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Paper Citation Record · LEDGER

Confidence in the Reasoning of Large Language Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.15296.

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

pith.paper-citation-record.v1
2412.15296 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:14:52.063511Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T05:37:36.077846Z

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 20eaaa64-2e50-469b-beeb-6403e792bc24 · inbound

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong cites this paper.

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong Confidence in the Reasoning of Large Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-23T05:37:36.081259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-23T05:37:22.955895Z digest=sha256:f2b07c408cb64d8bf199e2d9359e3b860112d16ee60a6e59a614d4b64953326c

Observation 925c3100-9e08-4ad1-b657-35d777dc3909 · inbound

From Divergence to Consensus: Evaluating the Role of Large Language Models in Facilitating Agreement through Adaptive Strategies cites this paper.

From Divergence to Consensus: Evaluating the Role of Large Language Models in Facilitating Agreement through Adaptive Strategies Confidence in the Reasoning of Large Language Models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-09T16:14:52.063511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T16:14:52.063511Z digest=sha256:f8a4f051a937bb1d579cee8be373a26955046c8cf4079a06c8764be661171894