Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can 3D Vision-Language Models Truly Understand Natural Language?
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.14760.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:09:41.110626Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T11:07:15.312693Z
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 200751b9-1a04-43b7-8a42-ea63d97bb9aa · inbound
Solving Zero-Shot 3D Visual Grounding as Constraint Satisfaction Problems cites this paper.
Solving Zero-Shot 3D Visual Grounding as Constraint Satisfaction Problems Can 3D Vision-Language Models Truly Understand Natural Language?
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:09:41.110626Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 89992980-f643-4bcd-9190-ff61916e22ca · inbound
Do large language vision models understand 3D shapes? cites this paper.
Do large language vision models understand 3D shapes? Can 3D Vision-Language Models Truly Understand Natural Language?
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:32:59.735368Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5c994a3-d779-46d0-8a4f-958398aa92f1 · inbound
DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework cites this paper.
DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework Can 3D Vision-Language Models Truly Understand Natural Language?
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T11:07:15.314158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.