Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2502.15040.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T22:26:40.753852Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T20:17:21.488969Z
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 7dad49f6-0ca2-4643-84ca-8c9a1f8d1e05 · inbound
TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders cites this paper.
TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:26:40.753852Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0249c07-72a9-408f-b65d-7cc5f4a44a11 · inbound
Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities cites this paper.
Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:26:06.434645Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a29367a2-8714-4faa-9309-4e6ccf9a6b5b · inbound
MedVQA-TREE: A Multimodal Reasoning and Retrieval Framework for Sarcopenia Prediction cites this paper.
MedVQA-TREE: A Multimodal Reasoning and Retrieval Framework for Sarcopenia Prediction Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:05:38.840616Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 715adf48-120d-4058-83bc-3cca19d9c11b · inbound
FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A cites this paper.
FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T20:17:21.490898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f26f4abf-940e-419d-a729-c023d903c2a3 · inbound
HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models cites this paper.
HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T08:33:06.786829Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.