Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning 1D Causal Visual Representation with De-focus Attention Networks
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.04342.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:22:14.772398Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T10:49:08.688094Z
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 cbf5f2d0-0890-4d63-815a-f1499f3040ee · inbound
Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning cites this paper.
Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning Learning 1D Causal Visual Representation with De-focus Attention Networks
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:22:14.772398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1065b4fa-72ca-4319-b427-b34a712b6197 · inbound
HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding cites this paper.
HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding Learning 1D Causal Visual Representation with De-focus Attention Networks
Reference 104
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T10:49:08.694510Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.