Pith. sign in

Paper Citation Record · LEDGER

Large-scale Self-supervised Video Foundation Model for Intelligent Surgery

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2506.02692.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.02692 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:53:49.299883Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b64842a9-7d56-4067-b22f-11c11e19576f · inbound

Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge cites this paper.

Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge Large-scale Self-supervised Video Foundation Model for Intelligent Surgery

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:26:14.407269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T10:22:32.253706Z digest=sha256:008b1a194f50b242db12b97c534dd3800acafe0a98dc8a2bc4824d5dd5d7fb9d

Observation 990252f6-75d3-4105-97b2-8984cb665170 · inbound

On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training cites this paper.

On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training Large-scale Self-supervised Video Foundation Model for Intelligent Surgery

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-03T07:53:49.299883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T07:53:49.299883Z digest=sha256:e4271fadd3f48959943b7374b349385ce2e91aa36236f20a1d0147cfa004c529