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Paper Citation Record · LEDGER

Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.07401.

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

pith.paper-citation-record.v1
2402.07401 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:41:18.674307Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T16:14:53.347282Z

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 d4360884-1e60-4b85-87b2-3f79429a9667 · inbound

Large Language Model Agent: A Survey on Methodology, Applications and Challenges cites this paper.

Large Language Model Agent: A Survey on Methodology, Applications and Challenges Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-05-22T21:52:10.147985Z

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=2026-05-22T21:51:34.309870Z digest=sha256:6a5cb19709bf5fb0831888f91922e68a99081e55773eb13a72dcc675de4e6615

Observation bda3d574-00b0-4f25-8020-014d7b7e12ed · inbound

CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs cites this paper.

CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-16T11:41:18.674307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:41:18.674307Z digest=sha256:3c68b7eadf6268f76d4cdcec91091e38e1ac13b8f56819815ab97975010d4b5d

Observation 5d76203e-21a4-4333-9880-11c17ea751c0 · inbound

EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions cites this paper.

EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T15:00:25.040828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:00:25.040828Z digest=sha256:acbe81fdd5e33ab8b01b150731f2a1a547965b66f3a709d5b370ed33b3d91679

Observation 40bf87cb-0288-42fc-af4a-d8ee532e8112 · inbound

Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Models cites this paper.

Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Models Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T19:51:42.248959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:51:42.248959Z digest=sha256:813da2dc3a8c5a1f278f997432f1360c20c34af5ea5dad2a085f9fcec2aaa41c

Observation 84720870-bf70-414f-b739-f28de0d78088 · inbound

Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents cites this paper.

Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-19T02:52:56.567874Z

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=2026-05-19T02:52:18.207343Z digest=sha256:5022badcb6e0efe95bc763bfaa5f0965281d20e30ba54ae50d301b9c953e7228

Observation 207a5d6d-65c5-4851-9185-d9f0bd2b04ad · inbound

RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild cites this paper.

RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-16T19:31:13.350143Z

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=2026-05-16T19:28:44.365827Z digest=sha256:d2ef67d111b1aa9fb5acc714a146c0ff247d1875a8432950026f557954900d63

Observation 70a01c0c-decc-435a-b677-a4a81d8dac07 · 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 Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 150

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T01:04:50.330378Z

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=sha256:7b3bc1206e9e10d10b38268798ed8b1f924abe9bbb579719eca13934256cccf0

Observation c93445c5-0346-4ce9-adf9-430d7f284754 · inbound

RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild cites this paper.

RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wild Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:41:25.693840Z

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=2026-05-12T05:04:08.747717Z digest=sha256:a29d3b24dc551aa87378338c34d2de9686bd3498b585bddb21bb28547bc1583d

Observation f7d81b8c-3498-4237-9471-08763c42e4d4 · inbound

ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models cites this paper.

ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:15:20.098538Z

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=2026-05-25T04:13:11.756347Z digest=sha256:52a7d5cacdceca448a9a2ae1c85a446621f3ed76497b759e789db446e51647bf

Observation d46f0613-fa2d-4c08-a773-a54bf1b92075 · inbound

ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models cites this paper.

ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-06-30T16:14:53.349076Z

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=2026-06-30T16:10:01.774725Z digest=sha256:be0dee239338c56dcb9d1141030baff46b35ccc00b5163b7d4e70cbb70744552