Paper Citation Record · LEDGER

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

As of 12 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 4 inbound Pith citation observations for arXiv:2506.07280.

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

pith.paper-citation-record.v1
2506.07280 v2

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:41:31.165907Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T00:56:18.867382Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:07:55.884435Z

Reference resolution

66 of 66 outbound references displayed

verified exact2
verified fuzzy17
unresolved47
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0348a0c4-89a8-4461-b629-bd6a23b83746 · outbound

This paper cites The Surprising Effectiveness of Test-Time Training for Few-Shot Learning.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models The Surprising Effectiveness of Test-Time Training for Few-Shot Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.938318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6511dde1-026f-43da-a121-bdb3bbbd27a6 · outbound

This paper cites Sequential modeling enables scalable learning for large vision models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Sequential modeling enables scalable learning for large vision models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.943273Z

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Observation f2952e48-e6eb-479a-92d9-58e4bf31d0f7 · outbound

This paper cites Visual Prompting via Image Inpainting.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Visual Prompting via Image Inpainting

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.946687Z

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Observation fb8220b0-4864-4f24-b987-69929aa5ffc1 · outbound

This paper cites How I got a record 53.6% on ARC-AGI-Pub using Sonnet 3.5.1.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models How I got a record 53.6% on ARC-AGI-Pub using Sonnet 3.5.1

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.877379Z

Source-reported events for the cited work

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Observation a7ccd42a-13c9-4c17-b516-d26b57255dcc · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.953970Z

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Observation e6a0b3b6-99d4-4692-af26-24af1f14a934 · outbound

This paper cites On the Measure of Intelligence.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models On the Measure of Intelligence

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.957711Z

Source-reported events for the cited work

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Observation e221e75a-56e9-4563-9461-5552967a257e · outbound

This paper cites ARC Prize 2024: Technical Report.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models ARC Prize 2024: Technical Report

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.961599Z

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Observation a12b027f-b994-496d-b99f-78c593889d8b · outbound

This paper cites Studying Image Diffusion Features for Zero-Shot Video Object Segmentation.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Studying Image Diffusion Features for Zero-Shot Video Object Segmentation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:41:31.446083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 41106a4a-3377-444f-914b-f7487777f1f9 · outbound

This paper cites A survey on in-context learning.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models A survey on in-context learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.867256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a3fe0e59-0e33-44af-aae3-f965ae67eb2d · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Movie Gen: A Cast of Media Foundation Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.972332Z

Source-reported events for the cited work

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Observation 04720d12-ca3c-42f6-995d-695c57328949 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.975917Z

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Observation dd993c70-04d6-439f-a489-cedf0b72ca68 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.979426Z

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Observation 1194eee9-5f64-4a62-b271-043a3751e3ce · outbound

This paper cites Cosmos World Foundation Model Platform for Physical AI.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Cosmos World Foundation Model Platform for Physical AI

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.982950Z

Source-reported events for the cited work

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Observation 0481c6eb-1835-421b-8778-291367bb911b · outbound

This paper cites Phenaki: Variable Length Video Generation From Open Domain Textual Description.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Phenaki: Variable Length Video Generation From Open Domain Textual Description

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.986777Z

Source-reported events for the cited work

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Observation cff8b1f4-dd36-486e-afd6-a90a77f5da0b · outbound

This paper cites Language Models are Few-Shot Learners.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Language Models are Few-Shot Learners

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.990512Z

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Observation 8fb64557-a41c-4537-9f74-e5d8777e0df9 · outbound

This paper cites LTX-Video: Realtime Video Latent Diffusion.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models LTX-Video: Realtime Video Latent Diffusion

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.993883Z

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Observation 66ca8194-ef71-4773-bd79-63cb2d32e5ad · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:30.997415Z

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Observation 41ab125b-d277-4229-8703-468c4b842815 · outbound

This paper cites an unresolved cited work.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:31.857522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c66b55ef-4c3f-45f6-99b2-0ba148981f3a · outbound

This paper cites The free-energy principle: a unified brain theory?Nature reviews neuroscience, 11(2):127–138, 2010.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models The free-energy principle: a unified brain theory?Nature reviews neuroscience, 11(2):127–138, 2010

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.004496Z

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Observation 67829754-744a-4d5b-9ea8-e744880cbd0e · outbound

This paper cites Instructdiffusion: A generalist modeling interface for vision tasks.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Instructdiffusion: A generalist modeling interface for vision tasks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.007829Z

Source-reported events for the cited work

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Observation 9fe6de16-f95a-4d18-b878-08bc7cc51ae1 · outbound

This paper cites Few-Shot Diffusion Models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Few-Shot Diffusion Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.011153Z

