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Paper Citation Record · LEDGER

Distilling Vision-Language Models on Millions of Videos

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.06129.

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

pith.paper-citation-record.v1
2401.06129 v2

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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:48:07.251910Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:29:57.132283Z

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 98f1a2a0-8a7a-4c6e-8f91-c50e1ee5aad4 · inbound

Movie2Story: A framework for understanding videos and telling stories in the form of novel text cites this paper.

Movie2Story: A framework for understanding videos and telling stories in the form of novel text Distilling Vision-Language Models on Millions of Videos

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T11:48:07.251910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:48:07.251910Z digest=sha256:b2aa40626040ac62865c6598daa0ec573a9838456f6c6c891da40fbab6cc16f0

Observation e8892617-34b6-4cd3-8f7f-6b49016190b9 · inbound

AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding cites this paper.

AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding Distilling Vision-Language Models on Millions of Videos

Reference 118

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:29:57.137835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:29:57.009131Z digest=sha256:8faf0c8e92801adf5f7ffcce097411b9e8b609802135ae51e8ec8f61a86b0f53