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Paper Citation Record · LEDGER

USP: Unified Self-Supervised Pretraining for Image Generation and Understanding

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.06132.

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

pith.paper-citation-record.v1
2503.06132 v3

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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:58:56.171577Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:36:26.505003Z

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 0c186524-2e2d-4b84-bbc2-9294c4108528 · inbound

Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion cites this paper.

Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion USP: Unified Self-Supervised Pretraining for Image Generation and Understanding

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-15T20:58:56.171577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:58:56.171577Z digest=sha256:a06bdf9679f08e250cc1c95206ba168efece9d9bc730cd7ed54b312cdd5afd95

Observation 0c4bd243-b568-4713-85f8-50dc1a6ebeb3 · inbound

Elucidating Representation Degradation Problem in Diffusion Model Training cites this paper.

Elucidating Representation Degradation Problem in Diffusion Model Training USP: Unified Self-Supervised Pretraining for Image Generation and Understanding

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:36:26.512693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-12T04:08:11.110912Z digest=sha256:13d3d4fc348b07e3fa781b492a8a1d14bd66347dbbb432418d64e2f218b90ab4