Paper Citation Record · LEDGER

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs

As of 13 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2411.13697.

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

pith.paper-citation-record.v1
2411.13697 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:19:45.907689Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:18:39.999709Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T17:18:42.348103Z

Reference resolution

54 of 54 outbound references displayed

verified exact0
verified fuzzy32
unresolved21
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e856053a-6e0c-4a4f-b7fe-56e089518534 · outbound

This paper cites Llama 3.1 8b instruct.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Llama 3.1 8b instruct

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.352098Z

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Observation 60ae80d8-2c42-495e-b593-0390a4b8b6a8 · outbound

This paper cites Neural module networks.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Neural module networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.336364Z

Source-reported events for the cited work

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Observation f12f664c-4f19-4989-8a7d-daa471ee13ab · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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Observation f3f049da-c169-4a70-939d-8025379a2636 · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Rank analysis of incomplete block designs: I

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.321195Z

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Observation 81ac125f-6d5e-4013-9888-b504a77fef0b · outbound

This paper cites Modularized zero-shot VQA with pre-trained models.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Modularized zero-shot VQA with pre-trained models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.301616Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2d805f23-9fcc-43d2-a855-7147837deef0 · outbound

This paper cites Complex claim verification with evidence re- trieved in the wild.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Complex claim verification with evidence re- trieved in the wild

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.285170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 210b3e1b-eab2-45ac-84b4-a6dccdecdd07 · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 7

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Observation 89ea2ab6-5635-46fe-b44e-ec01a53e9f69 · outbound

This paper cites Making the V in VQA matter: El- evating the role of image understanding in visual question answering.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Making the V in VQA matter: El- evating the role of image understanding in visual question answering

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.268963Z

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Observation 32f2aad0-d4e5-4acc-960e-edf2128df124 · outbound

This paper cites Breaking common sense: Whoops! A vision-and- language benchmark of synthetic and compositional images.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Breaking common sense: Whoops! A vision-and- language benchmark of synthetic and compositional images

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.250746Z

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Observation 711b46af-a74a-47e0-a91f-b5a38aca166c · outbound

This paper cites Efficient Multimodal Learning from Data-centric Perspective.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Efficient Multimodal Learning from Data-centric Perspective

Reference 10

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Observation 20631d07-13c1-4d08-8294-9f391786b97e · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.233252Z

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Observation b4c42340-4a5c-45d5-9213-fa1d075372fe · outbound

This paper cites Learning to reason: End-to-end module networks for visual question answering.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Learning to reason: End-to-end module networks for visual question answering

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.212297Z

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Observation 7a7eaa0a-5988-427c-ad5c-bc22122c910d · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.194409Z

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Observation a7617016-d6cd-4281-88a9-0c132aa9a876 · outbound

This paper cites Hudson and Christopher D.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hudson and Christopher D

Reference 14

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Source-reported events for the cited work

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Observation 47bf757d-5a76-4b4e-82d0-9cd359ffe7e8 · outbound

This paper cites Hallucination augmented contrastive learning for multimodal large language model.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hallucination augmented contrastive learning for multimodal large language model

Reference 15

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Observation 503ae8ae-5719-42b0-b0d1-1def6e291805 · outbound

This paper cites Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models

Reference 16

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Observation 7ee622db-7b9b-48ba-84f1-693bd84cb659 · outbound

This paper cites What’s ”up” with vision-language models? investigating their strug- gle with spatial reasoning.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs What’s ”up” with vision-language models? investigating their strug- gle with spatial reasoning

Reference 17

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Observation 79ed3313-35b0-4be9-bf92-0a156e22896f · outbound

This paper cites Shamma, Michael S.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Shamma, Michael S

Reference 18

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Observation e5e98b55-daa9-4db5-bf27-cff4e094e6d0 · outbound

This paper cites Mitigating object hallucinations in large vision-language models through vi- sual contrastive decoding.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Mitigating object hallucinations in large vision-language models through vi- sual contrastive decoding

Reference 19

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Observation f3485cbb-1db5-4fd5-b709-581e204e9658 · outbound

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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Unresolved cited work

Reference 20

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Observation 4a215b56-61d5-4b59-9c3e-91dad1ac794f · outbound

This paper cites Silkie: Preference Distillation for Large Visual Language Models.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Silkie: Preference Distillation for Large Visual Language Models

Reference 21

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Observation bf3af5f6-456b-489d-b3fc-5371f602f1c8 · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Evaluating object hallucination in large vision-language models

