Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.06260.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T12:09:39.641313Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T19:19:42.923464Z
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 fa681bf3-4f34-420d-8caf-879593a290f3 · inbound
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving cites this paper.
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T19:19:42.926469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5e243766-bc21-46c4-87c2-c5aa4b25eeaa · inbound
Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security cites this paper.
Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:09:39.641313Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42e18603-d2fb-4269-86ed-a41c0f7ff6c7 · inbound
FedNSAM:Consistency of Local and Global Flatness for Federated Learning cites this paper.
FedNSAM:Consistency of Local and Global Flatness for Federated Learning From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T18:46:29.515806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.