Paper Citation Record · LEDGER

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models

As of 14 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2411.16189.

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

pith.paper-citation-record.v1
2411.16189 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:29:48.353907Z

measured 19 of 19 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:11:10.561010Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:11:11.378980Z

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy12
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 97cb27f0-bab3-4fca-8e6c-bbbc647d6289 · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:48.193836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37e84f7b-ad91-4229-8a36-fcf226994a99 · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:48.219808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c301a4a-b5ef-46e4-baa4-84d8547ec3b8 · outbound

This paper cites DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:48.242502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 397c629d-7d16-4f47-974d-2759f419f19c · outbound

This paper cites Unsupervised quality estimation for neural machine translation.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Unsupervised quality estimation for neural machine translation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.652721Z

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.

Observation fac66f6f-83f7-45a6-a846-d60637c12660 · outbound

This paper cites DEUP: Direct Epistemic Uncertainty Prediction.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models DEUP: Direct Epistemic Uncertainty Prediction

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:48.278789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed768195-3fcb-438f-a637-3c29375d484a · outbound

This paper cites Uncertainty estimation and reduction of pre-trained models for text regression.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Uncertainty estimation and reduction of pre-trained models for text regression

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.640434Z

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.

Observation cb40176e-8466-4834-922a-dcb3fe4f754f · outbound

This paper cites Language models are unsupervised multitask learners.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Language models are unsupervised multitask learners

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.627141Z

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.

Observation a382527e-417a-40ba-9cff-be95aee0b3a6 · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Llama 2: Open foundation and fine-tuned chat models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.613582Z

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.

Observation cd01d538-5738-44b0-863d-8f90cd4b2ee5 · outbound

This paper cites Mistral 7B.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Mistral 7B

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:48.311097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 246809c5-246d-4999-af11-50ade06a429e · outbound

This paper cites Teaching models to express their uncertainty in words.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Teaching models to express their uncertainty in words

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.600927Z

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.

Observation 6af14b3c-1c7a-450d-a4c1-abd74d8b45b4 · outbound

This paper cites Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.588804Z

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.

Observation 10f025dc-2eed-466b-a6df-f95f85ba2834 · outbound

This paper cites ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T13:29:48.326792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 647519fb-e549-4345-b990-e0f384476b63 · outbound

This paper cites Metagpt: Meta programming for multi-agent collaborative framework.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Metagpt: Meta programming for multi-agent collaborative framework

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.576500Z

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.

Observation a91416d0-b005-42fc-9db8-330fec33441e · outbound

This paper cites A survey on large language model based autonomous agents.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models A survey on large language model based autonomous agents

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.564579Z

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.

Observation 6b8498f8-09ce-42bb-b69a-19cb0c50409d · outbound

This paper cites An intelligent llm-powered personalized assistant for digital banking using langgraph and chain of thoughts.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models An intelligent llm-powered personalized assistant for digital banking using langgraph and chain of thoughts

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.542440Z

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.

Observation c0999a56-3599-4c9f-a67c-bd06e0bd60f0 · outbound

This paper cites Venkadesh, S.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Venkadesh, S

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.508911Z

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.

Observation 1fec4fbe-1cc7-4eba-bb81-a99831799001 · outbound

This paper cites Attention is all you need.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Attention is all you need

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.471708Z

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.

Observation cca115ed-c8e3-48b8-a16d-139485edb96f · outbound

This paper cites Ernie 2.0: A continual pre-training framework for language understanding.

Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models Ernie 2.0: A continual pre-training framework for language understanding

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:29:48.457409Z

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.

Pith citing papers

Observation 8068ce89-8130-4a08-a8d3-3cc7ebefe6e5 · inbound

CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate cites this paper.

CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:11:11.386111Z

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.