Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2506.15223.
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 2026-08-06T13:03:26.560213Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T13:03:41.554494Z
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 8619e0cd-90c1-4f61-abf3-05035c8db30f · inbound
Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models cites this paper.
Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T13:03:41.661942Z
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 7d8cf0d6-753d-4819-87c4-4c6ddf290a81 · inbound
Active learning and explicit electrostatics enable accurate modeling of electrolytes cites this paper.
Active learning and explicit electrostatics enable accurate modeling of electrolytes An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T11:57:52.428808Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.