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arxiv: 2605.19031 · v2 · pith:7KU43LGSnew · submitted 2026-05-18 · 💻 cs.AI · eess.SP

KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition

Mengxi Liu , Sizhen Bian , Vitor Fortes , Francisco Calatrava Nicolas , Daniel Gei{\ss}ler , Maximilian Kiefer-Emmanouilidis , Bo Zhou , Paul Lukowicz This is my paper

Pith reviewed 2026-06-30 18:12 UTC · model grok-4.3

classification 💻 cs.AI eess.SP
keywords Kolmogorov-Arnold NetworksHuman Activity RecognitionIMU sensorsHybrid neural networksWearable sensingNeural architecture search
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The pith

A hybrid model using KAN for input embedding and classification with MLP layers in between improves IMU-based human activity recognition accuracy.

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

The paper explores how to combine Kolmogorov-Arnold Networks with conventional multi-layer perceptrons in models that recognize human activities from inertial measurement unit sensor data. KANs learn precise functions well on clean low-dimensional inputs but lose accuracy on the noisy signals typical of real wearable recordings, while MLPs tolerate noise better and run more efficiently. Systematic tests of KAN placements show that restricting KAN modules to the input embedding layer and a final LarctanKAN classifier, while keeping MLP layers for intermediate mixing, produces the best results. This hybrid raises average macro F1 score by 5.33 percent relative to a pure-MLP baseline across eight public datasets and also lifts performance when the same pattern is added to other established HAR architectures.

Core claim

The central claim is that replacing all MLP components with KANs degrades accuracy and efficiency on noisy IMU data, but a selective hybrid architecture that uses a KAN-based input embedding layer, retains MLP layers for intermediate feature mixing, and adds a specialized LarctanKAN module for final classification yields consistent gains. On eight public HAR datasets the hybrid model delivers a 5.33 percent average relative improvement in macro F1 score over the pure-MLP baseline and outperforms both standalone KAN and MLP models. Applying the identical hybrid pattern to other state-of-the-art HAR networks likewise improves their results, showing that careful orchestration of KAN and MLP com

What carries the argument

The hybrid KAN-MLP architecture that places KAN modules only at the input embedding layer and as a LarctanKAN classifier while retaining MLP layers for intermediate feature mixing.

If this is right

  • The hybrid strategy can be added to other existing HAR architectures to raise their accuracy without redesigning the full network.
  • Selective use of KAN components preserves noise robustness while adding precision that pure MLP models lack on IMU signals.
  • The approach yields more accurate and robust models for real-world wearable activity recognition tasks.
  • Careful placement of KAN modules matters more than blanket replacement of MLP layers.

Where Pith is reading between the lines

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

  • The same input-and-output KAN placement pattern may transfer to other noisy time-series classification problems beyond activity recognition.
  • Efficiency comparisons between the hybrid and pure models on edge devices could reveal whether the accuracy gain comes at an acceptable compute cost.
  • Further ablations that isolate the LarctanKAN classifier from the input embedding layer would clarify which component drives most of the observed lift.

Load-bearing premise

The performance gains arise specifically from the chosen placement of KAN modules rather than from differences in hyperparameter search effort or dataset-specific tuning.

What would settle it

Evaluating the same hybrid model and pure-MLP baseline on a new IMU HAR dataset with comparable noise levels and checking whether the 5.33 percent relative macro F1 improvement is reproduced.

Figures

Figures reproduced from arXiv: 2605.19031 by Bo Zhou, Daniel Gei{\ss}ler, Francisco Calatrava Nicolas, Maximilian Kiefer-Emmanouilidis, Mengxi Liu, Paul Lukowicz, Sizhen Bian, Vitor Fortes.

Figure 1
Figure 1. Figure 1: Comparison of model predictions on synthetic functions representing typical characteristics of sensor data. The step function [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed hybrid network architecture KAN-MLP-Mixer based on an empirical study. It consists of three modules: KAN [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average performance improvement compared to the MLPHAR baseline across eight datasets using the hybrid model with [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance comparison for KAN-MLP-Mixer and MLPHAR models on five datasets under three sensor configurations (single [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance comparison for KAN-MLP-Mixer and MLPHAR models under three window size configurations. Numerical [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The extending hybrid design across diverse neural backbones, the original models only have the pure convolutional layers on [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Number of parameters in different components of KAN-MLP-Mixer and MLPHAR models.(The [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Parameter-efficiency when scaling models comparing between KAN-MLP-Mixer and MLPHAR across eight benchmark [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Computational efficiency comparison between KAN-MLP-Mixer and MLPHAR across eight benchmark datasets. For KAN [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
read the original abstract

Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy and imperfect real-world datasets. In contrast, conventional multi-layer perceptrons (MLPs) are far more tolerant to noise and computationally efficient. Replacing all MLP components with KANs in HAR models often degrades accuracy and computation efficiency, highlighting an open challenge: how to combine KANs' precision with MLPs' noise robustness and efficiency. To address this, we systematically explore various placements of KAN modules within deep HAR networks and propose a hybrid architecture that strategically synergizes the strengths of both paradigms, which uses a KAN-based input embedding layer, retains MLP layers for intermediate feature mixing, and introduces a specialized LarctanKAN module for final activity classification. Across eight public HAR datasets, the hybrid KAN-MLP model achieves an average macro F1 score relative improvement of 5.33\% compared pure-MLP model, significantly outperforming standalone KAN and MLP baselines. Furthermore, integrating this hybrid strategy into other state-of-the-art HAR architectures consistently boosts their performance. Our findings demonstrate that a carefully orchestrated combination of KAN, MLP, or other conventional neural components yields more robust and accurate HAR models for real-world wearable sensing environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a hybrid KAN-MLP architecture for IMU-based human activity recognition that places a KAN module in the input embedding layer, retains MLP layers for feature mixing, and uses a specialized LarctanKAN classifier. It reports that this hybrid yields a 5.33% average relative improvement in macro F1 over pure-MLP baselines across eight public datasets, outperforms standalone KAN and MLP models, and can be integrated into other SOTA HAR architectures to improve their performance.

Significance. If the reported gains prove robust to hyperparameter matching and statistical controls, the work would demonstrate a practical way to combine KAN precision with MLP noise tolerance in real-world sensor data, potentially guiding hybrid designs for other noisy, high-dimensional tasks in wearable sensing.

major comments (2)
  1. [Abstract] Abstract: the central claim of a 5.33% average macro-F1 relative improvement is presented without per-dataset breakdowns, error bars, or statistical significance tests, which is load-bearing for the assertion that the specific KAN-MLP placement and LarctanKAN choice are the causal drivers rather than incidental factors.
  2. [Abstract] Abstract: the description of systematic architecture exploration does not reference ablation tables or controls that isolate the effect of KAN input embedding plus LarctanKAN classifier from differences in hyperparameter search budget or search space applied to the pure-MLP baselines.
minor comments (1)
  1. [Abstract] Abstract: the phrase "compared pure-MLP model" is missing the preposition "to".

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address each comment below and will revise the manuscript to strengthen the presentation of results and controls.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of a 5.33% average macro-F1 relative improvement is presented without per-dataset breakdowns, error bars, or statistical significance tests, which is load-bearing for the assertion that the specific KAN-MLP placement and LarctanKAN choice are the causal drivers rather than incidental factors.

    Authors: The manuscript reports per-dataset macro-F1 scores, standard deviations across five random seeds, and paired statistical tests in the results section and supplementary tables. The abstract summarizes the average gain as the primary finding. We will revise the abstract to note the consistency of gains and statistical support, e.g., by adding a parenthetical reference to the detailed tables. revision: yes

  2. Referee: [Abstract] Abstract: the description of systematic architecture exploration does not reference ablation tables or controls that isolate the effect of KAN input embedding plus LarctanKAN classifier from differences in hyperparameter search budget or search space applied to the pure-MLP baselines.

    Authors: The full manuscript includes ablation studies that apply identical hyperparameter search budgets and spaces to all model variants. We will revise the abstract to explicitly reference these controlled ablations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance comparison on public datasets

full rationale

The paper conducts an empirical investigation of KAN placements in HAR models, reporting macro F1 improvements on eight public datasets without any derivation chain, equations, or self-citations that reduce claims to inputs by construction. The central result is a set of experimental comparisons whose validity rests on dataset benchmarks and baselines rather than tautological definitions or fitted-parameter renamings. No load-bearing self-citation or ansatz smuggling is present.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The paper rests on standard assumptions about neural network training and the relative noise tolerance of KAN versus MLP layers; the only invented component is the LarctanKAN module whose independent evidence is limited to the reported performance numbers.

axioms (1)
  • domain assumption KANs excel on clean low-dimensional data but degrade on noisy real-world sensor streams while MLPs are more robust and efficient
    Explicitly stated in the opening sentences of the abstract as the motivation for hybrid design.
invented entities (1)
  • LarctanKAN module no independent evidence
    purpose: Specialized final classification layer that combines KAN precision with activity-label output
    Introduced as a custom component in the hybrid architecture; no external validation or theoretical derivation supplied beyond the empirical gains.

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