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Paper Citation Record · LEDGER

On the Convergence and Robustness of Adversarial Training

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.08304.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2112.08304 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:05:48.888250Z

measured 0 of 1 external citation measurements

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-24T02:38:47.015471Z digest=sha256:67f269584e1661af3aac74035cebcc5955b78a1ffe6176f037393b0899486ddc

Observation 3433a069-c3c2-42d3-a1fb-9a33bb1b0019 · inbound

How Do Diffusion Models Improve Adversarial Robustness? cites this paper.

How Do Diffusion Models Improve Adversarial Robustness? 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.

source=pdf_text observed=2026-08-07T13:05:48.888250Z digest=sha256:c6b3c6362869668640566aeeb7add48729bfaf4525e03c40ba80d6399520a312