Paper Citation Record · LEDGER

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks

As of 9 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 1 inbound Pith citation observation for arXiv:2605.27662.

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

pith.paper-citation-record.v1
2605.27662 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:31:56.603881Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T04:31:56.753684Z

Reference resolution

10 of 10 outbound references displayed

verified exact2
verified fuzzy0
unresolved2
parse uncertain0
malformed identifier1
metadata mismatch5

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 90e738ae-675c-43ec-8664-83af257ab9d5 · outbound

This paper cites Does equivariance matter at scale?.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks Does equivariance matter at scale?

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T18:03:48.004078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 67ae714d-7bd5-4ece-92b9-c89e161008f3 · outbound

This paper cites Elhag, A.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks Elhag, A

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T17:59:03.152111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dce4038-4efd-4cde-abbf-a650bab38d5a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks Adam: A Method for Stochastic Optimization

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T18:03:47.597382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0751ef79-8d03-46b6-8692-4a2254da0e66 · outbound

This paper cites Optimizers Qualitatively Alter Solutions And We Should Leverage This.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks Optimizers Qualitatively Alter Solutions And We Should Leverage This

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T18:03:48.001449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 97282371-aacd-41d2-8d1c-0d886d9da56a · outbound

This paper cites URLhttps://doi.org/10.1162/neco.1994.6.1.147.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks URLhttps://doi.org/10.1162/neco.1994.6.1.147

Reference 5

Resolution: verified exact; doi, observed 2026-06-29T18:03:47.594632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e4128130-beca-4421-9f3f-4237202705dd · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-29T18:03:47.995713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d17e655-3b1d-4bde-befe-534f04331c65 · outbound

This paper cites Quantum chemistry structures and properties of 134 thousand molecules.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks Quantum chemistry structures and properties of 134 thousand molecules

Reference 7

Resolution: metadata mismatch; doi, observed 2026-06-29T18:03:47.599198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a5a623ef-82ec-4f22-ba6f-a4fd170da1a8 · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks Dynamic graph cnn for learning on point clouds

Reference 8

Resolution: metadata mismatch; doi, observed 2026-06-29T18:03:47.601006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ca68aa1-a0a5-4c30-8ec2-21725ba1feb9 · outbound

This paper cites org/CorpusID:206592833.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks org/CorpusID:206592833

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T17:59:03.152111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9843ce7c-08d0-4f95-87d2-3aaefee50846 · outbound

This paper cites 6 How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks A.

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks 6 How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks A

Reference 10

Resolution: malformed identifier; arxiv_id, observed 2026-06-29T18:03:47.998524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation fb20c61e-15f1-4c34-92cb-2ce4e1bef9b2 · inbound

The Loss Does Not See the Basis, but Adam Does cites this paper.

The Loss Does Not See the Basis, but Adam Does How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T04:31:56.756734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.