Paper Citation Record · LEDGER

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR)

As of 4 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2604.11141.

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

pith.paper-citation-record.v1
2604.11141 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:34:11.393056Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact24
verified fuzzy2
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1209a7d1-5f90-44b9-a6e1-8cdb2385d884 · outbound

This paper cites Universal Self-Consistency for Large Language Model Generation.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Universal Self-Consistency for Large Language Model Generation

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.898152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 26114c27-56ef-476e-8d02-36f738d5381d · outbound

This paper cites DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:21:24.577996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9dba6d25-f4fb-4464-aa26-66286638a9fb · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Training Verifiers to Solve Math Word Problems

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:40:58.890322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d40ff8ae-38ae-4fad-bcf3-668ea42ed403 · outbound

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

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Chain-of-Verification Reduces Hallucination in Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:06:50.701847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 18fdde62-5829-4d8b-8729-c0557b91f5eb · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:01:45.301651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4f6fdc3d-72ee-471d-be0e-157d87299cd9 · outbound

This paper cites Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.886264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 951a5000-0b56-4db0-b7e4-b654371a8b18 · outbound

This paper cites Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Ingest-And-Ground: Dispelling Hallucinations from Continually-Pretrained LLMs with RAG

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.867875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c55da1e-5047-4bbc-b115-d3707dba3c7e · outbound

This paper cites LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.862990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1998b2f0-95fd-4d53-be0d-f3ad3ee94b3b · outbound

This paper cites Privacy Artifact ConnecTor (PACT): Embedding Enterprise Artifacts for Compliance AI Agents.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Privacy Artifact ConnecTor (PACT): Embedding Enterprise Artifacts for Compliance AI Agents

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.924552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eb940c7d-7934-4c13-a389-57ea4b616558 · outbound

This paper cites Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum B ayes Risk Decoding for Machine Translation.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum B ayes Risk Decoding for Machine Translation

Reference 10

Resolution: verified exact; doi, observed 2026-05-10T16:35:35.489919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 86cdcae7-4237-4f31-a5ef-0bd8753338d7 · outbound

This paper cites an unresolved cited work.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-05-17T14:41:53.192580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d42fb58c-0b33-4cfc-bef0-a9e9403479ca · outbound

This paper cites Survey of Hallucination in Natural Language Generation.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Survey of Hallucination in Natural Language Generation

Reference 12

Resolution: verified exact; doi, observed 2026-05-10T16:35:35.488339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c0b067d5-8037-41d9-9a46-d834587c9638 · outbound

This paper cites LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.908052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0bfc0619-0d38-434b-8f29-c2d28a04bb1d · outbound

This paper cites Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T18:00:05.468379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4b4b5c54-0f23-418b-b191-86f01c8b2a8c · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:40:58.842107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 545510d7-6e10-4542-83c6-c5c5245d22bf · outbound

This paper cites Inference-Time Intervention: Eliciting Truthful Answers from a Language Model.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Inference-Time Intervention: Eliciting Truthful Answers from a Language Model

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:21:59.414933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b68723f-206e-4d23-b8ac-819c637aff68 · outbound

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

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-14T00:00:16.565169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e1826237-3635-4c8e-9a79-e3e29f4c8023 · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Lost in the Middle: How Language Models Use Long Contexts

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T08:40:58.752120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 57cb6d5d-c8a6-46b7-99ad-124aa69b98d6 · outbound

This paper cites DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.855396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 56eae473-0c09-43b0-9297-4b014e2ac82c · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-15T15:11:22.649439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ad48257c-2bbc-438a-ac56-79b694408a86 · outbound

This paper cites Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.802969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c4a254bf-c574-44ed-bd40-b882402eb2b1 · outbound

This paper cites Training language models to follow instructions with human feedback.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Training language models to follow instructions with human feedback

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:40:58.808221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c7802480-b33d-4cff-862d-33aad4bd998a · outbound

This paper cites Trusting Your Evidence: Hallucinate Less with Context-aware Decoding.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Trusting Your Evidence: Hallucinate Less with Context-aware Decoding

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.821870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bbabe434-605c-4aa9-8f82-2cf5d744d459 · outbound

This paper cites an unresolved cited work.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-05-17T14:41:53.202441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e7125d8a-ca50-43c0-ade5-6893fb102cff · outbound

This paper cites Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:01:19.882845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ef48c5eb-805c-4ac0-9b45-744023dfb7d7 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-11T08:40:58.782395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d47e2458-c31f-4df1-bcec-0ddc3e397a01 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-12T14:21:16.592379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3fd10f9a-dc04-4649-b328-d243c928a74b · outbound

This paper cites Compliance Brain Assistant: Conversational Agentic AI for Assisting Compliance Tasks in Enterprise Environments.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Compliance Brain Assistant: Conversational Agentic AI for Assisting Compliance Tasks in Enterprise Environments

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:40:58.774960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7412d43b-858a-43bb-b75b-db9a13ae1caf · outbound

This paper cites an unresolved cited work.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-17T14:41:53.199312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7024adf6-75d6-413d-a656-4232c79aa06d · outbound

This paper cites I don't know.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) I don't know

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T14:41:53.195940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 836c5594-9a69-4d57-8648-5bdc85986f51 · outbound

This paper cites truthful.

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR) truthful

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T14:41:53.189326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.