Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2311.06400.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T10:10:42.810240Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T20:57:23.218534Z
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 796231c6-c561-40ed-98cf-7e06ee204137 · inbound
Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation cites this paper.
Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T10:10:42.810240Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 417b3318-3893-45b9-82e6-2221afb05710 · inbound
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey cites this paper.
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Reference 94
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:02:25.181481Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 86ceae1e-9898-44a5-8402-dd0903b25b12 · inbound
Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines cites this paper.
Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Reference 60
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T20:47:23.222561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5f661ad4-42bd-47e9-82a4-7758a4d37bd1 · inbound
Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline cites this paper.
Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Reference 91
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T20:57:23.219855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.