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Paper Citation Record · LEDGER

Clifford Group Equivariant Neural Networks

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2305.11141.

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

pith.paper-citation-record.v1
2305.11141 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:48:24.605932Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T09:09:53.219956Z

Reference resolution

0 of 0 outbound references displayed

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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 0905f5be-2bf5-4fec-a28f-9842062637ac · inbound

Probing Equivariance and Symmetry Breaking in Convolutional Networks cites this paper.

Probing Equivariance and Symmetry Breaking in Convolutional Networks Clifford Group Equivariant Neural Networks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T22:48:24.605932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:48:24.605932Z digest=sha256:4b934ee9d5ffed85810bc1a900af2d4cb1e50b42c18cdbae562c8e41cedcb053

Observation b1ace589-9392-47e2-b9b1-a995cd4728b5 · inbound

Composing Linear Layers from Irreducibles cites this paper.

Composing Linear Layers from Irreducibles Clifford Group Equivariant Neural Networks

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T17:13:52.715395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:13:52.715395Z digest=sha256:5b367b6022a595011db3b175e88a1daf0f42caba121cd0b9e33072fc58fddcdc

Observation fd87c79b-b4d1-474c-ad76-a08b4b11f41f · inbound

The Program Hypergraph: Multi-Way Relational Structure for Geometric Algebra, Spatial Compute, and Physics-Aware Compilation cites this paper.

The Program Hypergraph: Multi-Way Relational Structure for Geometric Algebra, Spatial Compute, and Physics-Aware Compilation Clifford Group Equivariant Neural Networks

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-15T08:45:19.209764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T08:42:20.931132Z digest=sha256:d16cbb2606912d930c99b83ed8ac2eb60b2a9f64f767c29b08c5e4ecd650a8fa

Observation 8de0be42-760f-4470-b7d5-6e6d08d1ac75 · inbound

The Program Hypergraph: Multi-Way Relational Structure for Geometric Algebra, Spatial Compute, and Physics-Aware Compilation cites this paper.

The Program Hypergraph: Multi-Way Relational Structure for Geometric Algebra, Spatial Compute, and Physics-Aware Compilation Clifford Group Equivariant Neural Networks

Reference 20

Resolution
unresolved
no resolver link, observed 2026-07-13T23:04:18.864523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:04:18.864523Z digest=sha256:468ce2174934dc89fd3fce0acf45c532c1508572b8f16b8d9d4aa9af030c7f6a

Observation fccd4a34-8a86-4784-836b-4906c8287f5e · inbound

Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI cites this paper.

Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI Clifford Group Equivariant Neural Networks

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-15T09:09:53.222021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T09:07:06.620864Z digest=sha256:41c2833e3bafa839fe1ac15d7144d484e6370a92d5dd1c1e9e2f8b650bc6d31e

Observation eac12f86-91f1-484d-aea5-bc9e5d70353b · inbound

Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI cites this paper.

Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI Clifford Group Equivariant Neural Networks

Reference 20

Resolution
unresolved
no resolver link, observed 2026-07-13T23:03:58.008815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:03:58.008815Z digest=sha256:f2b6082647575777a740f28e619c8f9f076b15c06925a9628aa61d6b6621802c

Observation e1e3d597-7b15-4196-bf2f-d719f9c3e9e5 · inbound

Virtues and Vices of Equivariant Transformers cites this paper.

Virtues and Vices of Equivariant Transformers Clifford Group Equivariant Neural Networks

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T00:13:49.164300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:13:49.164300Z digest=sha256:928818a0652c2eba3f4a86f8618cc6290fe258c270d5fb84b5153007f9ae5c4c