Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-13T04:50:59.096166Z
Paper Citation Record · LEDGER
As of 18 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2607.09185.
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 2026-07-13T04:50:59.096166Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
42 of 42 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 07ff60ca-cf46-438c-a5de-7b3cb3e70769 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models World Models
Reference 1
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Observation c56cc561-a74c-4f46-af9d-b36d484f4488 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Mastering Diverse Domains through World Models
Reference 2
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Observation bc35251b-d7bf-4213-a791-a5e00d354efe · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models ACWM-Phys: Investigating Generalized Physical Interaction in Action-Conditioned Video World Models
Reference 3
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Reference 4
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Reference 5
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Observation fd6e7070-44d1-40a2-abfa-0ebc86095f67 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models DROID: A large-scale in-the-wild robot manipulation dataset,
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Reference 7
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Observation eb1b3aed-65c2-4bf0-b7ec-392c70ec90b1 · outbound
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Observation 49f771d0-1a82-455c-8909-d25469ac2628 · outbound
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Reference 9
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Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Genie: Generative Interactive Environments
Reference 10
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Observation 0b0088f1-d016-4180-b812-fa525f3e1e18 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Learning to Act without Actions
Reference 11
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Observation a2599d80-5fd4-47f8-a549-f48fc204851d · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Latent action pretraining from videos,
Reference 12
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Observation 2489aaeb-58cb-410f-b335-30fcfe95d736 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models AdaWorld: Learning adaptable world models with latent actions,
Reference 13
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Observation 2ce2a0cb-13ef-471e-850f-08e498e7c0fb · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
Reference 14
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Observation 1bf6a949-6898-4de4-b09e-1f4ba0276d29 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models What do latent action models actually learn?
Reference 15
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Observation 2889d3d0-c090-4ed8-bbb2-9f8e68f98a9c · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Latent action learning requires supervision in the presence of distractors,
Reference 16
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Observation 64c48135-0882-44c7-854f-9726a42acdfa · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models ConLA: Contrastive latent action learning from human videos for robotic manipulation,
Reference 17
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Observation 7093808b-a0ee-45b4-bd49-78e95d7cf9f4 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
Reference 18
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Observation a510bd4b-146e-4949-b55a-2acd362e7474 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models SAM 3: Segment Anything with Concepts
Reference 19
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Observation 28fd2190-bc54-4d8b-a879-3ef00929c39a · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Sigmoid loss for language image pre-training,
Reference 20
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Observation 92211946-b2b6-4c53-981c-0b6d69a7929b · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video
Reference 21
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Observation cb5e7da1-cf19-4ccd-87ac-c767f075f3c4 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models CoWTracker: Tracking by warping instead of correlation,
Reference 22
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Observation 570925d7-b870-407b-a657-1f97499ede68 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Imitating Latent Policies from Observation
Reference 23
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Observation f7f8dd81-8c56-470d-a611-dedaaa8bd49e · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Reference 24
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Observation aeedf3a3-a40c-4494-a72e-640ce40a7752 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Moto: Latent motion token as the bridging language for learning robot manipulation from videos,
Reference 25
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Observation db4fb4c8-8d36-4d37-864c-cb06c02743e2 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI
Reference 26
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Observation 9bb717eb-f5a8-4154-81d7-1e521b2243d3 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Learning Additively Compositional Latent Actions for Embodied AI
Reference 27
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Observation 8ba35f2f-fe21-4efc-b25e-f6f0e0310f2a · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Co-Evolving Latent Action World Models
Reference 28
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Observation ca9bb646-ee94-41ac-a0c2-d12542f47f86 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models MVP-LAM: Learning Action-Centric Latent Action via Cross-Viewpoint Reconstruction
Reference 29
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Observation 0049136e-ec93-4301-b862-643d174158a8 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Masked autoencoders are scalable vision learners,
Reference 30
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Observation c18b0991-b9b4-4903-9c65-262d1e7a13bb · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Learning Interactive Real-World Simulators
Reference 31
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Observation 8d4d4af8-a7f1-4923-b6fb-465f31ec47d2 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Cosmos World Foundation Model Platform for Physical AI
Reference 32
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Observation 50789b1f-b1fd-4b8e-b193-07e5118e0672 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Towards Accurate Generative Models of Video: A New Metric & Challenges
Reference 33
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Observation 9b5b6ca2-1e64-4f27-9acb-7c455d8b09cf · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models MotionPro: A precise motion controller for image-to-video generation,
Reference 34
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Observation 268c3f36-dd7c-47aa-b8dd-8d261ff1f503 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models TAP-Vid: A Benchmark for Tracking Any Point in a Video
Reference 35
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Observation a6edee24-b5d8-4984-82ee-b32839e575b0 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement
Reference 36
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Observation 9f703488-beda-418c-a350-15d8521fce8b · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models CoTracker: It is better to track together,
Reference 37
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Observation 7af3ad13-35c2-4d17-8da9-387d7214f105 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Segment Anything
Reference 38
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Observation 5a662e86-4bac-4262-a032-1301b9122dc2 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models SAM 2: Segment Anything in Images and Videos
Reference 39
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Observation 8bad13f7-2533-41a3-9454-38da8e11f9c9 · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Invariant Risk Minimization
Reference 40
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Observation e49a5b0d-91ff-4aef-b5bd-efa9c1b7768e · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Shortcut learning in deep neural networks,
Reference 41
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Observation f313a658-7172-4e12-8462-dfd04f4cda4c · outbound
Causally Debiased Latent Action Model for Embodied Action Conditioned World Models Causal confusion in imita- tion learning,
Reference 42
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No inbound Pith citation observations are available.