Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LAMBench: A Benchmark for Large Atomistic Models
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2504.19578.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T08:46:41.886218Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T01:23:49.425751Z
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 df03dfc6-e14b-40b5-8094-288b899d37f6 · inbound
Comparing the latent features of universal machine-learning interatomic potentials cites this paper.
Comparing the latent features of universal machine-learning interatomic potentials LAMBench: A Benchmark for Large Atomistic Models
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T01:23:49.428148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ceb3f9e2-bad1-498e-a8ba-4c338a7ba76f · inbound
AI-Driven Expansion and Application of the Alexandria Database cites this paper.
AI-Driven Expansion and Application of the Alexandria Database LAMBench: A Benchmark for Large Atomistic Models
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T23:18:39.744690Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 102e0055-33b3-4914-b0c1-5d843dc039cc · inbound
Pushing the limits of unconstrained machine-learned interatomic potentials cites this paper.
Pushing the limits of unconstrained machine-learned interatomic potentials LAMBench: A Benchmark for Large Atomistic Models
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T08:46:41.886218Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8cdd2a5e-3726-46ce-974a-9f34280ae9f5 · inbound
How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science cites this paper.
How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science LAMBench: A Benchmark for Large Atomistic Models
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T03:08:26.138423Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6a00091-ffc5-4f5c-a656-78adfaa3dad5 · inbound
VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python cites this paper.
VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python LAMBench: A Benchmark for Large Atomistic Models
Reference 280
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T16:17:26.963678Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.