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Paper Citation Record · LEDGER

Graph Neural Prompting with Large Language Models

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2309.15427.

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

pith.paper-citation-record.v1
2309.15427 v2

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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:38:54.328015Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T18:34:17.947245Z

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 8dc09212-dafe-4425-9808-f2d46d9ed581 · inbound

Efficient Document Retrieval with G-Retriever cites this paper.

Efficient Document Retrieval with G-Retriever Graph Neural Prompting with Large Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-16T11:38:54.328015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:38:54.328015Z digest=sha256:a0734de2391c4ab65e4ff9b865afec4762ab2b572a193e661fce8ae6bc929f5a

Observation 409a880e-031e-4be1-866e-44695bc0063d · inbound

GraphMind: Theorem Selection and Conclusion Generation Framework with Dynamic GNN for LLM Reasoning cites this paper.

GraphMind: Theorem Selection and Conclusion Generation Framework with Dynamic GNN for LLM Reasoning Graph Neural Prompting with Large Language Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-21T18:34:17.948909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-21T18:31:46.510611Z digest=sha256:51faf624b64fb0d40e5f31cc58885fe0fa86e85984136590c1148d52ba459d42