Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.14309.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:53:49.683838Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T20:18:55.612495Z
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 273752d5-9c32-4cd4-919a-de7a25591e17 · inbound
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense cites this paper.
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:53:49.683838Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 83229549-ea71-436c-a867-d3bbd7edbfe9 · inbound
Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations cites this paper.
Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:01:18.356136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3dceb567-3a5b-46fd-bb3c-696e18671094 · inbound
Generative AI and Federated Learning for Intrusion Detection Systems: A Survey cites this paper.
Generative AI and Federated Learning for Intrusion Detection Systems: A Survey Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective
Reference 95
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T20:18:55.615838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.