Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.15444.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:27:49.944424Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T05:22:42.409410Z
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 20cc9a98-90fa-499e-a717-5be513db22bc · inbound
Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay cites this paper.
Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:27:49.944424Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8caca29-704e-48ed-9e21-89ae398199f8 · inbound
SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense cites this paper.
SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense HINT: Hypernetwork Approach to Training Weight Interval Regions in Continual Learning
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T05:22:42.412275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.