Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.11121.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:14:25.003751Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T18:35:46.056870Z
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 7aad9c9d-09bd-430e-8ad7-49f35bab40f0 · inbound
CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment cites this paper.
CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T18:35:46.059643Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b6b3514c-e7f8-439e-8e7e-dca534b20f04 · inbound
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models cites this paper.
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment
Reference 80
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T20:14:25.003751Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.