Pith. sign in

REVIEW 2 major objections 1 minor 30 references

Liquid Neural Networks provide superior parameter efficiency and robustness to missing data compared to LSTMs in sequential pattern recognition tasks.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

2026-06-29 19:24 UTC pith:G2WAQLAT

load-bearing objection This is a plain benchmarking study of CfC liquid nets versus LSTMs on four datasets with a temporal dropout test; it reports better efficiency and robustness for LNNs but supplies limited numbers and leaves external validity open. the 2 major comments →

arxiv 2605.27467 v1 pith:G2WAQLAT submitted 2026-05-26 cs.LG cs.AIcs.CV

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

Ye Kyaw Thu , Thazin Myint Oo , Thepchai Supnithi This is my paper
classification cs.LG cs.AIcs.CV
keywords Liquid Neural NetworksLSTMsequential pattern recognitionparameter efficiencyrobustnesstemporal dropoutclinical time seriesCfC networks
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper benchmarks Liquid Neural Networks against LSTMs across neuromorphic event data, stroke drawings, handwriting recognition, and physiological time series from sepsis patients. It applies temporal dropout to test performance under missing data conditions. The results indicate that LNNs require fewer parameters while delivering higher robustness, particularly in clinical environments with prevalent data sparsity. This is relevant because real-world sequential data often features continuous dynamics and irregular sampling that discrete models struggle with. Sympathetic readers would see potential for more efficient and reliable models in temporal domains.

Core claim

Closed-form Continuous-time Liquid Neural Networks, by evolving hidden states through continuous differential equations, consistently achieve better parameter efficiency and robustness to temporal dropout than LSTMs on N-MNIST, QuickDraw, IAM, and PhysioNet Sepsis-3 datasets.

What carries the argument

Closed-form Continuous-time (CfC) Liquid Neural Networks that model hidden state evolution as a continuous differential equation.

Load-bearing premise

The temporal dropout procedure and the four chosen datasets adequately represent the distribution of missing-data patterns and temporal dynamics encountered in real-world sequential pattern recognition tasks.

What would settle it

A comparison showing that LSTMs match or exceed LNNs in parameter efficiency and robustness on additional sequential datasets with different missing-value patterns would challenge the central claim.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • LNNs enable more parameter-efficient models for sequential tasks with limited computational resources.
  • Clinical prediction from physiological signals gains reliability from higher robustness under data sparsity.
  • Continuous-time modeling better captures fluid temporal dynamics than discrete time-step approaches in native temporal domains.
  • Event-based vision and stroke-based inputs become more tractable with LNNs due to the efficiency and robustness gains.
  • Real-world deployment in environments with intermittent sensor data becomes more practical using LNNs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The efficiency and robustness advantages may extend to other continuous-time domains such as robotics or financial time series.
  • Testing on real missing data rather than simulated temporal dropout could provide stronger validation of the robustness claim.
  • Hybrid models that combine LNN continuous dynamics with LSTM components might yield further performance improvements.
  • The results suggest examining whether the same benefits appear at larger model scales or on longer sequence lengths.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript conducts an empirical benchmarking study comparing Liquid Neural Networks (LNNs, specifically Closed-form Continuous-time or CfC networks) to LSTM models across four sequential datasets: neuromorphic event-based data (N-MNIST), stroke-based drawing (QuickDraw), visual handwriting (IAM), and physiological time-series (PhysioNet Sepsis-3). It includes a temporal dropout stress test to evaluate robustness to missing data and concludes that LNNs offer superior parameter efficiency and significantly higher robustness in natively temporal domains and clinical settings with data sparsity. The work is presented as an extended preprint with background, related work, and an appendix on implementation details.

Significance. If the reported performance advantages hold under scrutiny, the results could support greater use of continuous-time models like LNNs for irregular or sparse sequential data, with relevance to event-based sensing and clinical monitoring applications. The inclusion of an explicit temporal dropout stress test is a constructive element that directly addresses practical robustness concerns beyond standard accuracy metrics.

major comments (2)
  1. [Abstract] Abstract: the assertion that LNNs 'consistently provide superior parameter efficiency and significantly higher robustness' is presented without any accompanying numerical results, effect sizes, error bars, or statistical significance tests, which is load-bearing for the central empirical claim.
  2. [Datasets and Experimental Setup] Datasets and Experimental Setup (including the temporal dropout procedure): the four chosen datasets and the specific dropout mechanism are treated as representative of real-world missing-data patterns and temporal dynamics, particularly in clinical environments, but no additional validation, sensitivity analysis, or comparison to alternative missingness models is provided to support this external validity assumption that underpins the robustness conclusions.
minor comments (1)
  1. [Appendix] The appendix documenting full implementation and experimental settings is mentioned but could be cross-referenced more explicitly from the main experimental sections to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our benchmarking study. We address each major comment below and indicate planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that LNNs 'consistently provide superior parameter efficiency and significantly higher robustness' is presented without any accompanying numerical results, effect sizes, error bars, or statistical significance tests, which is load-bearing for the central empirical claim.

