Paper Citation Record · LEDGER

Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2410.22685.

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

pith.paper-citation-record.v1
2410.22685 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:16:23.746023Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:39:30.634270Z

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 3bdee7e4-02c4-4283-8e99-6d1b6eb55e39 · inbound

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems cites this paper.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T09:22:15.886714Z

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 7f24faa4-1c2d-4d4c-a4d1-fdc7cb4758ce · inbound

Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes cites this paper.

Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T16:16:23.746023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31cfdafd-3931-4965-ac1f-bd860cde78df · inbound

HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering cites this paper.

HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T13:21:32.596804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fc79eea-5df2-49c6-9f29-a8b2f2cadef4 · inbound

Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study cites this paper.

Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Study Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:50:09.200418Z

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 68029dea-0e50-44fc-ba88-8ba510a284ad · inbound

Sanity Checks for Long-Form Hallucination Detection cites this paper.

Sanity Checks for Long-Form Hallucination Detection Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:06:28.327192Z

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 b535c680-021b-4bf5-8c0b-d0e4b5a4c418 · inbound

Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence? cites this paper.

Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence? Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:23:24.359079Z

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 5607d830-07b4-4231-9eb0-d4134f437775 · inbound

Conf-Gen: Conformal Uncertainty Quantification for Generative Models cites this paper.

Conf-Gen: Conformal Uncertainty Quantification for Generative Models Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:03:29.732936Z

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 ae02b9ce-036a-4b09-ab0f-eeec19d38193 · inbound

Uncertainty Decomposition for Clarification Seeking in LLM Agents cites this paper.

Uncertainty Decomposition for Clarification Seeking in LLM Agents Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:59:21.334832Z

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 c166cf2e-285d-4e71-a135-ec8d4e26d9a2 · inbound

A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models cites this paper.

A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:39:30.637380Z

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 359aeea1-93ff-4fda-a66c-3bff58066147 · inbound

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs cites this paper.

Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:35:42.120441Z

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 6fe06fc0-7798-4846-bd23-464cd56ce72e · inbound

Calibrating Semantic Uncertainty from Observable Language-Model Probabilities cites this paper.

Calibrating Semantic Uncertainty from Observable Language-Model Probabilities Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T18:00:30.693187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.