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Paper Citation Record · LEDGER

Geometric Generalization of Neural Operators from a Kernel Integral Perspective

As of 7 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 2 inbound Pith citation observations for arXiv:2602.01498.

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

pith.paper-citation-record.v1
2602.01498 v2

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:25:51.088493Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:59:39.771766Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:50:04.808486Z

Reference resolution

7 of 7 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d5f2cbe-d56f-4972-aee0-cb83e54c424c · outbound

This paper cites The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks.

Geometric Generalization of Neural Operators from a Kernel Integral Perspective The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-04T06:25:50.578925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:25:50.578925Z digest=sha256:dfef3730150a9f0e87cdd883fde5c038279964f8c36b351775fc44752e91be05

Observation 0c05dd78-ef20-4b46-9904-bd61a550a5ca · outbound

This paper cites an unresolved cited work.

Geometric Generalization of Neural Operators from a Kernel Integral Perspective Unresolved cited work

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-04T06:25:50.663687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:25:50.663687Z digest=sha256:be1d76ac1777d2dee33a5ca8e59e58a0f6fb35bf2f748d04f83e69114aa22106

Observation 76580926-b422-4fc0-9157-9025c172bc85 · outbound

This paper cites Singh and K.

Geometric Generalization of Neural Operators from a Kernel Integral Perspective Singh and K

Reference 494

Resolution
unresolved
no resolver link, observed 2026-08-04T06:25:51.012439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:25:51.012439Z digest=sha256:b0aa73053e50f5e3b1a864314631cedf103f758a2e446d54e832407249614b6d

Observation 816d135b-df5f-442e-8612-90a86e5da702 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Geometric Generalization of Neural Operators from a Kernel Integral Perspective Fourier Neural Operator for Parametric Partial Differential Equations

Reference 1989

Resolution
unresolved
no resolver link, observed 2026-08-04T06:25:50.944325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:25:50.944325Z digest=sha256:c98514f94dd82e3177500bdf0618b5ae8f818627dde285b6d9c6d4c0bba3f7b1

Observation 986a3828-c78b-4d32-8801-608e20bd3bb5 · outbound

This paper cites Strain,Fast potential theory.

Geometric Generalization of Neural Operators from a Kernel Integral Perspective Strain,Fast potential theory

Reference 2008

Resolution
unresolved
no resolver link, observed 2026-08-04T06:25:51.088493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:25:51.088493Z digest=sha256:bab4e2ee6e3083b844b146d1c9fe7c8a768f63f3ae30a1dba458391d3e628186

Observation e441936f-de5f-4c0e-8fab-c975180c755e · outbound

This paper cites an unresolved cited work.

Geometric Generalization of Neural Operators from a Kernel Integral Perspective Unresolved cited work

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-04T06:25:50.757541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:25:50.757541Z digest=sha256:2aed56547078385ce331a3ce35d3ee51bf70f7121ea854e7c9c5f0fa74927cda

Observation 49076a72-69d2-48a4-bd5d-a75a461516f1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Geometric Generalization of Neural Operators from a Kernel Integral Perspective Adam: A Method for Stochastic Optimization

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-04T06:25:50.853883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:25:50.853883Z digest=sha256:32f9241cacadc4e96933b6015df7e1ab18b2fe0d6bcb24a1f7d5511346383aa8

Pith citing papers

Observation 0639604d-a1a3-4290-ac21-d730e76c2b9c · inbound

Let There Be Light: Reflection, Refraction and Scattering for Neural Operators cites this paper.

Let There Be Light: Reflection, Refraction and Scattering for Neural Operators Geometric Generalization of Neural Operators from a Kernel Integral Perspective

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-08-04T02:40:03.477826Z

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.

source=pdf_text observed=2026-06-28T10:59:39.771766Z digest=sha256:b966ff340b783544c0510935a1f16fa34b89fce05177b0a504231bc19bca2f17

Observation f2d2ab87-ef5f-4a23-96ca-c35cabef3db8 · inbound

Solver Exactness, Learned Flexibility: Equivariant Boundary-Correction Operators for Stokes Flow cites this paper.

Solver Exactness, Learned Flexibility: Equivariant Boundary-Correction Operators for Stokes Flow Geometric Generalization of Neural Operators from a Kernel Integral Perspective

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-08-04T02:40:03.477826Z

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.

source=arxiv_source observed=2026-06-25T22:26:06.511781Z digest=sha256:a68121898a999bed1dc944f6192de2ae786cd291e74b7ab5062af89e53d524f3