Comparative Analysis of Liquid Neural Networks and LSTM for Sequential Pattern Recognition: Robustness, Efficiency, and Clinical Utility

2026-05-26

AI Summary

This study addresses the limitations of traditional RNNs and LSTMs in modeling continuous-time sequences with sparse or missing data, such as clinical physiological signals. It systematically evaluates liquid neural networks (LNNs), particularly the Closed-form Continuous-time (CfC) model, on multimodal native time-series tasks by modeling hidden-state dynamics through continuous differential equations and incorporating temporal dropout for robustness stress testing. Experiments across diverse datasets—including N-MNIST, QuickDraw, IAM, and PhysioNet Sepsis-3—demonstrate that LNNs significantly outperform LSTMs in both parameter efficiency and resilience to missing data. The results highlight the superior practical utility of LNNs in real-world applications such as event-based vision, handwriting recognition, and clinical monitoring.

Abstract

Traditional Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units operate on discrete time steps, often failing to capture the fluid temporal dynamics of real-world physical processes. Liquid Neural Networks (LNNs), specifically Closed-form Continuous-time (CfC) networks, address this by modeling the hidden state evolution as a continuous differential equation. In this paper, we conduct a comprehensive benchmarking study across four distinct sequential modalities: neuromorphic event-based data (N-MNIST), stroke-based drawing (QuickDraw), visual handwriting (IAM), and physiological time-series (PhysioNet Sepsis-3). Furthermore, we perform a rigorous stress test using temporal dropout to evaluate model robustness against missing data. Our findings reveal that LNNs consistently provide superior parameter efficiency and significantly higher robustness in natively temporal domains and clinical environments where data sparsity is prevalent. This extended preprint provides additional background on related datasets and the LNN theoretical lineage, supplemented with a detailed appendix documenting our full implementation and experimental settings.

Problem

• Sequential Pattern Recognition
• Temporal Dynamics
• Data Sparsity
• Robustness
• Clinical Time-series

Innovation

• Liquid Neural Networks
• Continuous-time Modeling
• Temporal Robustness
• Parameter Efficiency
• Sequential Pattern Recognition