Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1902.07623.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T23:14:41.663871Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T13:05:50.450879Z
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 a825a9c6-b90b-490e-8040-4545187790c0 · inbound
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms cites this paper.
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T23:14:41.663871Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73af7a46-2e2e-4348-8f04-7932ad257400 · inbound
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment cites this paper.
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:35:50.841588Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88b6d9d5-b578-48d4-b0e8-54d28cc639d7 · inbound
How Do Diffusion Models Improve Adversarial Robustness? cites this paper.
How Do Diffusion Models Improve Adversarial Robustness? advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
Reference 2021
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:05:50.544098Z
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.