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Paper Citation Record · LEDGER

Visual prompt engineering for video models

As of 24 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2607.25537.

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

pith.paper-citation-record.v1
2607.25537 v1

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measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:13:12.723564Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

61 of 61 outbound references displayed

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Outbound references

Observation 5f090e9a-fe50-4d4c-94f5-f60cc954ff98 · outbound

This paper cites Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP.

Visual prompt engineering for video models Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 1

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source=pdf_text observed=2026-08-01T02:13:11.448951Z digest=sha256:76c7b0c18923f09870111c9fc6f7ba747524b8383acf6c9d75d163561011278d

Observation 09c09a26-2eb7-4188-a66e-f7914423a6b8 · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Visual prompt engineering for video models A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 2

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Observation 89a7b524-47c3-4ab3-b752-0fecacec8c1f · outbound

This paper cites Prompt engineering with ChatGPT: a guide for academic writers.Annals of biomedical engineering, 51(12):2629–2633, 2023.

Visual prompt engineering for video models Prompt engineering with ChatGPT: a guide for academic writers.Annals of biomedical engineering, 51(12):2629–2633, 2023

Reference 3

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Observation d5eedd9b-796c-43da-bdf3-c67c584abc2f · outbound

This paper cites Prompt programming for large language models: Beyond the few-shot paradigm.

Visual prompt engineering for video models Prompt programming for large language models: Beyond the few-shot paradigm

Reference 4

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source=pdf_text observed=2026-08-01T02:13:11.490979Z digest=sha256:8805dd0b3b37fff6e2ffb80da31834fd4cf838aea74ab25f8fd4852536e4074b

Observation 744a43c0-473a-478e-a94a-3098827c6b5e · outbound

This paper cites Dspy: compiling declarative language model calls into state-of-the-art pipelines.

Visual prompt engineering for video models Dspy: compiling declarative language model calls into state-of-the-art pipelines

Reference 5

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Observation bee71271-569c-4350-b0be-ca4ba20dbfa9 · outbound

This paper cites TextGrad: Automatic "Differentiation" via Text.

Visual prompt engineering for video models TextGrad: Automatic "Differentiation" via Text

Reference 6

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Observation ebe7cd9b-39d2-4f59-9b08-de03a11b14a2 · outbound

This paper cites Prompt engineering in large language models.

Visual prompt engineering for video models Prompt engineering in large language models

Reference 7

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Observation b0a38dd5-468c-4434-bfe6-c0d6a8719c8d · outbound

This paper cites Prompt engineering as an important emerging skill for medical professionals: tutorial.Journal of medical Internet research, 25:e50638, 2023.

Visual prompt engineering for video models Prompt engineering as an important emerging skill for medical professionals: tutorial.Journal of medical Internet research, 25:e50638, 2023

Reference 8

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Observation 0654ae9d-0209-4ca9-b583-f60db8edc336 · outbound

This paper cites Video models are zero-shot learners and reasoners.

Visual prompt engineering for video models Video models are zero-shot learners and reasoners

Reference 9

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Observation a8759541-caa2-4281-a86f-58fc7310dc89 · outbound

This paper cites Video as the New Language for Real-World Decision Making.

Visual prompt engineering for video models Video as the New Language for Real-World Decision Making

Reference 10

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Observation d062959b-d2a0-4e0b-8cd5-74fecac58800 · outbound

This paper cites Rethinking visual intelligence: Insights from video pretraining.arXiv preprint arXiv:2510.24448, 2025.

Visual prompt engineering for video models Rethinking visual intelligence: Insights from video pretraining.arXiv preprint arXiv:2510.24448, 2025

Reference 11

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Observation f48ddc45-34d6-46fe-9b10-ff51575dd041 · outbound

This paper cites A very big video reasoning suite.arXiv preprint arXiv:2602.20159, 2026.

