Paper Citation Record · LEDGER

Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2110.02905.

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

pith.paper-citation-record.v1
2110.02905 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:42:48.155065Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7e95b4b4-1318-4016-8ba4-09efba206a7c · inbound

Equivariant Action Sampling for Reinforcement Learning and Planning cites this paper.

Equivariant Action Sampling for Reinforcement Learning and Planning Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:28:18.612854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc183fc-4c9d-4a55-8df5-b5c852135887 · inbound

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials cites this paper.

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 170

Resolution: unresolved; no resolver link, observed 2026-08-08T13:08:49.709025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff3f5015-8ab3-4730-aba2-ef8dbbba5095 · inbound

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics cites this paper.

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T22:42:48.155065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a90f454c-95c3-4cd0-aeed-dbf05560c7c7 · inbound

STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation cites this paper.

STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:49.253471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc5545c0-a889-4842-9592-6b9fb88dc5d3 · inbound

Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks cites this paper.

Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T13:55:14.060146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdf013ab-c78c-465e-8837-c69728fc5522 · inbound

Cosmology with Topological Deep Learning cites this paper.

Cosmology with Topological Deep Learning Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:13.611854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39f847c9-8316-4309-867c-d27094515bf8 · inbound

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules cites this paper.

DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:41.031080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8321533-fd03-4df0-bfb9-33e9d65ff7d5 · inbound

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending cites this paper.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T14:49:53.084615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd7abdfb-4a3f-471d-b054-01ba5f90cc62 · inbound

Bayesian Prior Construction for Uncertainty Quantification in First-Principles Statistical Mechanics cites this paper.

Bayesian Prior Construction for Uncertainty Quantification in First-Principles Statistical Mechanics Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T22:36:20.082737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 036bb212-8138-47cc-a7fd-da9043e3c1e1 · inbound

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks cites this paper.

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:46:23.345033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e321fce4-0107-407e-8d9c-b2ef8dbd351e · inbound

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing cites this paper.

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:02:31.603361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0cfd1eb3-8411-4511-8726-cdb29e97c889 · inbound

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics cites this paper.

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T12:05:54.442913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0e8f0be-3b98-422c-b65c-abb9004d88e1 · inbound

Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks cites this paper.

Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-15T03:09:43.925689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cb5ea9e5-d795-4029-b29a-95522d124504 · inbound

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems cites this paper.

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 152

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:37:29.947866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7083180-5b0c-4685-859c-9cd38fc0b163 · inbound

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening cites this paper.

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T02:09:22.463380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 20460b72-18ba-4465-88f8-f64d4fd1cf92 · inbound

Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs cites this paper.

Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T16:48:40.348815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.