Paper Citation Record · LEDGER

Large language models can consistently generate high-quality content for election disinformation operations

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2408.06731.

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

pith.paper-citation-record.v1
2408.06731 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:58:26.987039Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T00:31:24.709229Z

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 f98931a6-9a99-464b-a876-0c03e8606c92 · inbound

Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation cites this paper.

Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation Large language models can consistently generate high-quality content for election disinformation operations

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T12:58:26.987039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e887c03-b0fa-4061-a7de-47257231c8b8 · inbound

LLM Harms: A Taxonomy and Discussion cites this paper.

LLM Harms: A Taxonomy and Discussion Large language models can consistently generate high-quality content for election disinformation operations

Reference 162

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:31:24.712735Z

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.

Observation db824a2a-0826-47b7-a0de-2e44af38734e · inbound

LLM Harms: A Taxonomy and Discussion cites this paper.

LLM Harms: A Taxonomy and Discussion Large language models can consistently generate high-quality content for election disinformation operations

Reference 154

Resolution: unresolved; no resolver link, observed 2026-08-03T18:19:25.066188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 063938aa-8752-4b54-8123-8f812d96d1fc · inbound

SWAN: Semantic Watermarking with Abstract Meaning Representation cites this paper.

SWAN: Semantic Watermarking with Abstract Meaning Representation Large language models can consistently generate high-quality content for election disinformation operations

Reference 63

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:51:09.606352Z

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.