Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A unified machine-learning framework for ab initio multiscale modeling of liquids
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2603.20493.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T13:05:36.641736Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T18:35:58.749363Z
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 b39168c4-f7ee-406f-a577-0aa8c7212e83 · inbound
Entropy density functional theory for inhomogeneous fluids cites this paper.
Entropy density functional theory for inhomogeneous fluids A unified machine-learning framework for ab initio multiscale modeling of liquids
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-07-09T02:19:52.020932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ff4b51da-736e-4ef5-9da0-12fc0def8083 · inbound
Entropy density functional universality: Correlation, response, and entropic Ornstein-Zernike structure cites this paper.
Entropy density functional universality: Correlation, response, and entropic Ornstein-Zernike structure A unified machine-learning framework for ab initio multiscale modeling of liquids
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T05:20:12.106534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0bd9e9c2-5989-41d6-a434-e8408c5c3924 · inbound
Entropy power functional theory for Brownian many-body dynamics cites this paper.
Entropy power functional theory for Brownian many-body dynamics A unified machine-learning framework for ab initio multiscale modeling of liquids
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T13:05:36.641736Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.