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Paper Citation Record · LEDGER

Enhancing Accuracy in Generative Models via Knowledge Transfer

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.16837.

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

pith.paper-citation-record.v1
2405.16837 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:56:13.859713Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T14:46:43.498005Z

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 d98618e7-79ce-457a-a337-27f85fe65be5 · inbound

Generative Distribution Prediction: A Unified Approach to Multimodal Learning cites this paper.

Generative Distribution Prediction: A Unified Approach to Multimodal Learning Enhancing Accuracy in Generative Models via Knowledge Transfer

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-08T13:56:13.859713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:56:13.859713Z digest=sha256:3257b16bced67ea4dd6d06438b2efff7d5ffd7fe2322d77be4955c784107f0dd

Observation f758c503-41e0-4127-abdb-5a7594a8677f · inbound

Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference cites this paper.

Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference Enhancing Accuracy in Generative Models via Knowledge Transfer

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:46:43.567530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T21:05:40.979113Z digest=sha256:26cb4b9a60addc812206131b27e386ac235046da26b983295ec55ac71097525e