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Paper Citation Record · LEDGER

End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.00524.

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

pith.paper-citation-record.v1
2503.00524 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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:28:31.910534Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:59:43.288344Z

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 81a25906-d69d-43f6-aa9f-9ab2904b8f9d · inbound

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework cites this paper.

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-05T19:28:31.910534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:28:31.910534Z digest=sha256:c997de792a5e8463e37cf7e06e54fc82091dcbc241dfca6be38c83241ec0c720

Observation 72f23e5d-3eaa-4de5-b5d7-44fc0560a6bd · inbound

Scalable Maximum Entropy Reinforcement Learning for Diffusion Policies via Adjoint Matching cites this paper.

Scalable Maximum Entropy Reinforcement Learning for Diffusion Policies via Adjoint Matching End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:59:43.289816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-26T10:38:05.185415Z digest=sha256:38d28491416514418cfbae790963ca66abe8b5171db5512d025b9a20691b4546