Paper Citation Record · LEDGER

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2504.15619.

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

pith.paper-citation-record.v1
2504.15619 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:27:36.967307Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:32:15.287630Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:54:20.297814Z

Reference resolution

44 of 44 outbound references displayed

verified exact0
verified fuzzy20
unresolved24
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 093899e6-7095-4e10-b223-4ddb7bcf3d23 · outbound

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

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

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Observation 92150601-f9d1-4c86-bdd9-6a97ca3e59e7 · outbound

This paper cites Rank analysis of incomplete block designs: I.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Rank analysis of incomplete block designs: I

Reference 2

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Observation 719192e5-89df-4588-bac2-e43d57f8e384 · outbound

This paper cites End-to- end object detection with transformers.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization End-to- end object detection with transformers

Reference 3

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Observation d81c4bbe-ef1f-4e95-a20b-9f4be29fe3b7 · outbound

This paper cites On Softmax Direct Preference Optimization for Recommendation.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization On Softmax Direct Preference Optimization for Recommendation

Reference 4

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Observation bb9c2a54-d787-4175-94d9-5ca5817a5c73 · outbound

This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 5

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Observation d04cb186-8fa6-46a8-80e5-ebef887297ef · outbound

This paper cites Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:36.799901Z

Source-reported events for the cited work

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Observation 3a17869f-2166-42cf-8746-51fd6f8a1e80 · outbound

This paper cites The Llama 3 Herd of Models.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization The Llama 3 Herd of Models

Reference 7

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Observation 95945a37-ba21-4a3d-baa5-fdab24ce670e · outbound

This paper cites Token pref- erence optimization with self-calibrated visual-anchored rewards for hallucination mitigation.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Token pref- erence optimization with self-calibrated visual-anchored rewards for hallucination mitigation

Reference 8

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Observation a6d22f44-da87-4636-b64c-3c08af0b715a · outbound

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

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.650484Z

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Observation d8808e51-ce19-4561-ba05-82f0bd35a36f · outbound

This paper cites Vcoder: Ver- satile vision encoders for multimodal large language models.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Vcoder: Ver- satile vision encoders for multimodal large language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.636582Z

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Observation eed9db34-3573-4c79-9481-0aebebfa17fd · outbound

This paper cites Segment any- thing.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Segment any- thing

Reference 11

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Source-reported events for the cited work

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Observation 7a39659f-7ac2-4425-9003-a4517094f08f · outbound

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

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.613634Z

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Observation 5a629912-dca5-4e7f-9a44-ec6495c14280 · outbound

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

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.600403Z

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Observation 4934d308-8fd1-44a2-b4de-e18754222cd3 · outbound

This paper cites Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.586286Z

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Observation 82e1dca7-42f8-4e1b-a925-a5a10ba7aaf6 · outbound

This paper cites Visual instruction tuning.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Visual instruction tuning

Reference 15

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Observation 424ef249-6884-4c4d-884f-fb2f98b8c06c · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization A Survey on Hallucination in Large Vision-Language Models

Reference 16

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Observation 130569f9-1b16-4e9e-8e42-4c1bcff895e6 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 17

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Observation 5f4a199b-33f3-482a-8863-026fdecfdc87 · outbound

This paper cites DAMA: Data- and Model-aware Alignment of Multi-modal LLMs.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization DAMA: Data- and Model-aware Alignment of Multi-modal LLMs

Reference 18

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Observation 125a8a78-1c1b-4aac-af6b-4f56387f5b13 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 19

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Observation 23502d6e-736e-4c68-87de-ac85f7769eb8 · outbound

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

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Training language models to follow instructions with human feedback

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.552804Z

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Observation adad47df-3b0a-4954-9301-9987268c5d02 · outbound

This paper cites The analysis of permutations.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization The analysis of permutations

Reference 21

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Observation dd965f93-ac32-4fd8-8348-4841393b0271 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Learning transferable visual models from natural language supervi- sion

Reference 22

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Observation e26f40fc-0a66-49a1-8279-52db45feb442 · outbound

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

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Direct preference optimization: Your language model is secretly a reward model

Reference 23

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Observation e19d75d0-46dc-46b5-b2e6-f4718afbb906 · outbound

This paper cites Object Hallucination in Image Captioning.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Object Hallucination in Image Captioning

Reference 24

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Observation 3f03910b-e67d-4962-94b5-a4567d505901 · outbound

This paper cites Proximal Policy Optimization Algorithms.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Proximal Policy Optimization Algorithms

