Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Multi-group Uncertainty Quantification for Long-form Text Generation
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.21057.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:37:54.912234Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T10:37:56.491378Z
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 0022d59b-9c90-4690-be92-147e1d0f787b · inbound
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions cites this paper.
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions Multi-group Uncertainty Quantification for Long-form Text Generation
Reference 130
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:37:54.912234Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0b10dbb-6461-4bda-922b-802b466d79dc · inbound
From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered cites this paper.
From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Multi-group Uncertainty Quantification for Long-form Text Generation
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:38:26.518075Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9dbccb37-645b-4d2d-93cc-6da88b279cc9 · inbound
Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study cites this paper.
Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study Multi-group Uncertainty Quantification for Long-form Text Generation
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T22:16:19.693954Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7d46812-b0df-4ea1-aa45-e98a3aa70052 · inbound
Strategic Decision Support for AI Agents cites this paper.
Strategic Decision Support for AI Agents Multi-group Uncertainty Quantification for Long-form Text Generation
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T10:37:56.492944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 939c9be0-4d0a-4587-adb0-61c859baf372 · inbound
Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation cites this paper.
Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation Multi-group Uncertainty Quantification for Long-form Text Generation
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T20:16:16.264871Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.