Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2504.10700.
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 2026-08-16T11:13:43.416218Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T19:37:19.117554Z
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 2ee6041a-b8ce-4a31-b439-5e4fd7a6c7e7 · inbound
High-performance training and inference for deep equivariant interatomic potentials cites this paper.
High-performance training and inference for deep equivariant interatomic potentials Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:13:43.416218Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03f625b2-6e5e-44b8-a7f2-fdcc2683133f · 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 Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:09:26.032731Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c41d02d-f7b7-412a-b05a-bb93634fc86d · inbound
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
Reference 257
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T19:37:19.119320Z
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.