Paper Citation Record · LEDGER

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation

As of 12 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2506.02708.

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

pith.paper-citation-record.v1
2506.02708 v1

Coverage vector

measured 38 of 38 reference resolution

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measured 39 of 39 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: pith, observed 2026-08-07T11:22:39.638306Z

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 2d14ec3e-2093-4db5-8a10-afb076786963 · outbound

This paper cites Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation

Reference 1

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This paper cites IAA datasets (e.g., A V A [3] and AADB [4]) consist of photographs scored by human annotators.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation IAA datasets (e.g., A V A [3] and AADB [4]) consist of photographs scored by human annotators

Reference 2

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This paper cites chosen” and “rejected.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation chosen” and “rejected

Reference 3

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This paper cites Datasets As IAA datasets, we use A V A [3] and AADB [4].

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Datasets As IAA datasets, we use A V A [3] and AADB [4]

Reference 4

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This paper cites Main Results Table 1 presents the results of our method applied to the LLaV A-NeXT-7B model on the A V A dataset, a part of which is also plotted in Fig.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Main Results Table 1 presents the results of our method applied to the LLaV A-NeXT-7B model on the A V A dataset, a part of which is also plotted in Fig

Reference 5

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Unresolved cited work

Reference 6

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This paper cites Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

Reference 7

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This paper cites Di- rect preference optimization: your language model is secretly a reward model,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Di- rect preference optimization: your language model is secretly a reward model,

Reference 8

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Observation 82622785-d97b-4c65-b9a4-80d9c7d07411 · outbound

This paper cites Ava: A large-scale database for aesthetic visual analy- sis,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Ava: A large-scale database for aesthetic visual analy- sis,

Reference 9

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Observation 2b5a600b-eecc-42df-a7e5-e780e169823b · outbound

This paper cites Photo aesthetics ranking network with attributes and content adaptation,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Photo aesthetics ranking network with attributes and content adaptation,

Reference 10

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This paper cites Image aesthetic assessment: An experimental survey,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Image aesthetic assessment: An experimental survey,

Reference 11

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This paper cites Vila: Learning image aes- thetics from user comments with vision-language pre- training,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Vila: Learning image aes- thetics from user comments with vision-language pre- training,

Reference 12

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This paper cites Q-align: Teach- ing LMMs for visual scoring via discrete text-defined levels,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Q-align: Teach- ing LMMs for visual scoring via discrete text-defined levels,

Reference 13

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This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Chain-of-thought prompting elicits reasoning in large language models,

Reference 14

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Observation 2029f5e8-406e-46f5-a8c8-d1c05c741f62 · outbound

This paper cites Multimodal explanations: Justifying de- cisions and pointing to the evidence,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Multimodal explanations: Justifying de- cisions and pointing to the evidence,

Reference 15

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This paper cites WT5?! Training Text-to-Text Models to Explain their Predictions.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation WT5?! Training Text-to-Text Models to Explain their Predictions

Reference 16

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This paper cites Measuring association between labels and free-text ra- tionales,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Measuring association between labels and free-text ra- tionales,

Reference 17

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This paper cites Few-shot self-rationalization with natural language prompts,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Few-shot self-rationalization with natural language prompts,

Reference 18

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This paper cites Reframing human- ai collaboration for generating free-text explanations,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Reframing human- ai collaboration for generating free-text explanations,

Reference 19

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This paper cites Large language models can self-improve,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Large language models can self-improve,

Reference 20

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This paper cites Self-refine: iterative refinement with self-feedback,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Self-refine: iterative refinement with self-feedback,

Reference 21

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Self-rewarding language models,

Reference 22

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Iterative reasoning preference optimization,

Reference 23

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This paper cites Enhancing large vision language models with self-training on image comprehension,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Enhancing large vision language models with self-training on image comprehension,

Reference 24

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Ties-merging: resolving in- terference when merging models,

Reference 25

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Nima: Neural image assessment,

Reference 26

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This paper cites Llava- next-interleave: Tackling multi-image, video, and 3d in large multimodal models,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Llava- next-interleave: Tackling multi-image, video, and 3d in large multimodal models,

Reference 27

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This paper cites Internvl: Scaling up vi- sion foundation models and aligning for generic visual- linguistic tasks,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Internvl: Scaling up vi- sion foundation models and aligning for generic visual- linguistic tasks,

Reference 28

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This paper cites Improved baselines with visual instruction tun- ing,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Improved baselines with visual instruction tun- ing,

Reference 29

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This paper cites Llava- next: Improved reasoning, ocr, and world knowl- edge,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Llava- next: Improved reasoning, ocr, and world knowl- edge,

Reference 30

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation LoRA: Low-rank adaptation of large language models,

Reference 31

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This paper cites Musiq: Multi-scale image quality transformer,.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Musiq: Multi-scale image quality transformer,

Reference 32

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation This could be achieved by using a higher megapixel camera or a camera with better low-light capabilities

Reference 33

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This paper cites Natural light is often preferred for food photography, as it can create a more appealing and natural look.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Natural light is often preferred for food photography, as it can create a more appealing and natural look

Reference 34

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This paper cites Consider the rule of thirds and the use of negative space to create a more balanced and visually appealing image.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation Consider the rule of thirds and the use of negative space to create a more balanced and visually appealing image

Reference 35

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation This can help draw attention to the main subject of the image

Reference 36

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Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation This can be done using photo editing software

Reference 37

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This paper cites This could include adjusting the exposure, contrast, and saturation to make the colors pop and the image more vibrant.

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation This could include adjusting the exposure, contrast, and saturation to make the colors pop and the image more vibrant

Reference 38

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Pith citing papers

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