Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T23:07:31.266899Z
Paper Citation Record · LEDGER
Adapting to Evolving Adversaries with Regularized Continual Robust Training
As of 16 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2502.04248.
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 2026-05-10T00:39:43.196010Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T00:39:48.128864Z
Reference resolution
17 of 17 outbound references displayed
- verified exact3
- verified fuzzy3
- unresolved9
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7fbd3025-b74f-4b5a-bf90-7b57b7532b69 · outbound
This paper cites an unresolved cited work.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T23:07:31.503737Z
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 953c78e0-aa45-4c5e-a293-72dd9a165aaa · outbound
This paper cites an unresolved cited work.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T23:07:31.495618Z
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 16c31f60-b46c-4555-a721-4f955269bf72 · outbound
This paper cites an unresolved cited work.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T23:07:31.487326Z
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 deb37944-da27-40c4-a917-8c9558a510f9 · outbound
This paper cites Intriguing properties of neural networks.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Intriguing properties of neural networks
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T23:07:31.227870Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e8aa49f4-5abd-4c4a-a59b-02beb245cff3 · outbound
This paper cites an unresolved cited work.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T23:07:31.470018Z
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 0f32566d-595a-4ea6-9895-267c9ba8a7d0 · outbound
This paper cites Additional Related Work Adversarial Attacks and Defenses: ML models are vulnerable to input-space perturbations known as adversarial examples (Szegedy et al., 2014).
Adapting to Evolving Adversaries with Regularized Continual Robust Training Additional Related Work Adversarial Attacks and Defenses: ML models are vulnerable to input-space perturbations known as adversarial examples (Szegedy et al., 2014)
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T23:07:31.461632Z
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 bd50f0ea-f63b-4879-b495-e3ce9dbc8957 · outbound
This paper cites URL http: //openaccess.thecvf.com/content_cvpr_ 2018/html/Zhang_The_Unreasonable_ Effectiveness_CVPR_2018_paper.html.
Adapting to Evolving Adversaries with Regularized Continual Robust Training URL http: //openaccess.thecvf.com/content_cvpr_ 2018/html/Zhang_The_Unreasonable_ Effectiveness_CVPR_2018_paper.html
Reference 9
- Resolution
- verified exact
- arxiv_id_nonexistent, observed 2026-08-08T23:07:31.386716Z
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 abf67717-c5c1-48f1-8a2e-709cd9bdaeae · outbound
This paper cites an unresolved cited work.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T23:07:31.478126Z
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 02787b62-0ebb-4621-929c-36344fcc897e · outbound
This paper cites Manifold Regularization for Locally Stable Deep Neural Networks.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Manifold Regularization for Locally Stable Deep Neural Networks
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T23:07:31.419451Z
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 6051aa7e-e847-4f71-a586-aaf9e4e7c816 · outbound
This paper cites Zhang, H., Yu, Y ., Jiao, J., Xing, E.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Zhang, H., Yu, Y ., Jiao, J., Xing, E
Reference 2016
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T23:07:31.623262Z
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 18e5eea0-39c5-410b-a202-0f19393d33fe · outbound
This paper cites NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models.
Adapting to Evolving Adversaries with Regularized Continual Robust Training NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models
Reference 2018
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-08T23:07:31.399074Z
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 475c79f6-5f24-4a9a-9b33-9d4f5b94e030 · outbound
This paper cites RobustBench: a standardized adversarial robustness benchmark.
Adapting to Evolving Adversaries with Regularized Continual Robust Training RobustBench: a standardized adversarial robustness benchmark
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T23:07:31.211913Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d9582d9-9974-4e46-8ed0-c4d4f592c727 · outbound
This paper cites an unresolved cited work.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Unresolved cited work
Reference 2020
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T23:07:31.452936Z
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 b9d4c0e6-2dd6-47b7-875b-9901b7377380 · outbound
This paper cites + ALR feature.
Adapting to Evolving Adversaries with Regularized Continual Robust Training + ALR feature
Reference 2021
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-08T23:07:31.444508Z
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 d20ebdfd-7980-4c5a-a96e-ef9b6b4507b9 · outbound
This paper cites Position: Towards Resilience Against Adversarial Examples.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Position: Towards Resilience Against Adversarial Examples
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T23:07:31.216225Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb5b9db8-986a-4f79-aece-4fde85ae624a · outbound
This paper cites URL http://proceedings.
Adapting to Evolving Adversaries with Regularized Continual Robust Training URL http://proceedings
Reference 7482
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T23:07:31.615087Z
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 d275753b-7e7a-4732-9e20-1838a7d7608d · outbound
This paper cites an unresolved cited work.
Adapting to Evolving Adversaries with Regularized Continual Robust Training Unresolved cited work
Reference 8655
- Resolution
- verified exact
- doi, observed 2026-08-08T23:07:31.298583Z
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.
Pith citing papers
Observation cb3e4588-8047-49fa-9af0-abf36eeae24b · inbound
Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing cites this paper.
Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing Adapting to Evolving Adversaries with Regularized Continual Robust Training
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T00:39:48.131345Z
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.