其在更广泛和复杂的场景中的应用。
URL
https://arxiv.org/abs/2510.07578
