Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.21100.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:13.976384Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:26:17.954731Z
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 639610e2-966e-4830-95cd-d0e7c3b49a1f · inbound
Testing Autonomous Driving Systems -- What Really Matters and What Doesn't cites this paper.
Testing Autonomous Driving Systems -- What Really Matters and What Doesn't AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests
Reference 47
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:26:17.959049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation bf64d0a9-65cd-4e90-9da7-5b6a2e2290f5 · inbound
NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment cites this paper.
NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:00:13.976384Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.