Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.00982.
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 2026-08-06T22:51:27.149398Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T20:43:09.268997Z
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 fc3de8ce-d6e2-4c72-a83b-92648f1da444 · inbound
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios cites this paper.
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
Reference 446
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:51:27.149398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8fcad50-7dac-412b-834f-4a7807d1b478 · inbound
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges cites this paper.
Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
Reference 65
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T20:43:09.342500Z
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.