Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2404.10237.
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 2026-08-07T00:59:11.712948Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T15:34:48.332917Z
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 40f71297-f92b-4e1a-bd19-4b39d742ef60 · inbound
HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding cites this paper.
HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T10:47:15.100883Z
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 4e8a4fb8-8f3b-430a-9ee1-a147d52dc6c2 · inbound
Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning cites this paper.
Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:59:11.712948Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 08425c3b-cea7-4a62-89f6-060f4486ea08 · inbound
MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines cites this paper.
MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:37:44.507398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92d04f16-1da0-4392-9ea3-ee45a5c4d21a · inbound
Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space cites this paper.
Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T18:16:32.584875Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06baef11-2974-4d44-83ad-9e4285eaa623 · inbound
BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis cites this paper.
BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T23:23:26.897958Z
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 90e1b575-01a0-404d-8864-38a4b2c59217 · inbound
Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis cites this paper.
Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:20:58.235552Z
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 157bf930-5cab-40ac-ba11-74d57a7086e8 · inbound
Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis cites this paper.
Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T22:34:02.160114Z
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 a1568723-99cd-46de-9736-58a0f91a93a0 · inbound
Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey cites this paper.
Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T15:34:48.336565Z
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 99ca3d0f-3773-49e8-88e1-ff4c1cf145b0 · inbound
When Language Models Meet NeuroGraphs: Exploring Enhanced Agentic LLM Framework Towards Brain Network Analysis cites this paper.
When Language Models Meet NeuroGraphs: Exploring Enhanced Agentic LLM Framework Towards Brain Network Analysis Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T05:55:11.062584Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.