Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2406.05857.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:51:16.348789Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T18:51:16.847391Z
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 46553a3c-cede-4c0a-9760-6c929195ebf0 · inbound
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics cites this paper.
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T18:51:16.855192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.