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Paper Citation Record · LEDGER

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

As of 12 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 2026-08-12T06:34:41.77262+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
  • malformed identifier1
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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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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Source-reported events for the cited work

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0d488472-9d28-400b-b160-f52ff37fed44 · outbound

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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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Source-reported events for the cited work

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Observation f1fa9123-ad8a-41a9-8c06-7fdaf60de9b2 · outbound

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

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b8ea8780-131f-40e6-af45-df2c5a4489c5 · outbound

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

Resolution
verified fuzzy
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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 74492595-f18e-4550-82aa-ecd00f0933b6 · outbound

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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation f3140a77-321f-4304-b80d-f00ae3241971 · outbound

This paper cites Simple Open-Vocabulary Object Detection with Vision Transformers.

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

Reference 28

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7db73d87-d104-4f06-b485-27237718928a · outbound

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 51092136-b004-4cc0-8694-69fe49af5349 · outbound

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
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d5def5ee-c41a-4c82-a70e-64e9ec9750bd · outbound

This paper cites GPT-4 Technical Report.

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

Reference 31

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This paper cites an unresolved cited work.

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

Reference 32

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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 8dd92a20-2a24-4a0f-8491-9d5b5597c2e2 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:19:56.904101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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unresolved
no resolver link, observed 2026-08-12T16:19:45.797663Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:19:45.797663Z digest=sha256:3599912ba909924d9e22024f56396ac5fdf85ddf4be598535b06a92f406bc958

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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no resolver link, observed 2026-08-12T16:19:45.802265Z

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source=pdf_text observed=2026-08-12T16:19:45.802265Z digest=sha256:bc077b693bde439892bbc8397823dc16662b5bef59581b3b44c6f2bc5bbed9a6

Observation d05d7781-24a7-4386-a523-9fc6ad15e9a6 · outbound

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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verified fuzzy
raw_fallback, observed 2026-08-12T16:19:56.869860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.807698Z digest=sha256:8ef43201bbbb77bddf59fa3de7830cfcdc71e0d370380280ee2b4e634942c58c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:19:56.851223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.813661Z digest=sha256:eb1d50cad297b5b90b4ba33bfe90490fda814dfca56b56be09f280f96c380b0c

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:19:56.832658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.818960Z digest=sha256:62bbb8485693da0a8def462e50939e2932b789f5f9440a415c340c08a15a1c49

Observation 5a041ad3-fc9a-4441-aa80-15e68acb0fb2 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:19:56.808390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.824360Z digest=sha256:8ad8931f466edd80dd0cc05290169c73742a69bab617d981f03b38da77a733a4

Observation 4272d5f7-1285-4f9d-97fc-d494b65de514 · outbound

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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verified fuzzy
raw_fallback, observed 2026-08-12T16:19:56.786805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.829246Z digest=sha256:1cf0c95ff43488bbf26d49afe6f57b582324487235d17d3dd776d3ff9d3ed086

Observation 24009d21-2838-45f5-b1c7-f608ec8eb06c · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:19:56.763724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.834397Z digest=sha256:ad4c3f0dc4249f6f483a08ce09d749181a5e39ca06fd3fd9bd448b6fb4bee58b

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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no resolver link, observed 2026-08-12T16:19:45.840224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:19:45.840224Z digest=sha256:567221442e4960fe03cdd0616a74a4871e1e34ac04f76e408511148dd96e3093

Observation c49e7674-0f24-430b-a717-72fb8183d2c2 · outbound

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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unresolved
no resolver link, observed 2026-08-12T16:19:45.846143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:19:45.846143Z digest=sha256:a340de5f69c9c73f2abba496c608f179518e11b25611096536b710108b33c373

Observation abb57ec7-d839-4ee2-aff5-5ac93aeef63a · outbound

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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unresolved
no resolver link, observed 2026-08-12T16:19:45.852589Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:19:45.852589Z digest=sha256:4425576666ea0f29e34ac90428f994a860202dc7af85f263b6a1c4abfabeae15

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

Resolution
unresolved
no resolver link, observed 2026-08-12T16:19:45.857856Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:19:45.857856Z digest=sha256:e3df743acef18dcf238866572d4897dec5de3c1ed1013ebe8ed066d976b21d0f

Observation 23fbd567-653a-4f0f-aee5-3b77a5136fd1 · outbound

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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no resolver link, observed 2026-08-12T16:19:45.864159Z

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source=pdf_text observed=2026-08-12T16:19:45.864159Z digest=sha256:4623407965be837e0668df0bd218b932572af098b40acd165a59ed673777cbbb

Observation 070514aa-d647-4995-9df7-f3f2f0995191 · outbound

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

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.869769Z digest=sha256:038a960be495d9e324905ec356e95bcafb71a1e9863d1b7b20f2f16bc64f8e54

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:19:45.875159Z digest=sha256:26e6b13f2df0edb6a3269bf8b1cac221ac3546315a2677d8503e347c3d672bb4

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.880507Z digest=sha256:b498909b4814d33cc7392368de1339a36c02505fb35e5c9dc2610ee2ddabc5f0

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

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

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T16:19:45.886327Z digest=sha256:dddd7754b1166ce29c4cbf693784d13c4c4076ebdb060f81da1bd35c2b1148f2

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:19:45.891415Z digest=sha256:a3eab30ef65e13701424eb6541ee7f0082d23ec2681de838c632899cf300874d

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.896930Z digest=sha256:33a50c837314c0cf14dfb2b0b5242b1c0a84b867a5c629cc287173ae0beebba2

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:19:45.901914Z digest=sha256:a3176c79c70045c9135a490f63c8eaf5b1dcd38ee47b006351450a50e8fa1de5

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T16:19:45.907689Z digest=sha256:990f76ee5a5ec42d7cfd9de0c6ec6a4d762f69c7e837dc8c7ab2935658cea498

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-10T17:18:39.999709Z digest=sha256:334d222e6bee0b713882ad0763c607a342e6c980d46323015d020f656a8cd3c5