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Paper Citation Record · LEDGER

Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2411.06535.

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

pith.paper-citation-record.v1
2411.06535 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:20:17.100355Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T14:06:37.951157Z

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 f8771898-a9fa-4b81-966d-fd942435a37a · inbound

Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models cites this paper.

Enhancing Answer Reliability Through Inter-Model Consensus of Large Language Models Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-12T13:21:28.112281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:21:28.112281Z digest=sha256:931b1b5fe67b718fd6b5ef3bf31a75268df78cf4133cf1faa2f9a60de0d52db8

Observation a628ed8e-70e6-4b52-808c-d638c1a63117 · inbound

LLM-Powered AI Agent Systems and Their Applications in Industry cites this paper.

LLM-Powered AI Agent Systems and Their Applications in Industry Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability

Reference 102

Resolution
verified exact
arxiv_id, observed 2026-05-22T14:06:37.954495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-22T14:05:54.535411Z digest=sha256:420e89b8879c487550969aa5de2526e300f43e162c5515c955d512042c71e4d6

Observation d4687e84-398f-4c82-9939-7cd4c7710235 · inbound

Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling cites this paper.

Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:55:37.950020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-15T12:52:15.996149Z digest=sha256:c25f1d13199bc5076e5db4c363d39eb034124ee11d0388454b92c8da146fb771

Observation 0b46bdb6-2ed1-42ae-8c2d-1003d0472b85 · inbound

Multi-LLM Consensus Framework for Evaluating Banking-Sector NIDS Dataset Coverage of MITRE ATT&CK Techniques cites this paper.

Multi-LLM Consensus Framework for Evaluating Banking-Sector NIDS Dataset Coverage of MITRE ATT&CK Techniques Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T15:20:17.100355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:20:17.100355Z digest=sha256:f6382c76242999dae0c58720ab1b1d70d16b88b20e5abad40af8ab106be5a6ab