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Observation a9968bef-1eca-470f-b0ac-684b9c07a69e · outbound

This paper cites an unresolved cited work.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:31.734798Z

Source-reported events for the cited work

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Observation fac107e4-a1ec-4815-a42a-e723c5a92d76 · outbound

This paper cites The llama 3 herd of models.arXiv e-prints, pages arXiv–2407, 2024.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models The llama 3 herd of models.arXiv e-prints, pages arXiv–2407, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.725623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d20e1074-9e58-42ed-9d6b-bf2282e772a6 · outbound

This paper cites Gem: A generalizable ego-vision mul- timodal world model for fine-grained ego-motion, object dynamics, and scene composition control.CVPR, 2025.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Gem: A generalizable ego-vision mul- timodal world model for fine-grained ego-motion, object dynamics, and scene composition control.CVPR, 2025

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.715712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e5d4b584-b4d5-4ec3-9d37-32aeef6aa1bb · outbound

This paper cites ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.024475Z

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Observation 24c97894-2354-4203-8680-26b925224050 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.028208Z

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Observation 1b2a258c-a87d-46cf-a217-23cba86ef8d9 · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.031550Z

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Observation 871eb699-84b9-4723-b0f0-b38910bbac91 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models LoRA: Low-rank adaptation of large language models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.035012Z

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Observation 3b29580e-2e53-47c1-9b1b-a1f72d26afbe · outbound

This paper cites LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.038292Z

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Observation 59ae6f48-dc4c-4fa6-8498-5b85dd3fb20f · outbound

This paper cites World and human action models towards gameplay ideation.Nature, 638(8051):656–663, 2025.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models World and human action models towards gameplay ideation.Nature, 638(8051):656–663, 2025

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.041881Z

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Observation 2bac4585-920e-43ab-8355-c4b405e25684 · outbound

This paper cites Flux.1-dev.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Flux.1-dev

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.687864Z

Source-reported events for the cited work

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Observation d6f6f6e8-80bc-4147-b11e-4ab67da20b9f · outbound

This paper cites Microsoft COCO: Common Objects in Context.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Microsoft COCO: Common Objects in Context

Reference 32

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Observation 68a26326-f4ad-4c45-8584-08b95c14948d · outbound

This paper cites RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.052141Z

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Observation 441411b7-2850-41a3-b225-e8d301fcf21f · outbound

This paper cites Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.055832Z

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Observation ae6c5fc6-e1bb-4699-b617-97775f7d40be · outbound

This paper cites Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.059441Z

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Observation 10d516c8-da65-4421-9223-93a00bb9a110 · outbound

This paper cites Tiny imagenet.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Tiny imagenet

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.678832Z

Source-reported events for the cited work

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Observation 35363675-3a16-4b09-a364-144e08557799 · outbound

This paper cites The conceptarc benchmark: Evaluating understanding and generalization in the arc domain.Trans.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models The conceptarc benchmark: Evaluating understanding and generalization in the arc domain.Trans

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.669753Z

Source-reported events for the cited work

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Observation 10ebee83-ba69-4bb6-a8ba-e40e8134b31a · outbound

This paper cites RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:41:31.283528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1e13f7f5-a7f7-4a11-9a83-b866ef615b19 · outbound

This paper cites Dia: A tts model capable of generating ultra-realistic dialogue in one pass.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Dia: A tts model capable of generating ultra-realistic dialogue in one pass

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.660405Z

Source-reported events for the cited work

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Observation eba69dc2-e832-47e7-9553-4903eeaa4aab · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Indoor segmentation and support inference from rgbd images

Reference 40

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Source-reported events for the cited work

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Observation 874ce09a-6a34-4af1-bf17-492ccfe38d7f · outbound

This paper cites GPT-4 Technical Report.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models GPT-4 Technical Report

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.079729Z

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Observation 1def0015-5617-4265-8bb6-27536b490489 · outbound

This paper cites MIT Press, 2022.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models MIT Press, 2022

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.645370Z

Source-reported events for the cited work

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Observation ab90e0c9-277b-4b44-a0d1-1fa1584f069f · outbound

This paper cites LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.086808Z

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Observation 844d56ca-d5c6-4c49-807f-9fe6defb1c76 · outbound

This paper cites WorldSimBench: Towards Video Generation Models as World Simulators.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models WorldSimBench: Towards Video Generation Models as World Simulators

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.090419Z

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Observation 10fb846e-223d-4945-9c33-d234b2e427dc · outbound

This paper cites Susskind.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Susskind

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.094235Z

Source-reported events for the cited work

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Observation 1e257c73-67aa-4e54-9935-035cf221317c · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models High- resolution image synthesis with latent diffusion models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.097645Z

