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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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

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

Source: cited_works

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=sha256:8fbfe0cae85e0e4cc527353182835b2b9931da1ce1005e0862148ce54196cdbc

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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source=pdf_text observed=2026-08-01T20:30:41.304130Z digest=sha256:9ae8ad28bd244289e17c0a71c62aefcd0c35e6c24ac6cb91a4f627e5b424763a

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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source=pdf_text observed=2026-08-01T20:30:41.447770Z digest=sha256:477abe8b5f3a192c399763fba5f8cd251b547e1bc73b4052a1b29c6688de1e31

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=sha256:42ce22a1f7dc4caeb0b50a121ef67652f60242a4997e93a60b755e150bb0804e

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=sha256:566dc28c7bf46d97f1bcb02a7151de9e0c5e454b8cff630c7698da99287161b6

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:a60f3224fc48083e07ffabdeec111a92c3b2819cfd3e0464c5f1fc9d69f0895b

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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source=pdf_text observed=2026-08-01T20:30:42.109330Z digest=sha256:dab58944330df02f9d992b97eaf4b7d1ea6fcc3676375732ab245746a4e3d9bd

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=sha256:7c913f107d3c71e365ce8c13798735e5dec62b472cbf3eafe2c6cc12691fb5b1

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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source=pdf_text observed=2026-08-01T20:30:43.078545Z digest=sha256:987456ae09123c5daa4e8a370c5af938c5435e1a712d95a0606828915006b9f6

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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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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source=pdf_text observed=2026-08-01T20:30:44.812933Z digest=sha256:9f09c907e08b5433bf51d54407df44c58ceacaf1f8914a98f19e7f1e7ee83038

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=sha256:0de46f9a4e2dd71c45f3eb8b420d6dab5d62b8d3d370a42a82686a6fb54738e0

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=sha256:68a74d569c4bee3e07b052e57472a94a34681558ae55b90dbcf0effb9cb44ac1

Pith citing papers

No inbound Pith citation observations are available.