Paper Citation Record · LEDGER

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 6 inbound Pith citation observations for arXiv:2412.06775.

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

pith.paper-citation-record.v1
2412.06775 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:25:43.222317Z

measured 49 of 49 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:32:34.535229Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T08:56:06.356150Z

Reference resolution

43 of 43 outbound references displayed

verified exact2
verified fuzzy21
unresolved20
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f81003c4-eb9b-44d3-b95a-426db803149b · outbound

This paper cites Qwen Technical Report.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Qwen Technical Report

Reference 1

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Source-reported events for the cited work

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Observation 00c7294d-3460-49be-89af-3ab1ed637e56 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

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Observation 62df30b9-d049-4346-8ab8-62cd7c8470b6 · outbound

This paper cites an unresolved cited work.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Unresolved cited work

Reference 3

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Observation 517f5c42-b516-4ecf-baba-4e6026fc226b · outbound

This paper cites HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T19:25:43.096461Z

Source-reported events for the cited work

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Observation 93922246-4e44-4c3f-9cc7-03c0ca023dc0 · outbound

This paper cites Instructblip: Towards general- purpose vision-language models with instruction tuning.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Instructblip: Towards general- purpose vision-language models with instruction tuning

Reference 5

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Observation 7b815f0f-d8b1-4f3d-9857-d44491380db0 · outbound

This paper cites Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding

Reference 6

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Observation f43832ae-4428-4888-ace2-f2fd5a4d313e · outbound

This paper cites Multi-modal hallucination control by visual information grounding.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Multi-modal hallucination control by visual information grounding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.725141Z

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Observation b2e9237c-30fb-4fa7-a169-5f29ee22efdf · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 8

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Observation ea98e691-5f73-42c1-9c38-4b7fb90f7821 · outbound

This paper cites Instructdiffusion: A generalist modeling in- terface for vision tasks.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Instructdiffusion: A generalist modeling in- terface for vision tasks

Reference 9

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Observation bc02ed5b-1846-4ea6-ba46-102392dd5a4b · outbound

This paper cites Detecting and preventing hallucinations in large vision language models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Detecting and preventing hallucinations in large vision language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.701499Z

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Observation 84948a4d-a461-4a2b-9e92-f4cf2989c798 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Denoising dif- fusion probabilistic models

Reference 11

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Observation b78fe7dd-76a9-4bdb-99aa-f1e784112c1c · outbound

This paper cites Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 12

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Observation cd106b2e-f4d4-436b-b522-9cafdb9f0487 · outbound

This paper cites Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 13

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Observation fdb1833b-144d-4e50-9a57-5c730edc5928 · outbound

This paper cites Contrastive decoding: Open-ended text gener- ation as optimization.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Contrastive decoding: Open-ended text gener- ation as optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.666353Z

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Observation c0c474f4-2b89-4a96-8bfe-aeb2bc49a9bc · outbound

This paper cites Evaluating object hallucination in large vision-language models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Evaluating object hallucination in large vision-language models

Reference 15

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Observation cb1469dc-2884-4420-93cb-3839b04fb2ba · outbound

This paper cites Text-driven image editing via learnable regions.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Text-driven image editing via learnable regions

Reference 16

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Observation b8d68aa0-5963-4ebc-a0d2-61937d6bad36 · outbound

This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 17

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Observation cbcb75aa-e72c-48c8-8e20-05c7ad93dce3 · outbound

This paper cites Improved baselines with visual instruction tuning.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Improved baselines with visual instruction tuning

Reference 18

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Observation 86ba8ac8-82d8-4010-8b5f-11610c3101e6 · outbound

This paper cites Clip-dpo: Vision-language models as a source of preference for fixing hallucinations in lvlms.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Clip-dpo: Vision-language models as a source of preference for fixing hallucinations in lvlms

Reference 19

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Observation 88d01faf-908b-4051-86b9-0b52c6809771 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Learn- ing transferable visual models from natural language super- vision

Reference 20

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Observation 7f6ef8f0-253d-4913-a6e1-9d250a35610f · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Direct preference optimization: Your language model is secretly a reward model

Reference 21

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Observation c0a999d9-f1c2-4de5-a36f-300120b60743 · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 22

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Observation 411676ff-41a7-4ceb-99e0-838a4fcab313 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 23

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Observation d4e90c2a-908a-49cd-bfb5-b35d75f245d3 · outbound

This paper cites Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding

Reference 24

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Observation ae7971dd-e756-4e26-806c-a984f267b86f · outbound

This paper cites Pre-Training Multimodal Hallucination Detectors with Corrupted Grounding Data.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Pre-Training Multimodal Hallucination Detectors with Corrupted Grounding Data