Visual prompt engineering for video models A very big video reasoning suite.arXiv preprint arXiv:2602.20159, 2026

Reference 12

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Observation b40ca782-8d73-490d-826a-4cd7ac835f78 · outbound

This paper cites MentisOculi: Revealing the Limits of Reasoning with Mental Imagery.

Visual prompt engineering for video models MentisOculi: Revealing the Limits of Reasoning with Mental Imagery

Reference 13

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Observation 22e635c4-1d8e-417e-99d7-c1a8bc7657b1 · outbound

This paper cites Video models reason early: Exploiting plan commitment for maze solving.arXiv preprint arXiv:2603.30043, 2026.

Visual prompt engineering for video models Video models reason early: Exploiting plan commitment for maze solving.arXiv preprint arXiv:2603.30043, 2026

Reference 14

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Observation 1ddbc3ed-f6a2-4f2e-b577-cb97588e1911 · outbound

This paper cites Are video models ready as zero-shot reasoners? an empirical study with the mme-cof benchmark.

Visual prompt engineering for video models Are video models ready as zero-shot reasoners? an empirical study with the mme-cof benchmark

Reference 15

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Observation a1e35699-3f7b-4289-b025-7f4af4bed506 · outbound

This paper cites Demystifying Video Reasoning.

Visual prompt engineering for video models Demystifying Video Reasoning

Reference 16

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Observation 80735478-d855-4326-a579-d2a52006468a · outbound

This paper cites Thinking in frames: How visual context and test-time scaling empower video reasoning.arXiv preprint arXiv:2601.21037, 2026.

Visual prompt engineering for video models Thinking in frames: How visual context and test-time scaling empower video reasoning.arXiv preprint arXiv:2601.21037, 2026

Reference 17

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Observation 0668deb4-1aad-42da-b63c-07be808d368b · outbound

This paper cites VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization.

Visual prompt engineering for video models VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization

Reference 18

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Observation bd531f51-11e3-419b-9ecd-d8ddce809eba · outbound

This paper cites Thinking with video: Video generation as a promising multimodal reasoning paradigm.

Visual prompt engineering for video models Thinking with video: Video generation as a promising multimodal reasoning paradigm

Reference 19

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Observation e3435844-0e9a-4b6a-b693-41c87750df24 · outbound

This paper cites Review of large vision models and visual prompt engineering.Meta-Radiology, 1(3):100047, 2023.

Visual prompt engineering for video models Review of large vision models and visual prompt engineering.Meta-Radiology, 1(3):100047, 2023

Reference 20

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Observation b7cda6a1-eada-4c41-8416-e152951f0bc6 · outbound

This paper cites A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models.

Visual prompt engineering for video models A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Reference 21

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Observation 0cd51713-ca86-417a-abe8-98a1a2fa6d14 · outbound

This paper cites What does clip know about a red circle? visual prompt engineering for vlms.

Visual prompt engineering for video models What does clip know about a red circle? visual prompt engineering for vlms

Reference 22

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Observation 2d743149-ad87-428e-a26b-16dbbce48e60 · outbound

This paper cites Cpt: Colorful prompt tuning for pre-trained vision-language models.AI Open, 5:30–38, 2024.

Visual prompt engineering for video models Cpt: Colorful prompt tuning for pre-trained vision-language models.AI Open, 5:30–38, 2024

Reference 23

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Observation 085e645b-ba73-4dbc-98ac-2d97d35e2bfc · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Visual prompt engineering for video models Exploring Visual Prompts for Adapting Large-Scale Models

Reference 24

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Observation 3070168c-54c1-4a00-9076-42c423b9a614 · outbound

This paper cites Highlight: Learning visual prompts for vision-language models, 2024.

Visual prompt engineering for video models Highlight: Learning visual prompts for vision-language models, 2024

Reference 25

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Observation f20ac7b9-241a-433e-869a-2982d5afeb3c · outbound

This paper cites Visual prompt tuning.

Visual prompt engineering for video models Visual prompt tuning

Reference 26

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Observation 54a50607-9700-44b8-85bc-541c0487cc3f · outbound

This paper cites Visual promptingviaimageinpainting.