Reference 25

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Observation 37f7f5e2-deb2-4bf3-a4a3-a7ad16d48696 · outbound

This paper cites Objects365: A large-scale, high-quality dataset for object detection.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Objects365: A large-scale, high-quality dataset for object detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.501824Z

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Observation 1214fcfc-d56e-4ed2-9e45-9a34c45751bf · outbound

This paper cites Aligning large multi- modal models with factually augmented rlhf.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Aligning large multi- modal models with factually augmented rlhf

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.487560Z

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Observation ee3e42fc-198b-4fbc-baa8-8b677aebf1ee · outbound

This paper cites Resolution-robust large mask inpainting with fourier convolutions.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Resolution-robust large mask inpainting with fourier convolutions

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.473590Z

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Observation c1c23691-359e-4934-86a2-7da63f6ed46c · outbound

This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Eyes wide shut? exploring the visual shortcomings of multimodal llms

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.459135Z

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Observation 2d99a1ee-41d2-498b-9578-1562c7ca1bd3 · outbound

This paper cites mDPO: Conditional Preference Optimization for Multimodal Large Language Models.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Reference 30

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Observation 7de03321-57d7-460a-8924-106a1a52e5c4 · outbound

This paper cites AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 31

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Observation 5f2e13fd-6b91-40ca-b3e9-fb4316cd8b43 · outbound

This paper cites V-dpo: Mitigating hallucination in large vision language models via vision-guided direct preference optimization.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization V-dpo: Mitigating hallucination in large vision language models via vision-guided direct preference optimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.445952Z

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Observation b1706a88-95c2-4dc8-91d8-c7a31db28773 · outbound

This paper cites Miti- gating object hallucination via concentric causal attention.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Miti- gating object hallucination via concentric causal attention

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.433369Z

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Observation 543e52e2-b383-42e1-b31c-521ecae710f5 · outbound

This paper cites Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.419164Z

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Observation 1b7b53f2-0ecd-44f9-8737-94a9d76baf2e · outbound

This paper cites Gradient surgery for multi-task learning.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Gradient surgery for multi-task learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.403148Z

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Observation 6ac5f39a-6808-4e98-a437-78bec96e61a2 · outbound

This paper cites Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional hu- man feedback.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional hu- man feedback

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.389025Z

Source-reported events for the cited work

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Observation a5b3b0f2-d9a5-4cd6-b1e4-0139ba3583ec · outbound

This paper cites Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness

Reference 37

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Observation 73ad28ea-13da-4df4-b350-6544a29966c6 · outbound

This paper cites Less is more: Mitigat- ing multimodal hallucination from an eos decision perspec- tive.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Less is more: Mitigat- ing multimodal hallucination from an eos decision perspec- tive

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.373451Z

Source-reported events for the cited work

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Observation 45faf425-0cfa-479f-8119-e2b3bd570cd4 · outbound

This paper cites Dino: Detr with improved denoising anchor boxes for end-to-end object de- tection.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Dino: Detr with improved denoising anchor boxes for end-to-end object de- tection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.359000Z

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Observation 01843850-9619-4c58-b617-ebe5e29f0e00 · outbound

This paper cites Automated multi-level preference for mllms.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Automated multi-level preference for mllms

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:27:37.344886Z

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Observation 44b884ca-911e-4888-bdc4-d7fd8c394d63 · outbound

This paper cites Recognize Anything: A Strong Image Tagging Model.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Recognize Anything: A Strong Image Tagging Model

Reference 41

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Observation 692784a0-41d9-449d-ae1a-f18255bc8761 · outbound

This paper cites Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance

Reference 42

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Observation e37eccb4-077b-490d-8871-adb8cb90c6ee · outbound

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

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization

Reference 43

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Observation 332e1d13-7462-41bc-a4f0-d5d64fe41616 · outbound

This paper cites Aligning Modalities in Vision Large Language Models via Preference Fine-tuning.

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization Aligning Modalities in Vision Large Language Models via Preference Fine-tuning

Reference 44

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Pith citing papers

Observation 96fb79cb-15f2-49a7-a315-a5403f276793 · inbound

Experience Augmented Policy Optimization for LLM Reasoning cites this paper.

Experience Augmented Policy Optimization for LLM Reasoning AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization

Reference 9

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Observation d05ecd30-59cf-4ae0-81dc-2c38046ca202 · inbound

Experience Augmented Policy Optimization for LLM Reasoning cites this paper.

Experience Augmented Policy Optimization for LLM Reasoning AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T09:32:15.287630Z

Source-reported events for the cited work

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