Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.16637.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:28:26.657390Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:56:13.926398Z
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 80e2e3bf-68dc-4bd1-90e9-bdb2235a8f95 · inbound
Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study cites this paper.
Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:22:46.867030Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 297e863c-0ad8-476b-a262-bf9b2b5ac3ac · inbound
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency cites this paper.
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:56:13.956000Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d84fd718-3aff-44ff-9459-8f1aab23113a · inbound
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles cites this paper.
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T04:28:26.657390Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.