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Paper Citation Record · LEDGER

Fine-Tuning Language Models via Epistemic Neural Networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.01568.

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

pith.paper-citation-record.v1
2211.01568 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 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:31:49.642330Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T20:37:55.847314Z

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 fdefa532-428c-4ed8-a321-37033282b9d6 · 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 Fine-Tuning Language Models via Epistemic Neural Networks

Reference 160

Resolution
verified exact
local_arxiv, observed 2026-08-11T20:37:55.850875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T20:37:55.007298Z digest=sha256:2236baa73767c8506e3095911810f16c5ee470b96b04c4272beba6e15df48504

Observation 6a40973f-2b54-4348-a648-22555657e211 · inbound

Random-Set Large Language Models cites this paper.

Random-Set Large Language Models Fine-Tuning Language Models via Epistemic Neural Networks

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-16T10:31:49.642330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T10:31:49.642330Z digest=sha256:02ed13c323d2d2ce286be0169124e31ddd4120755550f897392c5ccb08dfea68

Observation 251755a5-bb8f-4efd-923d-3705f615a417 · inbound

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know' cites this paper.

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know' Fine-Tuning Language Models via Epistemic Neural Networks

Reference 175

Resolution
unresolved
no resolver link, observed 2026-08-15T23:21:13.721643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:21:13.721643Z digest=sha256:ba2f53f613cb4b200202efe874c14f621b2c467190b945ba1e9b21234f257e26