Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1803.01588.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:09:26.088188Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T21:17:56.226483Z
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 6e0845d9-ccd0-40cf-a3d4-cb074066b65f · 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 N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
Reference 178
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T13:08:49.740670Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd14b472-53b0-4821-87b9-d8ac08e0460a · inbound
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling cites this paper.
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
Reference 2018
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T21:17:56.232786Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1f53d031-ddcb-4515-a686-14165123467a · inbound
The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products cites this paper.
The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:09:26.088188Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.