Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.16315.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:42:41.594933Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:25:31.501889Z
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 9ec0c7fa-59b7-4954-ad58-62752c0cfd0f · inbound
Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models cites this paper.
Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:42:41.594933Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2a0e8522-27cb-48d4-97dd-5eb5c574748c · inbound
Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities cites this paper.
Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:36:01.302479Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fcae1e76-f45e-46cf-9fb3-b39ab4503c61 · inbound
SoftPipe: A Soft-Guided Reinforcement Learning Framework for Automated Data Preparation cites this paper.
SoftPipe: A Soft-Guided Reinforcement Learning Framework for Automated Data Preparation Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:25:31.507449Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b950a0fc-53e8-4272-beb0-52eedc27d30f · inbound
NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding cites this paper.
NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T13:01:58.682634Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df276695-4726-4669-b557-c8617313a1d8 · inbound
NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding cites this paper.
NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T06:36:33.831821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.