Paper Citation Record · LEDGER

A Survey of Diffusion Models in Natural Language Processing

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.14671.

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

pith.paper-citation-record.v1
2305.14671 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:17:40.253465Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T11:39:46.313322Z

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 fc40b7e4-2008-4aa4-a644-455783a3ff5a · inbound

The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis cites this paper.

The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis A Survey of Diffusion Models in Natural Language Processing

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:40.253465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cf378ff-3a25-461c-b190-e39fecfd82d2 · inbound

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences cites this paper.

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences A Survey of Diffusion Models in Natural Language Processing

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T05:35:38.926375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d728357-70fa-4ea7-a41b-0acce4ed3511 · inbound

From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks cites this paper.

From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks A Survey of Diffusion Models in Natural Language Processing

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T20:53:58.320164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0baf97f3-ded2-4d62-b8df-1fb6eb6dc9b3 · inbound

Unified Energy for Invariant and Independent Decoding in Diffusion Language Models cites this paper.

Unified Energy for Invariant and Independent Decoding in Diffusion Language Models A Survey of Diffusion Models in Natural Language Processing

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:07:29.808731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1bdd4677-890a-42b8-815f-67bc44623303 · inbound

Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts cites this paper.

Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts A Survey of Diffusion Models in Natural Language Processing

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:18:22.794459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b17d6276-0eb0-4afd-b5bd-c771b3df23b1 · inbound

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger cites this paper.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger A Survey of Diffusion Models in Natural Language Processing

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:39:46.315069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.