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Paper Citation Record · LEDGER

Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.14233.

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

pith.paper-citation-record.v1
2404.14233 v2

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 2026-08-08T06:32:00.761636+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:27.832973Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:19:30.624198Z

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 a97de5da-d5ed-4d7f-b28e-ca623be17871 · inbound

Hallucination of Multimodal Large Language Models: A Survey cites this paper.

Hallucination of Multimodal Large Language Models: A Survey Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 178

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:33:33.187256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-11T12:33:32.631346Z digest=sha256:4628a0798140f073ad8f4f89fa426969787b98cbf15fbb524a172fd3cab53af0

Observation 36af26ce-af39-450a-aef7-46d7bc745b09 · inbound

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models cites this paper.

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-23T23:58:39.585392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T23:55:57.103971Z digest=sha256:36ba9c4aef9b1547833656b21b62a95b63b9f5115b764947e8a9c435bf9a4d93

Observation 88bb567c-e926-4e7a-b70e-0b643059a907 · inbound

T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts cites this paper.

T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-23T08:02:43.313619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-23T08:00:12.781392Z digest=sha256:2f3cd12a802376550c9bad90f1c350880612173fd45f6fd5284470de7bcc5521

Observation 25f123e3-86f5-451f-b8e5-c850ee76eddb · inbound

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning cites this paper.

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 210

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:32:33.354348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-23T04:30:38.804702Z digest=sha256:1b91e94c5d5b6992061d4f28012ddeed5d2b9289ba7313fc12d8c79cafc3ad14

Observation b83196bf-c051-4d16-b3fa-47a4e986b32d · inbound

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding cites this paper.

Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-07T15:03:27.832973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:03:27.832973Z digest=sha256:37dd6149762a0275f847c29b06055f4883f0849136f4767fb38e3fc761231634

Observation 3f6a8ad6-ab43-4e82-8b61-fcdb73ba4dba · inbound

LPOI: Listwise Preference Optimization for Vision Language Models cites this paper.

LPOI: Listwise Preference Optimization for Vision Language Models Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T13:45:57.781749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:45:57.781749Z digest=sha256:468d313b2b425a0af6b1c947383089ba7860cd7a12c34c4921f544772e8f6384

Observation 3ad55671-c59b-44e9-86e0-16f155a088cc · inbound

Mitigating Object Hallucinations via Sentence-Level Early Intervention cites this paper.

Mitigating Object Hallucinations via Sentence-Level Early Intervention Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 69

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:35:32.542248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T08:31:24.173135Z digest=sha256:9fa20fde6a6094c49f75f21efe9779951eee694cb00bdc7773b6de4a4a292cef

Observation 40d38145-8f16-4a24-9be5-f7d8969a19f7 · inbound

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination cites this paper.

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:16:24.385179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-12T05:15:34.156717Z digest=sha256:f4d17a4f5d46014dffb5f7dc2d1a614eba70631eb183ce1ea65dbc0bf314ffe8

Observation 41f26c5a-03f2-4a08-aba7-6946282bf94f · inbound

Spectral Query-Key Product Weight Steering for Training-Free VLM Hallucination Mitigation cites this paper.

Spectral Query-Key Product Weight Steering for Training-Free VLM Hallucination Mitigation Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:19:30.626874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-26T18:14:49.763342Z digest=sha256:08153d4334587c0199157fd85cd4ea0589f9ec2424b21a8cbca9bdad0e8cec87

Observation 0ba5ce4d-0511-44f2-b9ff-28e9724bce8b · inbound

Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs cites this paper.

Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-02T04:15:38.910266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T04:15:38.910266Z digest=sha256:ea13498be79b8d552ee3ec66318401b168d7ced55c209b0bd64eb0910a3f060b