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Paper Citation Record · LEDGER

Mathematical Foundations of Geometric Deep Learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2508.02723.

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

pith.paper-citation-record.v1
2508.02723 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:23:58.094037Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:29:02.705496Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation faadee9e-bfd5-4578-95fe-18807c064f77 · inbound

Deep Learning in Classical and Quantum Physics cites this paper.

Deep Learning in Classical and Quantum Physics Mathematical Foundations of Geometric Deep Learning

Reference 101

Resolution
unresolved
no resolver link, observed 2026-08-05T20:23:58.094037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:23:58.094037Z digest=sha256:a2eb5ae6887c04a6607d80ff9e42e767f4cd42ddae43a056bf449e7920a6682f

Observation 4dc40718-aa3b-4f67-b0cd-c23d05fb9608 · inbound

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule cites this paper.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Mathematical Foundations of Geometric Deep Learning

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:08.537271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:08.537271Z digest=sha256:12fde248139b7d42f303bbcdebf47426aaffcf975af3d801e9948f35c3639b70

Observation 8a1bcae8-f98d-4872-8a32-845289949f7a · inbound

Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity cites this paper.

Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Mathematical Foundations of Geometric Deep Learning

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:45:57.016876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-11T01:06:48.313418Z digest=sha256:cbeb63dbf6ebb019e46e702eb37392b7acbfc7a8f085914846fa82665b120a98

Observation 3577764f-1528-43ac-af8f-e5ea0197ea18 · inbound

Algebraic Networks and Architectural Degenerations cites this paper.

Algebraic Networks and Architectural Degenerations Mathematical Foundations of Geometric Deep Learning

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:29:02.707362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-26T22:13:06.697566Z digest=sha256:339ac4bf2320011a33d6e639ddf9992d85cf09ecf54194aab33007e77e14417f

Observation 1fce3073-55d7-4235-a924-28621cc632e7 · inbound

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models cites this paper.

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models Mathematical Foundations of Geometric Deep Learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-01T12:56:38.416613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T12:56:38.416613Z digest=sha256:fc2d4e1b8857973973cba5408b9cb77c7b3b852ba3e2019964382d520902adc5