Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
What Can We Learn from State Space Models for Machine Learning on Graphs?
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.05815.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T11:25:33.648056Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T22:13:30.581867Z
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 c158162d-542c-41f6-b483-399126a4077c · inbound
A Survey of Mamba cites this paper.
A Survey of Mamba What Can We Learn from State Space Models for Machine Learning on Graphs?
Reference 83
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T22:13:30.583377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d918f2fd-4185-4c6b-b802-9c6e880418a2 · inbound
Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN cites this paper.
Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN What Can We Learn from State Space Models for Machine Learning on Graphs?
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T11:25:33.648056Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e45168b-71f6-48d9-acab-54e7a87e01c5 · inbound
Benchmarking Sheaf Neural Networks for Inductive Tasks cites this paper.
Benchmarking Sheaf Neural Networks for Inductive Tasks What Can We Learn from State Space Models for Machine Learning on Graphs?
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T04:52:32.160373Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.