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Paper Citation Record · LEDGER

How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.11668.

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

pith.paper-citation-record.v1
2112.11668 v1

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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:50:22.664538Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T18:46:52.316056Z

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 3f9fdf38-eba1-4643-b126-fe19fd6871ac · inbound

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth cites this paper.

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T18:50:22.664538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:50:22.664538Z digest=sha256:5923278563dde09b805ff246ff6d98b7cc55053b343cc5cc90e369a8b99ef69d

Observation 7fb2a085-45bf-467b-b2aa-1cfc734151b8 · inbound

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers cites this paper.

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:46:52.322276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T18:46:51.546102Z digest=sha256:ea24c24a167887ff82300c4e923f76c1bc8e5a8f236f5ea192ea1495711f92a8