Reference 25

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Observation 3360df2a-f264-483b-a0e7-ebf184c12dd5 · outbound

This paper cites mplug- owl2: Revolutionizing multi-modal large language model with modality collaboration.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models mplug- owl2: Revolutionizing multi-modal large language model with modality collaboration

Reference 26

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Observation 28f44c20-793f-4a94-b7c6-05a84db7b8d2 · outbound

This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T19:25:43.171950Z

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Observation a7ab8baf-6144-4cd6-8281-6141fef8bf2e · outbound

This paper cites Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization

Reference 28

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This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems (NeurIPS), 2023.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems (NeurIPS), 2023

Reference 29

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Observation 20098fa2-ecb5-4a83-ba0a-e6e265ed529f · outbound

This paper cites Analyzing and mitigating object hallucination in large vision-language models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Analyzing and mitigating object hallucination in large vision-language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.574964Z

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Observation 68b1fe6a-a384-478b-a78c-31c6aa5c3320 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 31

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This paper cites IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T19:25:43.187709Z

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Observation 831ac5bc-2f96-46d7-bdc8-6803ad0684e1 · outbound

This paper cites Plausibility Constraints Based on the contrastive decoding process from (2) in the main paper, it may penalize the entire LVLM’s responses via contrast samples.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Plausibility Constraints Based on the contrastive decoding process from (2) in the main paper, it may penalize the entire LVLM’s responses via contrast samples

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.563475Z

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Observation 5eb55751-7d34-4c60-878d-00f6257c8ecc · outbound

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Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Unresolved cited work

Reference 34

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Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Unresolved cited work

Reference 35

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Observation fbfba72a-201b-4890-973e-96ac3978e181 · outbound

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Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Unresolved cited work

Reference 36

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Observation c688789c-92c3-4912-a866-9ec0dfa2e164 · outbound

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Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Unresolved cited work

Reference 37

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Observation 0f2c9c0a-708b-46bc-94c9-da7f48462323 · outbound

This paper cites Different from InstructBLIP, the no image method has a much lower PDD value, which could result from the difference in answering tendency (as shown in Figure 10).

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Different from InstructBLIP, the no image method has a much lower PDD value, which could result from the difference in answering tendency (as shown in Figure 10)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.513722Z

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Observation 2d71bfad-baf8-4c3d-957d-6ca73ecbaaaf · outbound

This paper cites yes” while LLaV A-1.5 is prone to answering “no.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models yes” while LLaV A-1.5 is prone to answering “no

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.502328Z

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Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Unresolved cited work

Reference 40

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Observation 20509efa-8636-4451-86d7-41306f20a363 · outbound

This paper cites On the POPE benchmark, the overlap between downsample and diffusion noise is higher.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models On the POPE benchmark, the overlap between downsample and diffusion noise is higher

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:25:43.482952Z

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Observation 8256bca8-8fb4-4f88-b76c-a58583ffaa13 · outbound

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Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models Unresolved cited work

Reference 42

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This paper cites We automatically cap- ture the objects or revise the questions to the affirmative sentences as the editing textual instructions.

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models We automatically cap- ture the objects or revise the questions to the affirmative sentences as the editing textual instructions

Reference 43

Resolution: verified exact; raw_fallback, observed 2026-08-11T19:25:43.326114Z

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Pith citing papers

Observation 18cf72b5-94b0-4ae0-a7ec-10d30ecc1f81 · inbound

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models cites this paper.

Extracting Visual Facts from Intermediate Layers for Mitigating Hallucinations in Multimodal Large Language Models Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

Reference 31

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Observation 6358ed77-3327-433b-ba74-e9eb8a1b005f · inbound

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey cites this paper.

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

Reference 172

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Observation 7cdabcfd-179b-411b-bf3b-dfea25b79a05 · inbound

HTDC: Hesitation-Triggered Differential Calibration for Mitigating Hallucination in Large Vision-Language Models cites this paper.

HTDC: Hesitation-Triggered Differential Calibration for Mitigating Hallucination in Large Vision-Language Models Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

Reference 16

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Observation de348113-2b4f-45c1-825a-7590cb12c68e · inbound

Mitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decoding cites this paper.

Mitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decoding Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

Reference 22

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Observation 54a217d7-584a-4a4a-b167-7ba3f158eb35 · inbound

HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models cites this paper.

HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T08:56:06.357908Z

Source-reported events for the cited work

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Observation b6858d32-aa28-412d-bd2e-cee69eb4cc7c · inbound

VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation cites this paper.

VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-31T02:56:57.075696Z

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