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Paper Citation Record · LEDGER

A solvable generative model with a linear, one-step denoiser

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.17807.

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

pith.paper-citation-record.v1
2411.17807 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:28:50.212651Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T10:54:07.982528Z

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 e140d5ea-1b00-44e8-900d-4ee73d66c4cd · inbound

Diffusion models under low-noise regime cites this paper.

Diffusion models under low-noise regime A solvable generative model with a linear, one-step denoiser

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T05:28:50.212651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:28:50.212651Z digest=sha256:7ab6815b60a3ee59ecf10d39cd861daa0cfed53985087ea949bb7a1fcdb7a712

Observation 5529318b-ff46-4fc0-8118-4bab545363d5 · inbound

Diffusion Models Memorize in Training -- and Generalize in Inference cites this paper.

Diffusion Models Memorize in Training -- and Generalize in Inference A solvable generative model with a linear, one-step denoiser

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-21T10:54:07.984585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-21T10:52:31.849094Z digest=sha256:e9925fd591f59ee0c068e426cb521033b04bc20c8d2d4cf403f360b9e6a31a44