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Paper Citation Record · LEDGER

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 3 inbound Pith citation observations for arXiv:2505.18131.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.18131 v1

Coverage vector

measured 37 of 37 reference resolution

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measured 40 of 40 standing notices

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T03:29:15.570760Z

measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-05-18T03:30:50.487543Z

Reference resolution

37 of 37 outbound references displayed

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Outbound references

Observation a43e6b0a-5670-476c-bbd4-31f1368b0ef8 · outbound

This paper cites DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems

Reference 1

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Observation 5c6ed50d-785e-4479-9a16-cef03ccaa356 · outbound

This paper cites (38) We proceed now to the inductive step forr≥2.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement (38) We proceed now to the inductive step forr≥2

Reference 2

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Observation 3ae60d8e-80e1-4839-8c79-64d232e126ed · outbound

This paper cites Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision

Reference 6

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Observation e4f59442-71a6-4704-8e9b-d0e9f9f2a169 · outbound

This paper cites Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks

Reference 7

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Observation 65322624-be23-4f16-b32e-2aa68f8e30f9 · outbound

This paper cites Detecting Dead Weights and Units in Neural Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Detecting Dead Weights and Units in Neural Networks

Reference 10

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Observation 9607c1e8-a447-42b4-9bb7-2e2fcf825ba5 · outbound

This paper cites On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks

Reference 11

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Observation 12bd6a03-a252-45bd-8260-8ccf724c4410 · outbound

This paper cites A., Jacob, B., Murphy, S.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement A., Jacob, B., Murphy, S

Reference 16

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Observation d7fdf2c5-e304-495d-891a-0b35679ea990 · outbound

This paper cites GKAN: Graph Kolmogorov-Arnold Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement GKAN: Graph Kolmogorov-Arnold Networks

Reference 18

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This paper cites Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster

Reference 23

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Observation 52114120-c127-4eae-b22d-0dd4403d96e8 · outbound

This paper cites A Survey on Kolmogorov-Arnold Network.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement A Survey on Kolmogorov-Arnold Network

Reference 24

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Observation c270de27-a23c-459f-a16e-4341b6b25721 · outbound

This paper cites J., Blanco, L., Pereira, R., and Caus, M.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement J., Blanco, L., Pereira, R., and Caus, M

Reference 27

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Observation d51c65a4-a3fe-49de-8fc5-6471d9706f5f · outbound

This paper cites K., Li, B., and Perdikaris, P.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement K., Li, B., and Perdikaris, P

Reference 30

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This paper cites doi: 10.1016/j.inffus.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement doi: 10.1016/j.inffus

Reference 31

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Observation 83a92786-088a-4887-8950-4a0a52ea5210 · outbound

This paper cites KAN versus MLP on Irregular or Noisy Functions.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement KAN versus MLP on Irregular or Noisy Functions

Reference 32

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Observation c62b1ffe-f005-4a84-9936-b0bcbd765341 · outbound

This paper cites We define the ReLUr−1 basis functions as ψ[r] i (x) =ReLU(x−t i)r−1.(30) Define A[r] ∈R (n+r−1)×(n+r−1) as the change-of-basis matrix between B[r] = h b[r] 1−r,.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement We define the ReLUr−1 basis functions as ψ[r] i (x) =ReLU(x−t i)r−1.(30) Define A[r] ∈R (n+r−1)×(n+r−1) as the change-of-basis matrix between B[r] = h b[r] 1−r,

Reference 33

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Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Unresolved cited work

Reference 35

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Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Unresolved cited work

Reference 36

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Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Unresolved cited work

Reference 37

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Observation 5213f60a-56e1-4a92-ae6c-62c654983778 · outbound

This paper cites A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Reference 1958

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Observation 2716aa12-0820-4195-8de7-5e7f43962a5e · outbound

This paper cites Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN

Reference 1959

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Observation bbdd7a56-b7b2-4a17-9fba-0f1b8a76a556 · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 1988

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Observation 981ea586-e5b9-48b4-a24e-5e03516dee74 · outbound

This paper cites Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Reference 1993

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Observation fc3cf6b5-0b7a-49c2-b1da-698838336912 · outbound

This paper cites From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning

Reference 1998

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Observation 326ef646-7d99-4676-8eea-c9219d744a74 · outbound

This paper cites ReLU-KAN: New Kolmogorov-Arnold Networks that Only Need Matrix Addition, Dot Multiplication, and ReLU.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement ReLU-KAN: New Kolmogorov-Arnold Networks that Only Need Matrix Addition, Dot Multiplication, and ReLU

Reference 1999

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This paper cites On the Activation Function Dependence of the Spectral Bias of Neural Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement On the Activation Function Dependence of the Spectral Bias of Neural Networks

Reference 2003

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This paper cites An Expert's Guide to Training Physics-informed Neural Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement An Expert's Guide to Training Physics-informed Neural Networks

Reference 2004

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This paper cites KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning

Reference 2009

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Observation 4de3d28d-e4ff-4fac-96e8-ceefb02f1c36 · outbound

This paper cites KAN 2.0: Kolmogorov-Arnold Networks Meet Science.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement KAN 2.0: Kolmogorov-Arnold Networks Meet Science

Reference 2013

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This paper cites SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks

Reference 2015

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Observation 8f553753-46aa-42a7-9086-3d5193a1d80c · outbound

This paper cites Wav-KAN: Wavelet Kolmogorov-Arnold Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Wav-KAN: Wavelet Kolmogorov-Arnold Networks

Reference 2016

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This paper cites Physics-informed Kolmogorov-Arnold Network with Chebyshev Polynomials for Fluid Mechanics.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Physics-informed Kolmogorov-Arnold Network with Chebyshev Polynomials for Fluid Mechanics

Reference 2017

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This paper cites Layer Normalization.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Layer Normalization

Reference 2018

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This paper cites MLP-KAN: Unifying Deep Representation and Function Learning.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement MLP-KAN: Unifying Deep Representation and Function Learning

Reference 2020

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Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis

Reference 2021

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Source-reported events for the cited work

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Observation 11af4b06-d585-48ef-a8e4-e23c1e1fccb8 · outbound

This paper cites Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability

Reference 2022

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Observation fcaca961-0a17-484f-a79c-718691a612c0 · outbound

This paper cites PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 2023

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Observation 2a64e3b3-00bd-47b0-b52c-47fe32df0232 · outbound

This paper cites A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks

Reference 2024

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Pith citing papers

Observation e9664c3a-ddc5-49dd-a4d3-6be5763b27c6 · inbound

A Practitioner's Guide to Kolmogorov-Arnold Networks cites this paper.

A Practitioner's Guide to Kolmogorov-Arnold Networks Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement

Reference 49

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arxiv_id, observed 2026-05-18T03:30:50.491302Z

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Observation f52d1f4b-f45a-498c-a0a2-e89c5b5e6d34 · inbound

Making Gaussian Kolmogorov-Arnold Networks Reliable and Accurate cites this paper.

Making Gaussian Kolmogorov-Arnold Networks Reliable and Accurate Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement

Reference 20

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arxiv_id, observed 2026-05-11T18:51:07.126159Z

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Observation 43d33cf9-66ac-4135-a6eb-143a16aca0d8 · inbound

Partition-of-Unity Gaussian Kolmogorov-Arnold Networks cites this paper.

Partition-of-Unity Gaussian Kolmogorov-Arnold Networks Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement

Reference 26

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arxiv_id, observed 2026-05-11T21:36:12.925873Z

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