Paper Citation Record · LEDGER

Assessing and Understanding Creativity in Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2401.12491.

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

pith.paper-citation-record.v1
2401.12491 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 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:07:25.450652Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:56:21.288304Z

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 c005c9ce-927a-41a9-a49f-f7ab3b8cf065 · inbound

Dynamic Reinforcement Learning for Actors cites this paper.

Dynamic Reinforcement Learning for Actors Assessing and Understanding Creativity in Large Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T19:07:25.450652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9aed053-a45f-4e13-aa44-1d9657529b80 · inbound

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery cites this paper.

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery Assessing and Understanding Creativity in Large Language Models

Reference 130

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:38.831145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f285043-ceae-4a7e-b7ac-a08237cb27f7 · inbound

THiNK: Can Large Language Models Think-aloud? cites this paper.

THiNK: Can Large Language Models Think-aloud? Assessing and Understanding Creativity in Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:35.188891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc64f3fb-322c-4ce4-ad8b-9a821d7efead · inbound

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials cites this paper.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Assessing and Understanding Creativity in Large Language Models

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:56:21.335575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.