Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.14633.
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 2026-08-15T16:50:25.095380Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T20:23:23.694958Z
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 caa41d11-0379-4976-8556-069e325d54b2 · inbound
Multi-Task Label Discovery via Hierarchical Task Tokens for Partially Annotated Dense Predictions cites this paper.
Multi-Task Label Discovery via Hierarchical Task Tokens for Partially Annotated Dense Predictions Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:55:28.775537Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d52eb31d-99b6-414b-8f96-4c9320688a8b · inbound
Enhancing Mamba Decoder with Bidirectional Interaction in Multi-Task Dense Prediction cites this paper.
Enhancing Mamba Decoder with Bidirectional Interaction in Multi-Task Dense Prediction Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:50:25.095380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b377be28-0c56-45f4-9208-853b8f62e8bc · inbound
SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models cites this paper.
SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T20:23:23.697333Z
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.