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Paper Citation Record · LEDGER

Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2310.12558.

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

pith.paper-citation-record.v1
2310.12558 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:29:40.051177Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T22:40:43.442740Z

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 11270c59-8609-4c06-86b4-3fee55ede431 · inbound

The Prompt Report: A Systematic Survey of Prompt Engineering Techniques cites this paper.

The Prompt Report: A Systematic Survey of Prompt Engineering Techniques Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T02:16:17.904569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-15T02:16:17.875268Z digest=sha256:19e94c36a9d4af7fe7c024c05c4447d87d6075479fc0960c86c0d3fc0db6fc7e

Observation 1675f972-7cf2-4a9c-b2af-aa930c7e7277 · inbound

2-Factor Retrieval for Improved Human-AI Decision Making in Radiology cites this paper.

2-Factor Retrieval for Improved Human-AI Decision Making in Radiology Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T05:29:40.051177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:29:40.051177Z digest=sha256:a70200794352be92061174287438fb6be2852e4ebc9f51a20186789d8b67ece2

Observation c74eb6c4-5ff9-45d8-916a-5f207499ad82 · inbound

GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Exploration cites this paper.

GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Exploration Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-10T14:57:05.737005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:57:05.737005Z digest=sha256:15eb3874750d8844dbd1a835c789321b76b3e064fb1d633a1a3fd44aca1854d7

Observation 311a8851-592a-44ec-9d78-05e9117f7428 · inbound

TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information cites this paper.

TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-10T14:43:55.618908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:43:55.618908Z digest=sha256:fb7fff2c34cb7e3d7d96db976dde128b75539d76f15921a85858fbe4bbb5173d

Observation 2cd5e4bc-436f-4365-9b47-c76d90088f13 · inbound

Statistical Hypothesis Testing for Auditing Robustness in Language Models cites this paper.

Statistical Hypothesis Testing for Auditing Robustness in Language Models Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T05:28:11.165754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:28:11.165754Z digest=sha256:6deed76d25eaf2e2ee5df8e66c758b5c996aa7de3d04f8665470a79c5f7a9c91

Observation bebfc0f7-16b3-47bd-b55b-aae7bfb2e248 · inbound

Measuring and mitigating overreliance to build human-compatible AI cites this paper.

Measuring and mitigating overreliance to build human-compatible AI Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 109

Resolution
verified exact
arxiv_id, observed 2026-05-21T22:40:43.444917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-21T22:37:37.267715Z digest=sha256:2399d90d6547977976aaddb000fff7069088a683e631e0c426edcb2cbcdd0df1

Observation e014aa36-9a6b-455b-bd2c-c02696c80c6c · inbound

VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization cites this paper.

VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-18T07:11:04.081503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-18T07:08:14.700656Z digest=sha256:3dead94fae791efef66e5a885fd7708f4a2986b793d69a1503df5b9cd3314617

Observation cb50a5f2-b020-4e1f-916d-52e06b00287f · inbound

Human-AI Complementarity: A Goal for Amplified Oversight cites this paper.

Human-AI Complementarity: A Goal for Amplified Oversight Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-04T07:21:22.538224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:21:22.538224Z digest=sha256:7daa3caf8a7d9b77427a9345a224c20b45c0a712a5a8388a534913db515ce7c9

Observation e8af95be-2c17-431e-8e25-b187e5d37fbc · inbound

Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives cites this paper.

Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong

Reference 149

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T01:04:50.349259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-10T01:00:41.543394Z digest=sha256:53887aa1364275c4ef285942c35aafa5fae9ef3fec1fbdd87d25bad956ae9f02