Paper Citation Record · LEDGER

Adversarial Robustness as a Prior for Learned Representations

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1906.00945.

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

pith.paper-citation-record.v1
1906.00945 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:50:06.519296Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:16:26.854184Z

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 306769df-3a4c-46ef-a865-e912e1af8680 · inbound

Toy Models of Superposition cites this paper.

Toy Models of Superposition Adversarial Robustness as a Prior for Learned Representations

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T22:44:43.473199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9d92415-b628-4d26-ba3e-9bd1b5d2727a · inbound

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models cites this paper.

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models Adversarial Robustness as a Prior for Learned Representations

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-09T16:50:06.519296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d97c185-6ae8-4577-93b4-9e7ee1c0d031 · inbound

Adversarial Examples Are Not Bugs, They Are Superposition cites this paper.

Adversarial Examples Are Not Bugs, They Are Superposition Adversarial Robustness as a Prior for Learned Representations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T16:56:08.294470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c3373a1-0f92-4d46-b7ad-0e5626e11198 · inbound

Accuracy Does Not Guarantee Human-Likeness: Cross-Domain Human-Centered Benchmark in Monocular Depth Estimation cites this paper.

Accuracy Does Not Guarantee Human-Likeness: Cross-Domain Human-Centered Benchmark in Monocular Depth Estimation Adversarial Robustness as a Prior for Learned Representations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T06:45:04.114873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f4bb584-eb06-43d2-9a04-147b1f5b37e2 · inbound

Adjoint Inversion Reveals Holographic Superposition and Destructive Interference in CNN Classifiers cites this paper.

Adjoint Inversion Reveals Holographic Superposition and Destructive Interference in CNN Classifiers Adversarial Robustness as a Prior for Learned Representations

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T10:01:29.684220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7294dc95-22be-417b-a6b6-f90fb23b2e2e · inbound

Laundering AI Authority with Adversarial Examples cites this paper.

Laundering AI Authority with Adversarial Examples Adversarial Robustness as a Prior for Learned Representations

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:41:08.058818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4566e119-1a80-4dc7-a70c-88f88a110a13 · inbound

Toward Understanding Adversarial Distillation: Why Robust Teachers Fail cites this paper.

Toward Understanding Adversarial Distillation: Why Robust Teachers Fail Adversarial Robustness as a Prior for Learned Representations

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:41:17.141808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22141891-5a9a-4a99-a3fc-24ca8e2f3f47 · inbound

Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models cites this paper.

Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models Adversarial Robustness as a Prior for Learned Representations

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:16:26.855746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 913940a8-ac31-4805-a08c-4f3856fb5c28 · inbound

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers cites this paper.

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers Adversarial Robustness as a Prior for Learned Representations

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-13T02:43:47.779443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.