Reference 22

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This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:57.038420Z

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Observation 33dc35c4-8198-4966-b538-5526e5d36670 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Improved Baselines with Visual Instruction Tuning

Reference 24

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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Visual instruction tuning

Reference 25

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This paper cites OK-VQA: A visual question answering benchmark requiring external knowledge.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs OK-VQA: A visual question answering benchmark requiring external knowledge

Reference 26

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This paper cites Factscore: Fine-grained atomic evaluation of factual precision in long form text generation.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Factscore: Fine-grained atomic evaluation of factual precision in long form text generation

Reference 27

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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Simple Open-Vocabulary Object Detection with Vision Transformers

Reference 28

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This paper cites Compositional chain-of-thought prompting for large multimodal models.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Compositional chain-of-thought prompting for large multimodal models

Reference 29

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This paper cites Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:56.944636Z

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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs GPT-4 Technical Report

Reference 31

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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Unresolved cited work

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This paper cites Manning, Stefano Ermon, and Chelsea Finn.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Manning, Stefano Ermon, and Chelsea Finn

Reference 33

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Observation 195df677-367f-4b23-9313-fe107c63c567 · outbound

This paper cites Object hallucination in image cap- tioning.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Object hallucination in image cap- tioning

Reference 34

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Observation 8d86171e-de17-4411-888f-2c2101986102 · outbound

This paper cites Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment

Reference 35

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This paper cites Toolformer: Lan- guage models can teach themselves to use tools.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Toolformer: Lan- guage models can teach themselves to use tools

Reference 36

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Observation 14eae642-95d4-4eb1-89f7-a02f446fd42f · outbound

This paper cites Averitec: A dataset for real-world claim verification with ev- idence from the web.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Averitec: A dataset for real-world claim verification with ev- idence from the web

Reference 37

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Observation 89250df4-7829-4ab8-b33b-fb3b36af9054 · outbound

This paper cites Towards VQA models that can read.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Towards VQA models that can read

Reference 38

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This paper cites Reclip: A strong zero-shot baseline for referring expression compre- hension.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Reclip: A strong zero-shot baseline for referring expression compre- hension

Reference 39

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This paper cites Aligning large multimodal models with factually aug- mented RLHF.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Aligning large multimodal models with factually aug- mented RLHF

Reference 40

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This paper cites Winoground: Probing vision and language models for visio- linguistic compositionality.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Winoground: Probing vision and language models for visio- linguistic compositionality

Reference 41

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Observation 8c198218-3604-4888-9501-cfca0cedf142 · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 42

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This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 43

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This paper cites Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 44

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Observation 2e0f2944-4597-4162-b86d-a19b143ecf7b · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 45

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 46

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:56.745479Z

Source-reported events for the cited work

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Observation 0ea2a3c2-2946-4c1f-aa6b-dc5e1eaec77d · outbound

This paper cites RLAIF-V: aligning mllms through open-source AI feedback for super GPT-4V trustworthiness.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs RLAIF-V: aligning mllms through open-source AI feedback for super GPT-4V trustworthiness

Reference 48

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Observation 05dfe205-3478-4350-afee-968d54eb9446 · outbound

This paper cites Less is more: Miti- gating multimodal hallucination from an EOS decision per- spective.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Less is more: Miti- gating multimodal hallucination from an EOS decision per- spective

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:56.724835Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f76ee1ca-8be6-4b4d-8d62-c1543f2210fa · outbound

This paper cites VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T16:19:45.886327Z

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Observation 8217e480-5c65-4cf6-8ed8-8967d74ffa66 · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T16:19:45.891415Z

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Observation 672a8d6a-4b5d-4751-838f-50e30593158b · outbound

This paper cites ROME: evaluating pre-trained vision-language models on reasoning beyond visual common sense.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs ROME: evaluating pre-trained vision-language models on reasoning beyond visual common sense

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:19:56.701669Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0544ff14-ccb0-47a9-bf0e-41073097769a · outbound

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

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Aligning Modalities in Vision Large Language Models via Preference Fine-tuning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T16:19:45.901914Z

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Observation 252f9984-8921-40f9-b03b-4fb8f2b2d412 · outbound

This paper cites Is the {sub} {relation} {obj}?.

Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Is the {sub} {relation} {obj}?

Reference 54

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:19:56.680852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation a76663c8-4a8f-4508-9668-446e09af7a64 · inbound

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model cites this paper.

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:18:42.352989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.