Source-reported events for the cited work

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Observation 445a628a-cb3c-466f-90fc-6bf6bc343497 · outbound

This paper cites DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.100838Z

Source-reported events for the cited work

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Observation 79220935-2fe0-43e3-a193-84ebc349088c · outbound

This paper cites Crossing the uncanny valley of voice.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Crossing the uncanny valley of voice

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.623588Z

Source-reported events for the cited work

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Observation 9ddc00ca-4416-47b6-82f6-6db271f658a7 · outbound

This paper cites Emer- gent correspondence from image diffusion.Advances in Neural Information Processing Systems, 36:1363–1389, 2023.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Emer- gent correspondence from image diffusion.Advances in Neural Information Processing Systems, 36:1363–1389, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.613361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 36d57015-2b1e-45a3-83d4-0875288067e0 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.602813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d12b698c-db14-4fdd-b8f8-85c6a519d4fa · outbound

This paper cites Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.114364Z

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Observation b1bd5879-3624-402e-9b6e-c555a43c721b · outbound

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

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Images speak in images: A generalist painter for in-context visual learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.117747Z

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Unavailable: canonical work link unavailable.

Observation 6847a085-8b7c-41ca-bd7d-0fdd14ac06e4 · outbound

This paper cites In-context learning unlocked for diffusion models.Advances in Neural Information Processing Systems, 36:8542–8562, 2023.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models In-context learning unlocked for diffusion models.Advances in Neural Information Processing Systems, 36:8542–8562, 2023

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.121040Z

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Observation 5de88ffb-3b23-4b47-9563-6a6cfedd9a05 · outbound

This paper cites Emergent Abilities of Large Language Models.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Emergent Abilities of Large Language Models

Reference 54

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This paper cites OmniGen: Unified Image Generation.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models OmniGen: Unified Image Generation

Reference 55

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This paper cites What matters when repurposing diffusion models for general dense perception tasks?The Thirteenth International Conference on Learning Representations (ICLR), 2025.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models What matters when repurposing diffusion models for general dense perception tasks?The Thirteenth International Conference on Learning Representations (ICLR), 2025

Reference 56

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

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Video as the New Language for Real-World Decision Making

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.134881Z

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Observation 42e79d60-92ef-444e-8010-d438332c5a31 · outbound

This paper cites Open-Sora: Democratizing Efficient Video Production for All.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Open-Sora: Democratizing Efficient Video Production for All

Reference 58

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This paper cites Scene parsing through ade20k dataset.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Scene parsing through ade20k dataset

Reference 59

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Observation d9336de3-c5b4-480b-8fd5-28403e13fd93 · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.International Journal of Computer Vision, 127(3):302–321, 2019.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Semantic understanding of scenes through the ade20k dataset.International Journal of Computer Vision, 127(3):302–321, 2019

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.565880Z

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This paper cites This is impractical, as it requires 17 visually distinct colors.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models This is impractical, as it requires 17 visually distinct colors

Reference 61

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Observation 92fb84dc-0f4c-4d51-9733-6dd64eed3900 · outbound

This paper cites However, this increases inference costs by nearly a factor of 20, which is prohibitive for our setup where inference speed is already a bottleneck.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models However, this increases inference costs by nearly a factor of 20, which is prohibitive for our setup where inference speed is already a bottleneck

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.543173Z

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From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:41:31.532560Z

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Observation 7a1916ad-60c0-4feb-a264-55fd681c7d33 · outbound

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From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Unresolved cited work

Reference 64

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Observation 67d6b4cd-1777-4c38-a140-ff00ce3dce88 · outbound

This paper cites an unresolved cited work.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Unresolved cited work

Reference 65

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Observation deeb5fd9-6c0f-43ac-8ede-674ea125f838 · outbound

This paper cites segment animals.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models segment animals

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:41:31.502352Z

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Pith citing papers

Observation 88ce5459-4164-4a44-985c-3275a995d336 · inbound

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

Video models are zero-shot learners and reasoners From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

Reference 15

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Observation 7cf32b5c-478a-4a61-b240-98418763a06a · inbound

3D MRI Image Pretraining via Controllable 2D Slice Navigation Task cites this paper.

3D MRI Image Pretraining via Controllable 2D Slice Navigation Task From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

Reference 1

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Observation 3e64fb90-010c-41ad-a446-94516e9c90d5 · inbound

World Model Self-Distillation: Training World Models to Solve General Tasks cites this paper.

World Model Self-Distillation: Training World Models to Solve General Tasks From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

Reference 1

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Observation 46f2b52c-3de1-473c-9ffb-ab0dfdc56485 · inbound

Video Generation Models are General-Purpose Vision Learners cites this paper.

Video Generation Models are General-Purpose Vision Learners From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models

Reference 1

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