Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating the Robustness of Neural Language Models to Input Perturbations
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2108.12237.
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 2026-08-06T14:57:06.893117Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T06:25:21.000290Z
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 e121a430-b6ba-4db3-a443-e334b19ab1c9 · inbound
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts cites this paper.
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts Evaluating the Robustness of Neural Language Models to Input Perturbations
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T06:25:21.003224Z
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.
Observation 5ac6a081-2131-489e-aab5-3c81dd804c36 · inbound
Agent Identity Evals: Measuring Agentic Identity cites this paper.
Agent Identity Evals: Measuring Agentic Identity Evaluating the Robustness of Neural Language Models to Input Perturbations
Reference 79
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:57:06.893117Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8653362-7194-47f1-bbe8-724f0738c7ae · inbound
Reliability Scaling Laws for Quantized Large Language Models cites this paper.
Reliability Scaling Laws for Quantized Large Language Models Evaluating the Robustness of Neural Language Models to Input Perturbations
Reference 92
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T08:45:52.855783Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.