Paper Citation Record · LEDGER

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach

As of 13 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 4 inbound Pith citation observations for arXiv:2411.17760.

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

pith.paper-citation-record.v1
2411.17760 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:42:49.344975Z

measured 45 of 45 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:18:40.043652Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T23:08:36.273358Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy13
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9664206d-b1d4-4869-8eb1-0818ffc363dc · outbound

This paper cites Pixtral 12B.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Pixtral 12B

Reference 1

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Source-reported events for the cited work

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Observation 5e3ee981-f43b-485c-9ede-81c4fea061cf · outbound

This paper cites Understanding Alignment in Multimodal LLMs: A Comprehensive Study.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Understanding Alignment in Multimodal LLMs: A Comprehensive Study

Reference 2

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Source-reported events for the cited work

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Observation 17e6d224-bede-4c49-b14f-71b440800297 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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Observation 75bb9f34-627c-4ae2-815e-6857afc292fa · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 4

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Observation d7091b5f-b2e2-45a6-adeb-5ff4feaac8d8 · outbound

This paper cites Enhancing Large Vision Language Models with Self-Training on Image Comprehension.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Enhancing Large Vision Language Models with Self-Training on Image Comprehension

Reference 5

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Observation af9c54af-75df-4baf-a867-4d0c221a1384 · outbound

This paper cites The Llama 3 Herd of Models.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T12:42:49.187310Z

Source-reported events for the cited work

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Observation c0df4a06-4e16-49d9-af38-74f31c16f0d1 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Multi-modal hal- lucination control by visual information grounding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:50.097804Z

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Observation a6a8cf59-d076-4253-8f86-fb6f5e809efa · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 8

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Observation 372d4f4a-0fa0-4afe-b7ee-558f26b3504b · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:50.082783Z

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Observation 33434ecd-d986-47a4-aa56-a16a269641ee · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:50.067522Z

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Observation 379e3394-eb4e-4230-89de-79b16e85e071 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Silkie: Preference Distillation for Large Visual Language Models

Reference 11

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Observation fcd3d2ba-a673-4504-9a7c-c4e75ec25384 · outbound

This paper cites Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 12

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Observation 2f0b93c8-b5df-4f57-9bc7-14f2f304cf2e · outbound

This paper cites Improved baselines with visual instruction tuning.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Improved baselines with visual instruction tuning

Reference 13

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Observation 359f8dce-5ce2-4753-be9e-407e9a530613 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 14

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Source-reported events for the cited work

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Observation 201c41a6-b09f-4247-875f-d7e42ef98740 · outbound

This paper cites Visual instruction tuning.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Visual instruction tuning

Reference 15

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Observation fd6427e1-1030-4dc9-bea9-b48249d23e54 · outbound

This paper cites CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs

Reference 16

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Observation 361d47bc-3e91-40a3-a0fc-e3bf17a30525 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Training language models to follow instructions with human feedback

Reference 17

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Source-reported events for the cited work

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Observation fa9b8285-67da-410a-b1cd-18b65fffeb48 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Direct preference optimization: Your language model is secretly a reward model

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:50.008652Z

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Observation a979d35e-c0be-4df4-bd71-34a8209f3ca7 · outbound

This paper cites Object Hallucination in Image Captioning.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Object Hallucination in Image Captioning

Reference 19

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Observation 660a6b1d-8f45-4379-a4f5-fc496e0a5cd4 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 20

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Observation 021fd46d-24eb-4e5f-abac-454855afb5ad · outbound

This paper cites A Survey on Self-Evolution of Large Language Models.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach A Survey on Self-Evolution of Large Language Models

Reference 21

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Observation afac81d5-79f2-44d9-baed-07cc2a12bcca · outbound

This paper cites Planbench: An extensible benchmark for evaluating large language mod- els on planning and reasoning about change.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Planbench: An extensible benchmark for evaluating large language mod- els on planning and reasoning about change

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:49.994252Z

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Observation b76cbf24-7d04-465b-ad59-d167cca69c75 · outbound

