Paper Citation Record · LEDGER

On Equivariant Model Selection through the Lens of Uncertainty

As of 7 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2506.18629.

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

pith.paper-citation-record.v1
2506.18629 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:21:45.558985Z

measured 12 of 12 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy0
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dff0581b-672c-47db-9938-e60cc22925be · outbound

This paper cites Conformal prediction: A gentle introduction.

On Equivariant Model Selection through the Lens of Uncertainty Conformal prediction: A gentle introduction

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:44.452626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf0fb8cf-dd50-4f9e-8670-4d61201df7ee · outbound

This paper cites Geometric deep learning on molecular representations.

On Equivariant Model Selection through the Lens of Uncertainty Geometric deep learning on molecular representations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:44.514042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff854c99-5434-46c2-aff3-0963d463232d · outbound

This paper cites E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

On Equivariant Model Selection through the Lens of Uncertainty E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:44.628950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d0275fd-8d77-44a8-b8ca-d1f9292cee2a · outbound

This paper cites The need for uncertainty quantification in machine-assisted medical decision making.

On Equivariant Model Selection through the Lens of Uncertainty The need for uncertainty quantification in machine-assisted medical decision making

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:44.711832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c50e1f2b-e0b3-45a6-967a-1c9992bea5b0 · outbound

This paper cites Fast, expressive se(n)-equivariant networks through weight-sharing in position-orientation space.

On Equivariant Model Selection through the Lens of Uncertainty Fast, expressive se(n)-equivariant networks through weight-sharing in position-orientation space

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:44.834752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec82b21c-398b-470b-911e-30b616e6c448 · outbound

This paper cites Pattern recognition and machine learning.

On Equivariant Model Selection through the Lens of Uncertainty Pattern recognition and machine learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:44.898100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40244cca-055b-4f09-bdf7-23d5e2298ffa · outbound

This paper cites Probabilistic symmetries and invariant neural networks.

On Equivariant Model Selection through the Lens of Uncertainty Probabilistic symmetries and invariant neural networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:44.988142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7557c321-3c38-4d66-ad12-89bde550fd5f · outbound

This paper cites Geometric and physical quantities improve e (3) equivariant message passing.

On Equivariant Model Selection through the Lens of Uncertainty Geometric and physical quantities improve e (3) equivariant message passing

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:45.113340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79832c17-0a10-404e-9b9e-dc745db244ed · outbound

This paper cites Verification of forecasts expressed in terms of probability.

On Equivariant Model Selection through the Lens of Uncertainty Verification of forecasts expressed in terms of probability

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:45.204945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5201ba91-9cb7-474f-a80a-9076e95882d2 · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

On Equivariant Model Selection through the Lens of Uncertainty Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:45.289074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4d7c957-a59a-40b5-a886-af91c376fc0e · outbound

This paper cites Uncertainty quantification with graph neural networks for efficient molecular design.

On Equivariant Model Selection through the Lens of Uncertainty Uncertainty quantification with graph neural networks for efficient molecular design

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:45.428234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0243acc6-62d0-4579-8286-07dc0530e64a · outbound

This paper cites Group equivariant convolutional networks.

On Equivariant Model Selection through the Lens of Uncertainty Group equivariant convolutional networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:45.558985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.