Pith. sign in

Paper Citation Record · LEDGER

Generative AI

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2309.07930.

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

pith.paper-citation-record.v1
2309.07930 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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:38:52.599529Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T20:38:53.070413Z

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 03e47ca4-d9ae-4c89-bb14-7f00c9e5c3fd · inbound

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models cites this paper.

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models Generative AI

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:38:53.075900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-10T20:38:52.599529Z digest=sha256:39cf0b4e2fdec6bb8c9029f8561d207797945f58975798fd7a055d118bf6b186

Observation 69155dd6-9fd0-4573-b2d4-e278c0c037be · inbound

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting cites this paper.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Generative AI

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-04T00:32:58.336134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:32:58.336134Z digest=sha256:ec28c97a7a72659cdd897cab80d18eb81c3f04fdd223ae1c0d8818905bf3bb00