Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Robustness of Transformers against Context Hijacking for Linear Classification
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.15609.
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 2026-08-05T22:10:17.523794Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T09:36:10.886425Z
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 f4789f4f-169c-4745-b6b3-5de1296588fa · inbound
Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression cites this paper.
Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression On the Robustness of Transformers against Context Hijacking for Linear Classification
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:10:17.523794Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf7372d7-53e5-4055-8ae1-482a1ff8a615 · inbound
Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory cites this paper.
Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory On the Robustness of Transformers against Context Hijacking for Linear Classification
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T09:36:10.896120Z
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.