Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2401.10216.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:23.388641Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T03:24:12.774857Z
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 bd85775d-05ee-4fdb-ad15-3d6b671ca4ad · inbound
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems cites this paper.
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:13:23.388641Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 838d1c70-aa81-483d-9c56-98e42f304711 · inbound
A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) cites this paper.
A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:40:12.192686Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 22adecc9-1367-4147-8d5d-3dad0a1ee386 · inbound
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications cites this paper.
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:23:14.080001Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 56064f07-af6b-41de-ad67-9d4aac42a0cf · inbound
Hot-Ham: an accurate and efficient E(3)-equivariant machine-learning electronic structures calculation framework cites this paper.
Hot-Ham: an accurate and efficient E(3)-equivariant machine-learning electronic structures calculation framework Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:52:52.872904Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 55b8531c-de6e-40d4-a79a-4b58374e7633 · 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 Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T03:09:43.979821Z
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 27ccec7d-e885-41ce-a1af-29be03b93f54 · inbound
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation cites this paper.
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T03:34:34.230735Z
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 44c3fd01-ed37-491a-8b34-7a9f3a73bc34 · inbound
Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field cites this paper.
Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 54
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T07:33:13.265025Z
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 b68ed8f4-0587-4569-a431-dc55cc71c837 · inbound
Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials cites this paper.
Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T03:24:12.776394Z
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.