Paper Citation Record · LEDGER

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.07073.

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

pith.paper-citation-record.v1
2507.07073 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:51:42.095493Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dd0b712f-d976-490c-91d0-aaf6268d750b · outbound

This paper cites Laplace–beltrami spectra as ‘shape-dna’ of surfaces and solids,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Laplace–beltrami spectra as ‘shape-dna’ of surfaces and solids,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.393162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf903be1-4f8c-4d3f-8c41-1e1bcad12fa4 · outbound

This paper cites Three-dimensional cad model matching with anisotropic diffusion maps,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Three-dimensional cad model matching with anisotropic diffusion maps,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.384582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ec5bbc3c-3153-4b80-b195-80f3a9b10e05 · outbound

This paper cites Deep wavelet neural pro- cess: Modeling stochastic variation of non-euclidean functional data for manufacturing qual- ity inference,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Deep wavelet neural pro- cess: Modeling stochastic variation of non-euclidean functional data for manufacturing qual- ity inference,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.375668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d82095d-5743-4f0b-b090-724001635a4d · outbound

This paper cites An intrinsic geometrical approach for statistical process control of surface and manifold data,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator An intrinsic geometrical approach for statistical process control of surface and manifold data,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.366387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99eac75d-0b06-443c-8555-70f073a1fa56 · outbound

This paper cites A registration-free approach for statistical process control of 3d scanned objects via fem,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator A registration-free approach for statistical process control of 3d scanned objects via fem,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.357417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf3029e7-3174-4e73-96dd-aa0c8df25e66 · outbound

This paper cites Chavel, Eigenvalues in Riemannian geometry.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Chavel, Eigenvalues in Riemannian geometry

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.348725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30ee7ff9-b00d-4c08-b4ec-5adcf1d6d40f · outbound

This paper cites Rosenberg, The Laplacian on a Riemannian Manifold: An Introduction to Analysis on Manifolds.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Rosenberg, The Laplacian on a Riemannian Manifold: An Introduction to Analysis on Manifolds

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.339807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b093a42-d03c-4424-b982-2b88969e941e · outbound

This paper cites Brainprint: A discriminative characterization of brain morphology,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Brainprint: A discriminative characterization of brain morphology,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.329785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7347c695-01aa-4ff7-9828-72ae7a4d1d7b · outbound

This paper cites Abc: A big cad model dataset for geometric deep learning,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Abc: A big cad model dataset for geometric deep learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.320173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b3f80ee-0ef0-47fc-9bb2-29a0e8dfd147 · outbound

This paper cites Universal approximation to nonlinear operators by neural net- works with arbitrary activation functions and its application to dynamical systems,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Universal approximation to nonlinear operators by neural net- works with arbitrary activation functions and its application to dynamical systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.310366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6951fdb9-268b-4830-93d5-a00be59edf39 · outbound

This paper cites Operator Learning: Algorithms and Analysis.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Operator Learning: Algorithms and Analysis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T18:51:42.043532Z

Source-reported events for the cited work

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Observation 75e30271-ce94-4b1a-9485-d7256b78b4f4 · outbound

This paper cites Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.301941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7f28ecc5-f225-48d9-a3a2-854d1e711db3 · outbound

This paper cites Pointwise convolutional neural networks,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Pointwise convolutional neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.292878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c2521115-1708-481b-8b04-7cf19c5cc7af · outbound

This paper cites View-based 3-d cad model retrieval with deep residual networks,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator View-based 3-d cad model retrieval with deep residual networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.283662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce094ae2-5b52-42a4-97df-c99ca5b364d4 · outbound

This paper cites Learning geometric operators on meshes,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Learning geometric operators on meshes,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.275282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ec392eb5-006b-4e96-a8ac-f0980afb34d4 · outbound

This paper cites Deep learning on geometry representations,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Deep learning on geometry representations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.266990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8496fd19-0ba7-4ecf-a023-674163f3414d · outbound

This paper cites Geometric deep learning: going beyond euclidean data,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Geometric deep learning: going beyond euclidean data,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.258411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b3c74c6e-e2b7-41c7-94e0-b21e783f637e · outbound

This paper cites Graph convolutional networks for learn- ing laplace-beltrami operators,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Graph convolutional networks for learn- ing laplace-beltrami operators,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.249615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 73ea24e0-5139-48ab-8e5b-a875ba865b4b · outbound

This paper cites Discrete differential-geometry operators for triangulated 2-manifolds,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Discrete differential-geometry operators for triangulated 2-manifolds,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.241246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2954632b-57e0-42d8-a089-a01ce0365e8e · outbound

This paper cites libigl: A simple C++ geometry processing library,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator libigl: A simple C++ geometry processing library,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.232131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 96d981a2-9cf9-44d2-9dcd-91a2d0289537 · outbound

This paper cites an unresolved cited work.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:51:42.222550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 003233d8-c241-41ad-b014-62cfe5054db7 · outbound

This paper cites Efficient multi-scale curvature and crease es- timation,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Efficient multi-scale curvature and crease es- timation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.213837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87af82da-2202-4bd7-aaab-0c5f0d2dc862 · outbound

This paper cites Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T18:51:42.076234Z

Source-reported events for the cited work

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Observation 1db02edb-2760-452e-a1aa-f6041adb48c1 · outbound

This paper cites Wide neural networks of any depth evolve as linear models under gradient descent,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Wide neural networks of any depth evolve as linear models under gradient descent,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.204973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7fc340d-2523-46d6-ac47-8af900deb05c · outbound

This paper cites Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.195314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80428b06-4325-4e6b-8f95-bfb885a31ef5 · outbound

This paper cites Pymeshlab,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Pymeshlab,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.185688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c9b8f53-b3a8-47c0-9507-49c7749c8346 · outbound

This paper cites Practical implementation of an end-to-end spectral method- ology for statistical process control of 3-d part geometry: A case study,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Practical implementation of an end-to-end spectral method- ology for statistical process control of 3-d part geometry: A case study,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.176627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 17db357c-0aaa-47d8-bd3b-c7c0d7ab5189 · outbound

This paper cites Trimesh,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Trimesh,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.167235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6201d020-d17b-4496-91bb-4e30e4777d72 · outbound

This paper cites A pragmatic view of accuracy measurement in forecasting,.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator A pragmatic view of accuracy measurement in forecasting,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.157936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d98a5353-5cd0-46c1-8208-140998b10195 · outbound

This paper cites Peak signal-to-noise ratio revisited: Is simple beautiful?.

An AI Approach for Learning the Spectrum of the Laplace-Beltrami Operator Peak signal-to-noise ratio revisited: Is simple beautiful?

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:51:42.147374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

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