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Paper Citation Record · LEDGER

Geometrical aspects of lattice gauge equivariant convolutional neural networks

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2303.11448.

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

pith.paper-citation-record.v1
2303.11448 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:15:54.453209Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T00:58:48.370863Z

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 c65168e2-943f-4d57-ac19-4c8dd53e2a20 · inbound

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics cites this paper.

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics Geometrical aspects of lattice gauge equivariant convolutional neural networks

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-10T21:15:54.453209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:15:54.453209Z digest=sha256:7e443dd1094653452a80431448623c37d805cb30c40d639fe2eda84e8075c9d8

Observation fe9fcd2d-e8b2-4b80-b215-6e34b64cd9bf · inbound

Symmetry-preserving neural networks in lattice field theories cites this paper.

Symmetry-preserving neural networks in lattice field theories Geometrical aspects of lattice gauge equivariant convolutional neural networks

Reference 83

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:58:48.433396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T00:58:38.860519Z digest=sha256:df08fee753bd7899f6befd4032ecef2ec0762128209bccc23e0554ad2406e809