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Paper Citation Record · LEDGER

Memory-Driven Text-to-Image Generation

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2208.07022.

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

pith.paper-citation-record.v1
2208.07022 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:42:11.634344Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T13:32:17.357702Z

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 599a9a48-42c2-425e-b1eb-13a6994510a3 · inbound

Retrieval-Augmented Generation for AI-Generated Content: A Survey cites this paper.

Retrieval-Augmented Generation for AI-Generated Content: A Survey Memory-Driven Text-to-Image Generation

Reference 107

Resolution
verified exact
arxiv_id, observed 2026-05-15T13:32:17.359146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-15T13:32:17.177021Z digest=sha256:b54e8801d3f4695a352a90eb431f2edd94b34132ff23aa5f7291cc01367dfcc5

Observation 9817f8ec-460c-4afb-8a60-9a47f2792294 · inbound

GMem: A Modular Approach for Ultra-Efficient Generative Models cites this paper.

GMem: A Modular Approach for Ultra-Efficient Generative Models Memory-Driven Text-to-Image Generation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T17:42:11.634344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:42:11.634344Z digest=sha256:518fda13491be52b544c926ab5f4083698e1e32a64219edb47572a363feb8230