Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving the Adversarial Robustness of NLP Models by Information Bottleneck
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2206.05511.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:51:42.792706Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T05:05:02.966784Z
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 a572c567-f59f-4d07-ada3-de646ceaa9f6 · inbound
VIBE: Annotation-Free Video-to-Text Information Bottleneck Evaluation for TL;DR cites this paper.
VIBE: Annotation-Free Video-to-Text Information Bottleneck Evaluation for TL;DR Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:51:42.792706Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df2ae603-7724-4d3e-86b5-f432756651af · inbound
A Survey on Data Security in Large Language Models cites this paper.
A Survey on Data Security in Large Language Models Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Reference 2022
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T05:05:02.970227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6d37f56c-0ba8-4c55-8d0c-8d027c2f187e · inbound
Evaluation of Adversarial Robustness in Arabic Language Models cites this paper.
Evaluation of Adversarial Robustness in Arabic Language Models Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T01:26:21.934341Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.