Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2412.15484.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T04:09:50.687652Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:17:29.017556Z
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 d0338b92-ab70-4b33-84f3-8f52ba425fb5 · inbound
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation cites this paper.
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T03:06:29.642228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1c122c86-07e9-45f2-81ff-a39571f7cca3 · inbound
CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning cites this paper.
CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:17:29.019126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 893d933d-6f00-4da1-84f3-d36db0bd92c8 · inbound
CAPEval: A Decoupled Caption Evaluation across Understanding and Generation cites this paper.
CAPEval: A Decoupled Caption Evaluation across Understanding and Generation Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T04:09:50.687652Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.