Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:24:27.413833Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 17 inbound Pith citation observations for arXiv:2507.19468.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:24:27.413833Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T20:38:55.747199Z
70 of 70 outbound references displayed
External citation measurements
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Observation b1793104-a74f-4e87-964c-91983d040163 · outbound
Back to the Features: DINO as a Foundation for Video World Models Recurrent world models facilitate policy evolution
Reference 1
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Back to the Features: DINO as a Foundation for Video World Models GAIA-1: A Generative World Model for Autonomous Driving
Reference 2
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Back to the Features: DINO as a Foundation for Video World Models Learning interactive real-world simulators
Reference 3
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Back to the Features: DINO as a Foundation for Video World Models Video gen- eration models as world simulators
Reference 4
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Back to the Features: DINO as a Foundation for Video World Models VaViM and VaVAM: Autonomous Driving through Video Generative Modeling
Reference 7
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Back to the Features: DINO as a Foundation for Video World Models Cosmos World Foundation Model Platform for Physical AI
Reference 8
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Back to the Features: DINO as a Foundation for Video World Models Movie Gen: A Cast of Media Foundation Models
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Reference 11
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Back to the Features: DINO as a Foundation for Video World Models A path towards autonomous machine intelligence, 2022
Reference 12
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Reference 13
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Back to the Features: DINO as a Foundation for Video World Models Eyes wide shut? exploring the visual shortcomings of multimodal llms
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Back to the Features: DINO as a Foundation for Video World Models Mastering diverse control tasks through world models
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Reference 17
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Back to the Features: DINO as a Foundation for Video World Models DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
Reference 18
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Reference 19
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Reference 20
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Back to the Features: DINO as a Foundation for Video World Models Intuitive physics understanding emerges from self-supervised pretraining on natural videos
Reference 21
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Back to the Features: DINO as a Foundation for Video World Models Do generative video models understand physical principles?
Reference 22
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Back to the Features: DINO as a Foundation for Video World Models Dinov2: Learning robust visual features without supervision
Reference 23
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Back to the Features: DINO as a Foundation for Video World Models Revisiting feature prediction for learning visual representations from video
Reference 24
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Back to the Features: DINO as a Foundation for Video World Models Making the world differentiable: on using self supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environments
Reference 26
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Back to the Features: DINO as a Foundation for Video World Models Goodwin and K.S
Reference 27
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Back to the Features: DINO as a Foundation for Video World Models Lillicrap, Jimmy Ba, and Mohammad Norouzi
Reference 28
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Back to the Features: DINO as a Foundation for Video World Models Transformers are sample-efficient world models
Reference 29
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Back to the Features: DINO as a Foundation for Video World Models Lillicrap, Ian S
Reference 30
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Back to the Features: DINO as a Foundation for Video World Models Navigation World Models
Reference 31
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Back to the Features: DINO as a Foundation for Video World Models Video pixel networks
Reference 32
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Back to the Features: DINO as a Foundation for Video World Models Campbell, and Sergey Levine
Reference 33
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Back to the Features: DINO as a Foundation for Video World Models Stochastic video generation with a learned prior
Reference 34
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Back to the Features: DINO as a Foundation for Video World Models VideoGPT: Video Generation using VQ-VAE and Transformers
Reference 35
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Back to the Features: DINO as a Foundation for Video World Models Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, and Lu Jiang
Reference 36
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Back to the Features: DINO as a Foundation for Video World Models Photorealistic video generation with diffusion models
Reference 37
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Back to the Features: DINO as a Foundation for Video World Models Phenaki: Variable length video generation from open domain textual descriptions
Reference 38
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Reference 39
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Back to the Features: DINO as a Foundation for Video World Models Action-conditional video prediction using deep networks in atari games
Reference 40
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Reference 41
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Back to the Features: DINO as a Foundation for Video World Models How Far is Video Generation from World Model: A Physical Law Perspective
Reference 42
