Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Uncertainty Quantification for In-Context Learning of Large Language Models
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.10189.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T23:05:00.401365Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T19:16:00.104174Z
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 9f88eef8-ca47-48ee-89fd-baf571793d46 · inbound
TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning cites this paper.
TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning Uncertainty Quantification for In-Context Learning of Large Language Models
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T14:01:38.496532Z
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.
Observation fbc47071-af24-43e3-9562-03ab0db3c823 · inbound
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems cites this paper.
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Uncertainty Quantification for In-Context Learning of Large Language Models
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T09:22:15.916798Z
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.
Observation dd1deed7-e547-490c-9eab-f4d9d966a7d8 · inbound
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not cites this paper.
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not Uncertainty Quantification for In-Context Learning of Large Language Models
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T19:16:00.105687Z
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.