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Paper Citation Record · LEDGER

Equivariant Matrix Function Neural Networks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.10434.

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

pith.paper-citation-record.v1
2310.10434 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:26:28.203901Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:37:19.115296Z

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 19859d4d-d87b-44c8-82ab-e3449130c212 · inbound

Learning the Electronic Hamiltonian of Large Atomic Structures cites this paper.

Learning the Electronic Hamiltonian of Large Atomic Structures Equivariant Matrix Function Neural Networks

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-09T21:26:28.203901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:26:28.203901Z digest=sha256:6bfff7d5fc8397ac2367dacb254bd224f2e560a70feb59dcb7550e9774f7ee4d

Observation 45402d9c-455a-4059-9301-c1ab0728d22c · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Equivariant Matrix Function Neural Networks

Reference 98

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:37:19.116541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T21:27:50.941166Z digest=sha256:b0d11e5394152bfab4f34e0cdbeedf7c100584e7d5f55bf3efd6deda14ddc4d3