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Paper Citation Record · LEDGER

Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.18048.

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

pith.paper-citation-record.v1
2402.18048 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:35:43.974468Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:22:33.270277Z

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 9b43a599-8375-4fad-bed7-6901bff0f610 · inbound

Aligning Large Language Models for Faithful Integrity Against Opposing Argument cites this paper.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:43.974468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.974468Z digest=sha256:53cbbcf2c1a6c0f9ac1f6c092b48d98ae4ab26d592843b49966381d3e0eb72ff

Observation 83fc8e36-c515-4ea8-8b13-6fd684cbbffb · inbound

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations cites this paper.

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T11:23:45.108804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:23:45.108804Z digest=sha256:2a9db472bf8f58fabe06025059f9b8b4928b6a39c4825f4cc9fbc20c3f32cefc

Observation 078e2312-3137-4546-9d68-36e378522202 · inbound

Neural Message-Passing on Attention Graphs for Hallucination Detection cites this paper.

Neural Message-Passing on Attention Graphs for Hallucination Detection Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-04T13:52:12.846283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T13:52:12.846283Z digest=sha256:13754ebb7d9ced0c8fbaff41f3f5ee97010551b66f10e36addd1977d44c0351f

Observation 69835344-27f4-4230-9f58-17f9b218a052 · inbound

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension cites this paper.

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:22:33.273011Z

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.

source=pdf_text observed=2026-05-18T10:22:28.096542Z digest=sha256:87ded77e0d345d48e24ff416182c37c0597908a754f9d70e84511f95142c5e62

Observation 635985ab-d5ac-4a52-86d2-2c5fcd355c6a · inbound

Large Vision-Language Models Get Lost in Attention cites this paper.

Large Vision-Language Models Get Lost in Attention Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:26:10.184826Z

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.

source=arxiv_source observed=2026-05-08T11:54:01.224588Z digest=sha256:b25749c965097d360202378e4c707e57cae32b49baa62d6674be1e4eab86ee5d