Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Polynomial Width is Sufficient for Set Representation with High-dimensional Features
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2307.04001.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:28:10.408292Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T20:10:34.853598Z
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 85961c29-5fcf-4e07-898b-3f59822e3a97 · inbound
Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning cites this paper.
Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning Polynomial Width is Sufficient for Set Representation with High-dimensional Features
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:28:10.408292Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 49aa55ef-d454-45e5-9afc-c53120890f29 · inbound
Monotone and Separable Set Functions: Characterizations and Neural Models cites this paper.
Monotone and Separable Set Functions: Characterizations and Neural Models Polynomial Width is Sufficient for Set Representation with High-dimensional Features
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T20:10:34.856202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 56f66d03-05bf-4e11-b31d-d082ba2fbd72 · inbound
Monotone and Separable Set Functions: Characterizations and Neural Models cites this paper.
Monotone and Separable Set Functions: Characterizations and Neural Models Polynomial Width is Sufficient for Set Representation with High-dimensional Features
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T08:28:39.518345Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.