This paper cites LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 23

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Observation edea6d44-21f1-4b73-93e6-bf0f9cdef135 · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach CogVLM: Visual Expert for Pretrained Language Models

Reference 24

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Observation e873e831-6705-4fb1-932d-1d76b66ed484 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional hu- man feedback

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:49.980201Z

Source-reported events for the cited work

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Observation b75e6337-6f51-457c-8ca2-e82c3a73f64d · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T12:42:49.277290Z

Source-reported events for the cited work

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Observation 7a023b58-55ce-49b4-a52a-f0c56adade20 · outbound

This paper cites Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective

Reference 27

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Observation fedf3290-a067-4888-9074-b4406b4c1ae6 · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization

Reference 28

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Observation 88cb4e6c-d4a0-49fd-b3cc-5a46da43d503 · outbound

This paper cites Analyzing and Mitigating Object Hallucination in Large Vision-Language Models.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Analyzing and Mitigating Object Hallucination in Large Vision-Language Models

Reference 29

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Observation 584df2d3-30ba-4c47-9cc3-f1d50f109cfa · outbound

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

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Aligning Modalities in Vision Large Language Models via Preference Fine-tuning

Reference 30

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Observation c3de1e84-38b5-4f68-92d3-cb91b1ff467f · outbound

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Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Unresolved cited work

Reference 31

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Observation 8093b29e-f648-4049-a2ae-e032ca3521f3 · outbound

This paper cites - The salad is correctly noted as being part of the spread, but the caption could be more specific about the contents of the salad.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach - The salad is correctly noted as being part of the spread, but the caption could be more specific about the contents of the salad

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:49.951336Z

Source-reported events for the cited work

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Observation f7d23ead-32f6-4401-8886-e3249bd6a60e · outbound

This paper cites However, it inaccurately refers to a wine glass; the image shows glasses of what appears to be a juice or iced tea rather than wine glasses.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach However, it inaccurately refers to a wine glass; the image shows glasses of what appears to be a juice or iced tea rather than wine glasses

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:49.937245Z

Source-reported events for the cited work

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Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach - The mention of a potted plant is inaccurate; while there is foliage in the background, it cannot be clearly identified as a potted plant

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:49.922953Z

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This paper cites The scene includes forks but not knives.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach The scene includes forks but not knives

Reference 35

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Observation f94029b2-2584-4ce3-a07b-e72cce4a1f55 · outbound

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Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Unresolved cited work

Reference 36

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Observation 37d3f887-7c41-4f0b-ba8a-803fc1a0bf16 · outbound

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Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Unresolved cited work

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Observation 8f869abc-499c-4d5b-b7f1-bf423f33c387 · outbound

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Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Unresolved cited work

Reference 38

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Observation aedf9a77-1b0b-4dd9-94a4-7e0014abfc5f · outbound

This paper cites It connects this diagram to the essay's content, although it does not specify what the diagram illustrates.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach It connects this diagram to the essay's content, although it does not specify what the diagram illustrates

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:49.846812Z

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Observation 1185ff57-3593-4cce-ae4b-238674a23caf · outbound

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Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Unresolved cited work

Reference 40

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Observation 11ce3f01-c141-48c3-96f2-abd6d739b99f · outbound

This paper cites Overall, the caption effectively captures the primary elements of the image, including the handwriting, topic, and visual characteristics.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach Overall, the caption effectively captures the primary elements of the image, including the handwriting, topic, and visual characteristics

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:42:49.816188Z

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Pith citing papers

Observation 0d0af034-c869-4ba6-b326-848eca0e2cd2 · inbound

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach

Reference 46

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Observation 612bf3fb-f15e-4b05-85a7-cc4a788cbf1d · 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 Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach

Reference 22

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Visual Preference Optimization with Rubric Rewards cites this paper.

Visual Preference Optimization with Rubric Rewards Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach

Reference 11

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Observation 6218e471-fbd3-45c2-985f-6b97bc14c7d4 · inbound

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs cites this paper.

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach

Reference 2

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