Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.10114.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:29:23.181738Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T15:40:18.951655Z
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 df2fc534-4c75-410d-80b9-d958d25ec915 · inbound
Vision-Language Models for Edge Networks: A Comprehensive Survey cites this paper.
Vision-Language Models for Edge Networks: A Comprehensive Survey Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Reference 171
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T12:20:08.831376Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5cbaf2f-0b8f-406f-baa9-c2508c2dd471 · inbound
Token-Level Prompt Mixture with Parameter-Free Routing for Federated Domain Generalization cites this paper.
Token-Level Prompt Mixture with Parameter-Free Routing for Federated Domain Generalization Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:29:23.181738Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e366e8f4-3869-4b5b-acd9-afb80d98e751 · inbound
A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning cites this paper.
A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Reference 77
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:16:30.658382Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 24bc6e20-6904-4e19-9807-1f97a72c4bbe · inbound
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach cites this paper.
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:22:59.589159Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bff5b8e1-3782-4c74-b5d5-e6513d402562 · inbound
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing cites this paper.
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T15:40:18.954185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.