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Paper Citation Record · LEDGER

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration

As of 18 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.16602.

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

pith.paper-citation-record.v1
2607.16602 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:30:44.812933Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

36 of 36 outbound references displayed

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  • unresolved36
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation cbaae70f-e6bf-4b66-8b03-9fb849b55596 · outbound

This paper cites Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation

Reference 4

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source=pdf_text observed=2026-08-01T20:30:40.895214Z digest=sha256:d5f542c0777809399093f132d6812f00d94acfc3c0f6ae31a5d5f0a6096e855e

Observation c10ddbfd-c93d-49f2-a0fa-8d2273be62e4 · outbound

This paper cites AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

Reference 5

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Observation 7518812a-a323-47d0-bbf3-da174e8e74a0 · outbound

This paper cites an unresolved cited work.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Unresolved cited work

Reference 6

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Observation ac7ab751-59db-4aba-bee1-78be359240a4 · outbound

This paper cites Wow: Towards a world omniscient model through embodied interaction.arXiv preprint arXiv:2509.22642,.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Wow: Towards a world omniscient model through embodied interaction.arXiv preprint arXiv:2509.22642,

Reference 7

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Observation fc76cf69-284b-4193-a9e7-acff560b5182 · outbound

This paper cites Vidar: Embodied Video Diffusion Model for Generalist Manipulation.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Vidar: Embodied Video Diffusion Model for Generalist Manipulation

Reference 8

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Observation 835f39e4-287b-492b-a7dc-74e9b99309bd · outbound

This paper cites Gigaworld-0: World models as data engine to empower embodied ai.arXiv preprint arXiv:2511.19861,.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Gigaworld-0: World models as data engine to empower embodied ai.arXiv preprint arXiv:2511.19861,

Reference 9

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Observation d651d21a-704b-4b18-9b19-d9c2292be197 · outbound

This paper cites FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model

Reference 10

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source=pdf_text observed=2026-08-01T20:30:41.733860Z digest=sha256:03952c8ee05e7850a3d89ffe2d9a6277d3966d8f1d5bfcb6f01e4676118fc84b

Observation f9a15e5c-90bc-4faa-9318-195435590c65 · outbound

This paper cites DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos

Reference 11

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source=pdf_text observed=2026-08-01T20:30:41.863048Z digest=sha256:9223f709e4113d6340d85762f84af238189a6fc1f16a323b270d9104d333fc1b

Observation ef6610a1-ee6b-4e4a-8c94-73ab24559219 · outbound

This paper cites Intuitive physics understanding emerges from self-supervised pretraining on natural videos.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Intuitive physics understanding emerges from self-supervised pretraining on natural videos

Reference 12

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source=pdf_text observed=2026-08-01T20:30:41.923953Z digest=sha256:fd2d50e9880aad7261ffd7e5c167167de8f980b5bf15ca591508794efec4ad5b

Observation 7ca6f6d1-b20c-476c-959d-514f6e3e80d3 · outbound

This paper cites Ctrl-World: A Controllable Generative World Model for Robot Manipulation.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Ctrl-World: A Controllable Generative World Model for Robot Manipulation

Reference 13

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Observation b979dbaa-24ea-4f19-bc73-77816d4118fd · outbound

This paper cites WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL

Reference 15

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Observation 7ee0e438-1e3b-4816-8e1d-e5542b8dd38d · outbound

This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 16

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Observation 5a142909-dc06-4269-abf7-869ada2d09bc · outbound

This paper cites Segment Anything.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Segment Anything

Reference 17

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source=pdf_text observed=2026-08-01T20:30:42.548666Z digest=sha256:d54899225102ae5f1b1494be7c10b19c02a252eb59fc3d2641d368518c0a21a2

Observation f8351085-71b3-4046-b884-51628f00edf5 · outbound

This paper cites Dreamitate: Real-World Visuomotor Policy Learning via Video Generation.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Dreamitate: Real-World Visuomotor Policy Learning via Video Generation

Reference 19

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Observation 46a71396-c459-484f-ab79-bf15b935c35f · outbound

This paper cites Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

Reference 20

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Observation fe9cea05-9176-457a-a327-ee2baa00f1af · outbound

This paper cites Flow Matching Policy Gradients.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Flow Matching Policy Gradients

