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Paper Citation Record · LEDGER

Zero-Resource Hallucination Prevention for Large Language Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2309.02654.

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

pith.paper-citation-record.v1
2309.02654 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:21:42.261692Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T01:05:16.553329Z

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 c6124468-7436-416c-8953-c96afa385684 · inbound

A Survey of Hallucination in Large Foundation Models cites this paper.

A Survey of Hallucination in Large Foundation Models Zero-Resource Hallucination Prevention for Large Language Models

Reference 135

Resolution
verified exact
arxiv_id, observed 2026-05-16T15:21:00.954046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-16T15:21:00.778049Z digest=sha256:7c81d5c350ee196072bd83da00cad6121b7a4bbc283cf4534542d787e34edb0b

Observation f8bbb785-fbe7-4dac-a26e-f48f5f8b2ad6 · 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 Zero-Resource Hallucination Prevention for Large Language Models

Reference 207

Resolution
verified exact
arxiv_id, observed 2026-05-13T02:46:27.790036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-13T02:46:26.957539Z digest=sha256:818e42f70ac7a65b2f4d00e6858bf0b0c34ce17cc77d2573f0715ba059e90af1

Observation 3cef1b22-d5df-40c3-8fac-69d24a0a9fd8 · inbound

Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs cites this paper.

Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs Zero-Resource Hallucination Prevention for Large Language Models

Reference 39

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T00:52:02.542357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-18T00:52:02.421389Z digest=sha256:59aea9589b1347caaa0e59a66ee8b88dd7631e65bec5034f26a2ee4dc42f2a8b

Observation 218b2513-2e67-4d29-8ca2-82789d5918fb · inbound

Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models cites this paper.

Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models Zero-Resource Hallucination Prevention for Large Language Models

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T23:21:42.261692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:21:42.261692Z digest=sha256:3b54c65d1d20fed688433c5355cb5559aaaa0a1d15709b7075c5e0fadadc3a98

Observation 3b951f86-ae35-4774-a481-4e680b7d9d56 · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Zero-Resource Hallucination Prevention for Large Language Models

Reference 164

Resolution
verified exact
arxiv_id, observed 2026-05-23T01:05:16.555793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-23T01:03:26.037233Z digest=sha256:f5efb45ef8eaa6092ff41dd18c2b90c1d2433cdb9bc9845a8ba7c07e985b4f31

Observation 6c3d9d33-08a1-4979-8d6f-5f644730bd46 · inbound

Pierce the Mists, Greet the Sky: Decipher Knowledge Overshadowing via Knowledge Circuit Analysis cites this paper.

Pierce the Mists, Greet the Sky: Decipher Knowledge Overshadowing via Knowledge Circuit Analysis Zero-Resource Hallucination Prevention for Large Language Models

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T15:39:17.661094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:39:17.661094Z digest=sha256:7596a4472b57c1cea604a48fd7c1bfd693db1ec2327fd4b8c138541b9293af27

Observation f54e3a99-6d1a-4e6f-a624-1da69c3f0ec6 · inbound

RoE-FND: A Case-Based Reasoning Approach with Dual Verification for Fake News Detection via LLMs cites this paper.

RoE-FND: A Case-Based Reasoning Approach with Dual Verification for Fake News Detection via LLMs Zero-Resource Hallucination Prevention for Large Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:06.886309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:07:06.886309Z digest=sha256:2ebc093bb46452b898036f402aaa369409f8bd344716f2a14ae2a48f8608662c

Observation 4d25b101-aacd-4ad6-9bd9-0ed4e5fe2151 · inbound

A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models cites this paper.

A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models Zero-Resource Hallucination Prevention for Large Language Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T20:03:49.053574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:03:49.053574Z digest=sha256:60a87cb1bf6d580368c6c4d2fced1c89170d6d28d5d26187e384c2973a1e1cc3