Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Convergence and Robustness of Adversarial Training
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2112.08304.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T23:18:14.550398Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T02:43:47.690813Z
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 1ce77072-33b0-40c3-9bd1-42f874bc0640 · inbound
Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems cites this paper.
Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems On the Convergence and Robustness of Adversarial Training
Reference 129
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T02:43:47.693731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation bca67eba-b43f-401c-8624-8b24343de6d5 · inbound
Unsupervised dense retrieval with conterfactual contrastive learning cites this paper.
Unsupervised dense retrieval with conterfactual contrastive learning On the Convergence and Robustness of Adversarial Training
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:18:14.550398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3433a069-c3c2-42d3-a1fb-9a33bb1b0019 · inbound
Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry cites this paper.
Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry On the Convergence and Robustness of Adversarial Training
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:05:48.888250Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.