    Authors: We agree that the abstract would be improved by including supporting quantitative details. In the revised version we will add specific metrics (e.g., parameter counts, accuracy deltas, and robustness percentages under temporal dropout) drawn from the experimental results already reported in the main text. revision: yes

  2. Referee: [Datasets and Experimental Setup] Datasets and Experimental Setup (including the temporal dropout procedure): the four chosen datasets and the specific dropout mechanism are treated as representative of real-world missing-data patterns and temporal dynamics, particularly in clinical environments, but no additional validation, sensitivity analysis, or comparison to alternative missingness models is provided to support this external validity assumption that underpins the robustness conclusions.

    Authors: The four datasets were deliberately chosen to span event-based, stroke, handwriting, and physiological modalities, and the temporal dropout was introduced to probe robustness to missing observations. We acknowledge that explicit sensitivity analysis to other missingness mechanisms (e.g., random vs. bursty dropout) would strengthen the external-validity argument. We will add a dedicated paragraph discussing the rationale for the chosen dropout model together with a limited sensitivity study on at least one alternative missingness pattern. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely empirical benchmarking

full rationale

The manuscript is a comparative empirical study that benchmarks LNN (CfC) models against LSTMs across four datasets using accuracy, parameter count, and a temporal dropout robustness test. No derivations, closed-form predictions, fitted parameters presented as independent results, or self-citation load-bearing uniqueness theorems appear in the provided text or abstract. The central claims rest directly on experimental outcomes rather than any internal reduction to inputs by construction. This is the most common honest finding for benchmarking papers and warrants score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical benchmarking study. No free parameters, axioms, or invented entities are introduced or required by the abstract.

pith-pipeline@v0.9.1-grok · 5721 in / 1075 out tokens · 32857 ms · 2026-06-29T19:24:51.593808+00:00 · methodology

0 comments
read the original 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.

Figures

Figures reproduced from arXiv: 2605.27467 by Thazin Myint Oo, Thepchai Supnithi, Ye Kyaw Thu.

Figure 1
Figure 1. Figure 1: Example visualization of an N-MNIST sample (digit “3”). [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Sequential reconstruction of three “bird” sketches from the Quick [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example physiological time-series from the PhysioNet Sepsis-3 [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: N-MNIST Architecture: CNN backbone with 128 units CfC/LSTM [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: PhysioNet Architecture: 128/256-unit CfC core for binary sepsis [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: QuickDraw Architecture: 256-unit core for 50-class sketch recognition. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

30 extracted references · 5 canonical work pages · 2 internal anchors

  1. [1]

    Hasani, M

    R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, ”Closed-form continuous-time neural networks,”Nature Machine Intelligence, vol. 4, no. 11, pp. 992–1003, 2022

    2022

  2. [2]

    Hasani, M

    R. Hasani, M. Lechner, et al., ”Liquid Time-constant Networks,”AAAI Conference on Artificial Intelligence, 2021

    2021

  3. [3]

    R. T. Q. Chen, Y . Rubanova, J. Bettencourt, and D. K. Duvenaud, ”Neu- ral Ordinary Differential Equations,”Advances in Neural Information Processing Systems (NeurIPS), 2018

    2018

  4. [4]

    Lechner, R

    M. Lechner, R. Hasani, et al., ”Neural circuit policies enabling inter- pretable robotic control,”Nature Machine Intelligence, vol. 2, no. 10, pp. 642–652, 2020

    2020

  5. [5]

    Hasani, ”Interpretable Recurrent Neural Networks in Continuous-time Control Environments,” Ph.D

    R. Hasani, ”Interpretable Recurrent Neural Networks in Continuous-time Control Environments,” Ph.D. dissertation, TU Wien, Vienna, Austria, 2020

    2020

  6. [6]

    Hochreiter and J

    S. Hochreiter and J. Schmidhuber, ”Long Short-Term Memory,”Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997

    1997

  7. [7]

    Cho et al., ”Learning Phrase Representations using RNN Encoder- Decoder for Statistical Machine Translation,”EMNLP, 2014

    K. Cho et al., ”Learning Phrase Representations using RNN Encoder- Decoder for Statistical Machine Translation,”EMNLP, 2014

    2014

  8. [8]

    D. E. Rumelhart, G. E. Hinton, and R. J. Williams, ”Learning repre- sentations by back-propagating errors,”Nature, vol. 323, pp. 533–536, 1986

    1986

  9. [9]

    Orchard, A

    G. Orchard, A. Jayawant, S. K. Cohen, and N. Thakor, ”Converting static image datasets to spiking neuromorphic datasets,”Frontiers in Neuroscience, vol. 9, p. 437, 2015

    2015

  10. [10]

    Marti and H

    U. Marti and H. Bunke, ”The IAM-database: an English sentence database for offline handwriting recognition,”Int. Journal on Document Analysis and Recognition, vol. 5, pp. 39–46, 2002

    2002

  11. [11]