Reference 21

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Observation 1bce67c5-1919-4cc8-8ef8-9f17637a47c2 · outbound

This paper cites an unresolved cited work.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Unresolved cited work

Reference 23

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Observation cc7340d3-fc1b-44b2-96bb-847bd1ca10a1 · outbound

This paper cites AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation

Reference 24

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Observation fe581c1d-702a-4898-b557-e0a23d583abd · outbound

This paper cites Advancing Open-source World Models.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Advancing Open-source World Models

Reference 25

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Observation 2a897ddf-d53c-42dd-b416-b0048a52ccd9 · outbound

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

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Wan: Open and Advanced Large-Scale Video Generative Models

Reference 26

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Observation 62a73ad0-c200-436f-a05d-1488218105b9 · outbound

This paper cites an unresolved cited work.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Unresolved cited work

Reference 28

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Observation c454e112-6e83-4f75-ab9e-3b967702b890 · outbound

This paper cites World Action Models are Zero-shot Policies.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration World Action Models are Zero-shot Policies

Reference 29

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Observation b2259c60-5796-456d-996a-39b25c8e0395 · outbound

This paper cites an unresolved cited work.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Unresolved cited work

Reference 30

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Observation ce468fb6-1f63-4289-8872-b1efda046f9e · outbound

This paper cites Zhang, Z.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Zhang, Z

Reference 31

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source=pdf_text observed=2026-08-01T20:30:44.378851Z digest=sha256:4001a4c5585ec60b1c28b2fa91d1f7497a140e44a8edbb71bad12d233d576eae

Observation 22b79230-0cae-4057-9ba5-830856e48c95 · outbound

This paper cites Zhang, H.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Zhang, H

Reference 32

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Observation f6ffa1e8-1b9b-43e3-9dcf-5b0da6407561 · outbound

This paper cites DiffusionNFT: Online Diffusion Reinforcement with Forward Process.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration DiffusionNFT: Online Diffusion Reinforcement with Forward Process

Reference 33

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Observation 35227d53-5af8-4eb7-b2ae-a65fdd183184 · outbound

This paper cites Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 34

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Observation da77a5fb-c314-4e99-a494-7fda8291fd78 · outbound

This paper cites IRASim: A Fine-Grained World Model for Robot Manipulation.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration IRASim: A Fine-Grained World Model for Robot Manipulation

Reference 35

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Observation 1b3fddde-3842-4c27-bb9c-e29e8ee46b31 · outbound

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PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Unresolved cited work

Reference 36

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Observation 9f2216fb-6351-4fd5-a78e-f4864cf7e610 · outbound

This paper cites Worldcom- pass: Reinforcement learning for long-horizon world models.arXiv preprint arXiv:2602.09022, 2026b.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Worldcom- pass: Reinforcement learning for long-horizon world models.arXiv preprint arXiv:2602.09022, 2026b

Reference 2004

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Observation 9a01b61f-002e-4ced-a348-35c46999cb42 · outbound

This paper cites Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

Reference 2010

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Observation 6c688b7d-c638-413b-9df5-639c99fd4561 · outbound

This paper cites Evaluating robot policies in a world model.arXiv preprint arXiv:2506.00613,.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Evaluating robot policies in a world model.arXiv preprint arXiv:2506.00613,

Reference 2013

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Observation 6e658dfb-886a-47e0-a332-ee637673de36 · outbound

This paper cites Unified Video Action Model.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Unified Video Action Model

Reference 2018

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Observation dfb48152-7642-4099-a81f-09edefca2ccb · outbound

This paper cites Bardhan, P.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Bardhan, P

Reference 2024

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source=pdf_text observed=2026-08-01T20:30:40.760132Z digest=sha256:97baa381975a1b20d70f5b3d7254cd609f3db4c90913f304a256991e136102ff

Observation e516b15a-4060-4965-9e63-344c22bc24c6 · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 2025

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Observation 727962d1-6e52-4975-b144-750d5353919d · outbound

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

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration Cosmos World Foundation Model Platform for Physical AI

Reference 2026

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source=pdf_text observed=2026-08-01T20:30:40.547441Z digest=sha256:7c1f59483b6e87ce7ae27d859f9ea135b5c2ef785545d0e1cb6fd07c60f30e6e

Pith citing papers

No inbound Pith citation observations are available.