Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Noise is an Efficient Learner for Zero-Shot Vision-Language Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.06019.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:19.132444Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T23:17:14.256179Z
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 bd4549b0-3e2a-421f-bf14-7c5a85841c67 · inbound
On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? cites this paper.
On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable? Noise is an Efficient Learner for Zero-Shot Vision-Language Models
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:21:19.132444Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ea186bd-6a2d-4f2e-9755-61afd259452c · inbound
Adapting Vision-Language Models Without Labels: A Comprehensive Survey cites this paper.
Adapting Vision-Language Models Without Labels: A Comprehensive Survey Noise is an Efficient Learner for Zero-Shot Vision-Language Models
Reference 167
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T23:17:14.261226Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 75b0ab1b-a219-4e23-b106-cf629ad64b23 · inbound
Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift cites this paper.
Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Noise is an Efficient Learner for Zero-Shot Vision-Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T19:13:53.548210Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.