    A Neural Representation of Sketch Drawings

    D. Ha and D. Eck, ”A Neural Representation of Sketch Drawings,”arXiv preprint arXiv:1704.03477, 2017. [Google QuickDraw Dataset]

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  12. [12]

    M. A. Reyna et al., ”Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019,”Critical Care Medicine, vol. 48, no. 2, 2020

    2019

  13. [13]

    K. He, X. Zhang, S. Ren, and J. Sun, ”Deep Residual Learning for Image Recognition,”IEEE CVPR, 2016

    2016

  14. [14]

    LeCun, L

    Y . LeCun, L. Bottou, Y . Bengio, and P. Haffner, ”Gradient-based learning applied to document recognition,”Proceedings of the IEEE, 1998

    1998

  15. [15]

    Graves, S

    A. Graves, S. Fern ´andez, F. Gomez, and J. Schmidhuber, ”Connection- ist temporal classification: labelling unsegmented sequence data with RNNs,”ICML, 2006

    2006

  16. [16]

    Chahine et al., ”Robust flight navigation out of distribution with liquid neural networks,”Science Robotics, vol

    M. Chahine et al., ”Robust flight navigation out of distribution with liquid neural networks,”Science Robotics, vol. 8, no. 77, 2023

    2023

  17. [17]

    Fenghao et al., ”Liquid Neural Networks: Next-Generation AI for Telecom from First Principles,”China Communications, 2025

    Z. Fenghao et al., ”Liquid Neural Networks: Next-Generation AI for Telecom from First Principles,”China Communications, 2025

    2025

  18. [18]

    Srivastava et al., ”Dropout: A simple way to prevent neural networks from overfitting,”JMLR, vol

    N. Srivastava et al., ”Dropout: A simple way to prevent neural networks from overfitting,”JMLR, vol. 15, no. 1, 2014

    2014

  19. [19]

    Singer et al., ”The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),”JAMA, vol

    M. Singer et al., ”The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),”JAMA, vol. 315, no. 8, pp. 801– 810, 2016

    2016

  20. [20]

    Lechner and R

    M. Lechner and R. Hasani, ”Learning long-term dependencies in open- ended numerical expressions,”arXiv preprint arXiv:2010.12451, 2020

    work page arXiv 2010

  21. [21]

    J. G. White, E. Southgate, J. N. Thomson, and S. Brenner, ”The struc- ture of the nervous system of the nematodeCaenorhabditis elegans,” Philosophical Transactions of the Royal Society B, vol. 314, no. 1165, pp. 1–340, 1986

    1986

  22. [22]

    Lichtsteiner, C

    P. Lichtsteiner, C. Posch, and T. Delbruck, ”A128×128120 dB 15µs latency asynchronous temporal contrast vision sensor,”IEEE Journal of Solid-State Circuits, vol. 43, no. 2, pp. 566–576, 2008

    2008

  23. [23]

    Sironi, M

    A. Sironi, M. Brambilla, N. Bourdoukan, and P. Vandergheynst, ”HATS: Histograms of averaged time surfaces for robust event-based object classification,”IEEE CVPR, pp. 1731–1740, 2018

    2018

  24. [24]

    Generating Sequences With Recurrent Neural Networks

    A. Graves, ”Generating sequences with recurrent neural networks,”arXiv preprint arXiv:1308.0850, 2013

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  25. [25]

    Xu et al., ”SketchBERT: Learning sketch bidirectional encoder representation from transformers by self-supervised learning of sketch gestalt,”IEEE CVPR, 2021

    C. Xu et al., ”SketchBERT: Learning sketch bidirectional encoder representation from transformers by self-supervised learning of sketch gestalt,”IEEE CVPR, 2021

    2021

  26. [26]

    Graves, M

    A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber, ”A novel connectionist system for unconstrained hand- writing recognition,”IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, vol. 31, no. 5, pp. 855–868, 2009

    2009

  27. [27]

    Trocr: Transformer-based optical character recognition with pre-trained models,

    M. Li et al., ”TrOCR: Transformer-based optical character recognition with pre-trained models,”arXiv preprint arXiv:2109.10282, 2021

    work page arXiv 2021

  28. [28]

    Harutyunyan, H

    H. Harutyunyan, H. Khachatrian, D. C. Kale, G. Ver Steeg, and A. Galstyan, ”Multitask learning and benchmarking with clinical time series data,”Scientific Data, vol. 6, no. 1, p. 96, 2019

    2019

  29. [29]

    Zhang et al., ”Improved sepsis prediction using transformer-based models on ICU time-series data,”arXiv preprint arXiv:2010.10850, 2020

    Z. Zhang et al., ”Improved sepsis prediction using transformer-based models on ICU time-series data,”arXiv preprint arXiv:2010.10850, 2020

    work page arXiv 2010

  30. [30]

    Rubanova, R

    Y . Rubanova, R. T. Q. Chen, and D. K. Duvenaud, ”Latent ordinary differential equations for irregularly-sampled time series,”NeurIPS, 2019

    2019