Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2105.11136.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:40:49.454924Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:25:55.517919Z
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 0005a546-b9b5-4d12-90d1-c2c27a91e0d7 · inbound
Coordinated Robustness Evaluation Framework for Vision-Language Models cites this paper.
Coordinated Robustness Evaluation Framework for Vision-Language Models Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:40:49.454924Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca5d8135-ce8e-42d7-bc25-ef1220d1d270 · inbound
AutoEvoEval: An Automated Framework for Evolving Close-Ended LLM Evaluation Data cites this paper.
AutoEvoEval: An Automated Framework for Evolving Close-Ended LLM Evaluation Data Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:45:01.788221Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 82d50c74-e9a1-4e10-84a4-005db90a5202 · inbound
Innocence in the Crossfire: Roles of Skip Connections in Jailbreaking Visual Language Models cites this paper.
Innocence in the Crossfire: Roles of Skip Connections in Jailbreaking Visual Language Models Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:25:55.524211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.