Visual prompt engineering for video models Visual promptingviaimageinpainting

Reference 27

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Observation 345bc233-e3ed-46bd-bfe9-e89dfc631b4f · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

Visual prompt engineering for video models Images speak in images: A generalist painter for in-context visual learning

Reference 28

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Observation a64133c8-05fe-46d5-80fd-e478ce72cf3b · outbound

This paper cites Visualcloze: A universal image generation framework via visual in-context learning.

Visual prompt engineering for video models Visualcloze: A universal image generation framework via visual in-context learning

Reference 29

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Observation 4c4ec16f-038e-440b-8c90-77973ef24ef5 · outbound

This paper cites Draw-and-understand: Leveraging visual prompts to enable mllms to comprehend what you want.

Visual prompt engineering for video models Draw-and-understand: Leveraging visual prompts to enable mllms to comprehend what you want

Reference 30

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Observation a38f6da9-6952-4955-bd88-71f4f76b3eff · outbound

This paper cites Visual Physics Comprehension Test (VPCT) Dataset.https://huggingface.

Visual prompt engineering for video models Visual Physics Comprehension Test (VPCT) Dataset.https://huggingface

Reference 31

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Observation 5a03b69f-f3fb-40bf-8a81-95d7e64f3319 · outbound

This paper cites Nano Banana 2: Gemini Image Generation Overview.https://gemini.google/ov erview/image-generation/, 2026.

Visual prompt engineering for video models Nano Banana 2: Gemini Image Generation Overview.https://gemini.google/ov erview/image-generation/, 2026

Reference 32

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Observation 61c54c8f-14b9-47a8-8e17-518fb2d2cc1c · outbound

This paper cites Gemini 3.1 Pro.

Visual prompt engineering for video models Gemini 3.1 Pro

Reference 33

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Observation 589565e5-19eb-431f-a5b4-421644604b78 · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

Visual prompt engineering for video models Wan: Open and Advanced Large-Scale Video Generative Models

Reference 34

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Observation ec29296a-05f9-4d7d-954a-5385078ca917 · outbound

This paper cites an unresolved cited work.

Visual prompt engineering for video models Unresolved cited work

Reference 35

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Observation a2921345-95d1-4bca-b538-6fe676ddc70f · outbound

This paper cites Omni Flash Model Card.https://deepmind.google/models/model-cards/g emini-omni-flash/, 2026.

Visual prompt engineering for video models Omni Flash Model Card.https://deepmind.google/models/model-cards/g emini-omni-flash/, 2026

Reference 36

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Observation 58327f99-3e6a-4d34-a34f-99a46d33de5c · outbound

This paper cites Interpreting and controlling model behavior via constitutions for atomic concept edits.

Visual prompt engineering for video models Interpreting and controlling model behavior via constitutions for atomic concept edits

Reference 37

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Observation 843dabc7-1838-4840-8831-6ec344419360 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Visual prompt engineering for video models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 38

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Observation cc6a05d0-6c30-4c7f-8176-41394e174f2b · outbound

This paper cites Gemini developer API pricing.https://ai.google.dev/gemini-api/docs/pr icing#veo-3.1, 2026.

Visual prompt engineering for video models Gemini developer API pricing.https://ai.google.dev/gemini-api/docs/pr icing#veo-3.1, 2026

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Observation f69b5237-8b0e-463e-8ae0-e3291e97c427 · outbound

This paper cites Performance vs.

Visual prompt engineering for video models Performance vs

Reference 40

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Observation d65c511e-c7ab-4aa3-ac98-bd961710bf65 · outbound

This paper cites How can we know what language models know?Transactions of the Association for Computational Linguistics, 8:423–438, 2020.

Visual prompt engineering for video models How can we know what language models know?Transactions of the Association for Computational Linguistics, 8:423–438, 2020

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Observation 9e66877a-2c83-43d4-9e57-4047f3e4fe62 · outbound

This paper cites Inducing relational knowledge from BERT.

