Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.11278.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:25:22.520615Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T06:22:38.200397Z
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 7dda602a-ab1a-4fee-a7f3-590d06b6e1cd · inbound
Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure cites this paper.
Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T06:22:38.206734Z
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.
Observation 05c2a8ae-f790-43a4-be6b-c416339b4d11 · inbound
Towards Harmonized Uncertainty Estimation for Large Language Models cites this paper.
Towards Harmonized Uncertainty Estimation for Large Language Models Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:25:22.520615Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68ad06ee-ffc7-463b-86b8-27fd5093c1f5 · inbound
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics cites this paper.
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:07:30.848417Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c114b9ee-6a43-4818-9e8f-68790a2eb0c6 · inbound
Un-considering Contextual Information: Assessing LLMs' Understanding of Indexical Elements cites this paper.
Un-considering Contextual Information: Assessing LLMs' Understanding of Indexical Elements Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:58:29.891157Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 982491e1-574d-43ff-a895-e4e2a825dd88 · inbound
Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning cites this paper.
Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:25:58.696341Z
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.