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Back to the Features: DINO as a Foundation for Video World Models Predicting deeper into the future of semantic segmentation
Reference 43
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Back to the Features: DINO as a Foundation for Video World Models Segmenting the future
Reference 44
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Back to the Features: DINO as a Foundation for Video World Models Predicting future instance segmentation by forecasting convolutional features
Reference 45
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Back to the Features: DINO as a Foundation for Video World Models Anticipating visual representations from unlabeled video
Reference 46
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Reference 47
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Back to the Features: DINO as a Foundation for Video World Models Anticipative feature fusion transformer for multi-modal action anticipation
Reference 48
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Back to the Features: DINO as a Foundation for Video World Models Auto-encoding variational bayes
Reference 49
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Reference 50
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Reference 51
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Reference 52
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Back to the Features: DINO as a Foundation for Video World Models Attention is all you need
Reference 53
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Back to the Features: DINO as a Foundation for Video World Models Rethinking patch dependence for masked autoencoders
Reference 54
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Back to the Features: DINO as a Foundation for Video World Models Roformer: Enhanced transformer with rotary position embedding
Reference 55
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Reference 56
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Back to the Features: DINO as a Foundation for Video World Models Decoupled weight decay regularization
Reference 57
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Reference 58
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Back to the Features: DINO as a Foundation for Video World Models The Kinetics Human Action Video Dataset
Reference 61
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Back to the Features: DINO as a Foundation for Video World Models Vspw: A large-scale dataset for video scene parsing in the wild
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Reference 63
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Back to the Features: DINO as a Foundation for Video World Models Intphys 2019: A benchmark for visual intuitive physics understanding
Reference 64
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Back to the Features: DINO as a Foundation for Video World Models Grasp: A novel benchmark for evaluating language grounding and situated physics understanding in multimodal language models
Reference 65
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Back to the Features: DINO as a Foundation for Video World Models Benchmarking progress to infant-level physical reasoning in AI
Reference 66
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Back to the Features: DINO as a Foundation for Video World Models Scaling rectified flow transformers for high-resolution image synthesis
Reference 67
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Back to the Features: DINO as a Foundation for Video World Models Diffusion policy: Visuomotor policy learning via action diffusion
Reference 68
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Back to the Features: DINO as a Foundation for Video World Models D4RL: Datasets for Deep Data-Driven Reinforcement Learning
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Reference 70
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Reference 182
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Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations Back to the Features: DINO as a Foundation for Video World Models
Reference 3
Source-reported events for the cited work
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Observation ae265a73-b4f7-4c7b-b72d-bfb4a17d3ea0 · inbound
Envision4D: Envisioning Visual Futures via Feed-forward 4D Gaussian Splatting for Autonomous Driving Back to the Features: DINO as a Foundation for Video World Models
Reference 52
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Observation bb059efd-1c82-45a5-bb56-67e876b71806 · inbound
Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application Back to the Features: DINO as a Foundation for Video World Models
Reference 214
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Observation c07cf121-1c4a-42a8-8068-55d7b3522e2e · inbound
Future Dynamic 3D Reconstruction: A 3D World Model with Disentangled Ego-Motion Back to the Features: DINO as a Foundation for Video World Models
Reference 2
Source-reported events for the cited work
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Observation 1c8089d9-01b6-4a7d-bfb3-f439e3855bb7 · inbound
Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling Back to the Features: DINO as a Foundation for Video World Models
Reference 117
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Observation 645df39c-c401-4de0-81ef-3b53daab1ac7 · inbound
Self-Supervised Learning of Structured Dynamics from Videos Back to the Features: DINO as a Foundation for Video World Models
Reference 2
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Unavailable: canonical work link unavailable.
Observation f1a7fb7f-ec3d-455c-afbc-2c92c56a6548 · inbound
Failure Detection for Surgical Robot Imitation Policies via Flow-Matching World Modeling Back to the Features: DINO as a Foundation for Video World Models
Reference 26
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Unavailable: canonical work link unavailable.
Observation 291abc56-8acb-4515-949a-231de4f18a6e · inbound
DF$^3$: World Modeling via Decoder-Free Feature Forecasting in Autonomous Navigation Back to the Features: DINO as a Foundation for Video World Models
Reference 35
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Unavailable: canonical work link unavailable.
Observation 4e666f9c-e272-4158-ab78-ad6e6b9fe3e9 · inbound
Uncertainty-Aware World Model for Aerial Image-Goal Navigation Back to the Features: DINO as a Foundation for Video World Models
Reference 4
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