Visual prompt engineering for video models Inducing relational knowledge from BERT

Reference 42

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Observation 40892ac7-8b43-41a3-91b1-ca301b55024b · outbound

This paper cites Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020.

Visual prompt engineering for video models Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020

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Observation f7810ad5-54e8-48c9-914a-5599cbf52dd5 · outbound

This paper cites Unmasking Clever Hans predictors and assessing what machines really learn.Nature communications, 10(1):1096, 2019.

Visual prompt engineering for video models Unmasking Clever Hans predictors and assessing what machines really learn.Nature communications, 10(1):1096, 2019

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Observation 0fba6822-e52a-409b-8bdf-7093defd00d9 · outbound

This paper cites Unbiased look at dataset bias.

Visual prompt engineering for video models Unbiased look at dataset bias

Reference 45

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Observation 88035d6b-db89-4b45-b581-abb7c189b321 · outbound

This paper cites Cosmos 3: Omnimodal World Models for Physical AI.

Visual prompt engineering for video models Cosmos 3: Omnimodal World Models for Physical AI

Reference 46

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Observation 558e47f8-d117-4434-bee0-2a3ca4ee0768 · outbound

This paper cites this vertical drop leads directly into the first bucket on the left.

Visual prompt engineering for video models this vertical drop leads directly into the first bucket on the left

Reference 47

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Observation 0ee5b61a-16dc-40fc-9423-fd5f1be4e28a · outbound

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Visual prompt engineering for video models Unresolved cited work

Reference 48

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Observation 1814a3af-73f5-4c0a-9383-453be937d88a · outbound

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Visual prompt engineering for video models Unresolved cited work

Reference 49

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Observation f6e5c226-f8c0-4a60-9e7f-e307a3cfdf3c · outbound

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Visual prompt engineering for video models Unresolved cited work

Reference 50

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Observation 2df7766c-1d9d-4856-8a91-229bcfb18784 · outbound

This paper cites an unresolved cited work.

Visual prompt engineering for video models Unresolved cited work

Reference 51

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Observation 003ff0e1-6050-4106-8dc5-2cc570bcba7c · outbound

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Visual prompt engineering for video models Unresolved cited work

Reference 52

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Observation 5e611fd3-90b4-45f2-a345-eaf0524a8e35 · outbound

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Visual prompt engineering for video models Unresolved cited work

Reference 53

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Observation f5104bf3-1644-4950-9952-94d42ad2e31b · outbound

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Visual prompt engineering for video models Unresolved cited work

Reference 54

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Observation 09508719-b996-4a91-ab5c-ae012492def5 · outbound

This paper cites an unresolved cited work.

Visual prompt engineering for video models Unresolved cited work

Reference 55

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Observation f6225924-a716-48ff-9187-a2c7911213a9 · outbound

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Visual prompt engineering for video models Static shot, no zoom or pan

Reference 56

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Observation da033842-a0c7-4868-b0ee-0e7477f01da3 · outbound

This paper cites an unresolved cited work.

Visual prompt engineering for video models Unresolved cited work

Reference 57

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Observation 7139350d-05e2-440a-8f02-301b721cfce4 · outbound

This paper cites justification.

Visual prompt engineering for video models justification

Reference 59

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Observation 08a7fe50-9548-444a-b797-d6fdceb30cd2 · outbound

This paper cites moves”: A list of strings representing the extracted moves in order, e.g., [“A N.

Visual prompt engineering for video models moves”: A list of strings representing the extracted moves in order, e.g., [“A N

Reference 60

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Observation b1c04840-bd69-40ac-adae-20f12b9cc66e · outbound

This paper cites invalid_after_seconds.

Visual prompt engineering for video models invalid_after_seconds

Reference 61

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Observation 94721f54-1154-44b3-ac2c-bda806b17fb9 · outbound

This paper cites justification.

Visual prompt engineering for video models justification

Reference 62

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Pith citing papers

No inbound Pith citation observations are available.