Paper Citation Record · LEDGER

LM vs LM: Detecting Factual Errors via Cross Examination

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2305.13281.

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

pith.paper-citation-record.v1
2305.13281 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 2026-08-09T06:31:02.800959+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:33:09.173143Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T02:49:24.815871Z

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 0ab96fcc-e229-4d6b-bc46-3f51ab1e6f1c · inbound

Chain-of-Verification Reduces Hallucination in Large Language Models cites this paper.

Chain-of-Verification Reduces Hallucination in Large Language Models LM vs LM: Detecting Factual Errors via Cross Examination

Reference 133

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:06:50.314126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e7204f27-d9df-4f9a-b50c-038300083fca · inbound

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions cites this paper.

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions LM vs LM: Detecting Factual Errors via Cross Examination

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:46:27.509276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a687471-ec87-4886-ac71-cf5b9d79dc56 · inbound

When One LLM Drools, Multi-LLM Collaboration Rules cites this paper.

When One LLM Drools, Multi-LLM Collaboration Rules LM vs LM: Detecting Factual Errors via Cross Examination

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T22:33:09.173143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0948e69e-ba72-4427-803b-3d784c73f858 · inbound

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

Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations LM vs LM: Detecting Factual Errors via Cross Examination

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T11:23:46.698941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39d90c0d-a8ee-40b2-84ba-03fa3ab5e52a · inbound

Recon, Answer, Verify: Agents in Search of Truth cites this paper.

Recon, Answer, Verify: Agents in Search of Truth LM vs LM: Detecting Factual Errors via Cross Examination

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:13:03.107851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5842b6e-284a-4084-b50b-91ecb1d5a297 · inbound

Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models cites this paper.

Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models LM vs LM: Detecting Factual Errors via Cross Examination

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T05:23:58.876380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc6a5cdc-f467-4901-92a5-4f356a7a366c · inbound

Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses cites this paper.

Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses LM vs LM: Detecting Factual Errors via Cross Examination

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T23:40:02.851978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cb737a0-b2a2-4d02-9f69-79b5f7957ce6 · inbound

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling cites this paper.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling LM vs LM: Detecting Factual Errors via Cross Examination

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T17:41:05.035830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e17f5c6-4a1f-409f-a267-4e8283d8fab4 · inbound

NOMAD: A Multi-Agent LLM System for UML Class Diagram Generation from Natural Language Requirements cites this paper.

NOMAD: A Multi-Agent LLM System for UML Class Diagram Generation from Natural Language Requirements LM vs LM: Detecting Factual Errors via Cross Examination

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T04:59:03.985585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7923c59c-971b-4c60-8c07-517ca62c9f75 · inbound

Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation cites this paper.

Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation LM vs LM: Detecting Factual Errors via Cross Examination

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T06:07:41.396631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7ae44b2b-fdc1-48c9-bd0b-941cad2c5278 · inbound

Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning cites this paper.

Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning LM vs LM: Detecting Factual Errors via Cross Examination

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:49:24.817627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.