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Paper Citation Record · LEDGER

Adaptive Uncertainty Quantification for Generative AI

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.08990.

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

pith.paper-citation-record.v1
2408.08990 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:24:05.304277Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T03:15:21.277063Z

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 6a007392-6a87-4b51-817b-399763c2e83c · inbound

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective cites this paper.

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective Adaptive Uncertainty Quantification for Generative AI

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-23T03:15:21.279564Z

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.

source=arxiv_source observed=2026-05-23T03:14:00.526112Z digest=sha256:d4c08de3355dad7e2c8f88d02d0b5db98431d055c718738f685dca35f8b42dfe

Observation a956ee7e-a413-4fac-851a-9ec6782c1c3a · inbound

Generative AI for Autonomous Driving: A Review cites this paper.

Generative AI for Autonomous Driving: A Review Adaptive Uncertainty Quantification for Generative AI

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-07T15:24:05.304277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:24:05.304277Z digest=sha256:eb972ba69a3098befaa88fae74b328f1ba4f28837086a334d10445ca4693310d

Observation 583ee6e9-fbd5-44d1-89ce-6c7efdc82fe4 · inbound

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression cites this paper.

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression Adaptive Uncertainty Quantification for Generative AI

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T21:45:10.306354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:45:10.306354Z digest=sha256:24be3a724d921d9e16cdc3fbeea084b252983c06e356fdee1484bc50ee3c774f