Paper Citation Record · LEDGER

In-Context World Modeling for Robotic Control

As of 6 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2606.26025.

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

pith.paper-citation-record.v1
2606.26025 v3

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T12:05:57.682386Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy0
unresolved48
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

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Outbound references

Observation b1df1a76-ee05-49e6-8b39-154d2e7b9ab8 · outbound

This paper cites Tran, Radu Soricut, Anikait Singh, Jaspiar Singh, Pierre Sermanet, Pannag R.

In-Context World Modeling for Robotic Control Tran, Radu Soricut, Anikait Singh, Jaspiar Singh, Pierre Sermanet, Pannag R

Reference 1

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Observation 8854b983-6229-4b40-8119-d8f4191cd4c2 · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

In-Context World Modeling for Robotic Control OpenVLA: An Open-Source Vision-Language-Action Model

Reference 2

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Observation 7e5dd307-c5af-4138-a903-f431b656dfe0 · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

In-Context World Modeling for Robotic Control $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 3

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Observation 15a18cb3-2460-47d5-9b0b-325163ef454e · outbound

This paper cites LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models.

In-Context World Modeling for Robotic Control LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Reference 4

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Observation 33548797-233d-45ab-9870-ef5bb28ef4cc · outbound

This paper cites Goldberg.

In-Context World Modeling for Robotic Control Goldberg

Reference 5

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Observation 10c874f3-da37-429e-a71c-642627d80fd2 · outbound

This paper cites MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos.

In-Context World Modeling for Robotic Control MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos

Reference 6

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Observation 01eea3b5-9a98-4db3-a828-c3714bbf94fc · outbound

This paper cites Language models are unsu- pervised multitask learners.

In-Context World Modeling for Robotic Control Language models are unsu- pervised multitask learners

Reference 7

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Observation 89fb8244-9533-4f84-ae6a-c69f69909b7a · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

In-Context World Modeling for Robotic Control LLaMA: Open and Efficient Foundation Language Models

Reference 8

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Observation 634f9de5-da21-43d8-ac64-6c001529eab8 · outbound

This paper cites Introducing chatgpt, 2022.

In-Context World Modeling for Robotic Control Introducing chatgpt, 2022

Reference 9

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Observation d7204318-91ef-4745-bf39-aab6d526410c · outbound

This paper cites Language Models are Few-Shot Learners.

In-Context World Modeling for Robotic Control Language Models are Few-Shot Learners

Reference 10

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Observation 4c2e2353-54bb-47c1-b80e-5b9d827b5e66 · outbound

This paper cites an unresolved cited work.

In-Context World Modeling for Robotic Control Unresolved cited work

Reference 11

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Observation 1b2e6d06-ab0f-43f9-b416-ae5d83bd4d08 · outbound

This paper cites A survey on in-context learning.

In-Context World Modeling for Robotic Control A survey on in-context learning

Reference 12

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Observation 2d45bfad-5859-4b5c-85e3-7871140399d3 · outbound

This paper cites an unresolved cited work.

In-Context World Modeling for Robotic Control Unresolved cited work

Reference 13

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Observation 991f3055-7b1e-4e86-b084-7f627d3266ab · outbound

This paper cites RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models.

In-Context World Modeling for Robotic Control RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models

Reference 14

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Observation a3d179df-d985-4252-8814-069ac488d1a8 · outbound

This paper cites Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers.

In-Context World Modeling for Robotic Control Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers

Reference 15

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Observation ae691da8-7d10-4b7f-9b9a-93081501e768 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

In-Context World Modeling for Robotic Control Model-agnostic meta-learning for fast adaptation of deep networks

Reference 16

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Observation dbb5135e-6fe0-4a1c-9c50-8380b597cca4 · outbound

This paper cites Meta-learning with implicit gradients.

In-Context World Modeling for Robotic Control Meta-learning with implicit gradients

Reference 17

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Observation a9188aa6-fa40-4ba2-968b-72c445373802 · outbound

This paper cites RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

In-Context World Modeling for Robotic Control RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Reference 18

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Observation 40ae2581-9fbb-4985-ba1e-cffcc7728cb5 · outbound

This paper cites VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.

In-Context World Modeling for Robotic Control VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Reference 19

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Observation bc72f299-86a6-4f91-974b-d650da4b97bf · outbound

This paper cites Recurrent world models facilitate policy evolution.

In-Context World Modeling for Robotic Control Recurrent world models facilitate policy evolution

Reference 20

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Observation 02dae480-14c3-4d8f-b110-f7756792684f · outbound

This paper cites A path towards autonomous machine intelligence version 0.9.2, 2022-06-27.

In-Context World Modeling for Robotic Control A path towards autonomous machine intelligence version 0.9.2, 2022-06-27

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T12:05:57.682386Z

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Observation f258e5c0-d90b-4198-8fe8-39e44f276f80 · outbound

This paper cites Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, and Yong Li.

In-Context World Modeling for Robotic Control Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, and Yong Li

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T12:05:57.682386Z

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Observation 8ef4dd4e-a938-4f05-be5a-96a998163478 · outbound

This paper cites World modeling makes a better planner: Dual preference optimization for embodied task planning.

In-Context World Modeling for Robotic Control World modeling makes a better planner: Dual preference optimization for embodied task planning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T12:05:57.682386Z

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Observation a8472bcc-b993-47b1-8e27-10ebe7917b59 · outbound

This paper cites World Action Models: The Next Frontier in Embodied AI.

In-Context World Modeling for Robotic Control World Action Models: The Next Frontier in Embodied AI

Reference 24

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Observation 11491788-8080-4c54-b82a-8f4dc8eea142 · outbound

This paper cites Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation.

In-Context World Modeling for Robotic Control Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T12:05:57.682386Z

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Observation 05e812df-2eb5-4c04-bb7d-d2b3b462136b · outbound

This paper cites Gr-mg: Leveraging partially- annotated data via multi-modal goal-conditioned policy.IEEE Robotics and Automation Letters, 10:1912–1919, 2024.

In-Context World Modeling for Robotic Control Gr-mg: Leveraging partially- annotated data via multi-modal goal-conditioned policy.IEEE Robotics and Automation Letters, 10:1912–1919, 2024

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T12:05:57.682386Z

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Observation 3d642e5d-0b08-4d52-9f4b-dfd9f1040d5e · outbound

This paper cites Cot-vla: Visual chain-of-thought reasoning for vision-language-action models.

In-Context World Modeling for Robotic Control Cot-vla: Visual chain-of-thought reasoning for vision-language-action models

Reference 27

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Observation d421a32d-c2da-4a94-a5c4-a007bd0fc9dd · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

In-Context World Modeling for Robotic Control WorldVLA: Towards Autoregressive Action World Model

Reference 28

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Observation d166357b-048b-4b27-8576-4eb61fe6db4b · outbound

This paper cites an unresolved cited work.

In-Context World Modeling for Robotic Control Unresolved cited work

Reference 29

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Observation 9207b1e0-ddf9-44f6-ae2d-c01e81b25534 · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

In-Context World Modeling for Robotic Control Dream to Control: Learning Behaviors by Latent Imagination

Reference 30

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Observation e1af1b5d-5d00-4573-9b15-51775a892522 · outbound

This paper cites Mastering Atari with Discrete World Models.

In-Context World Modeling for Robotic Control Mastering Atari with Discrete World Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T12:05:57.682386Z

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Observation 7e91f066-5196-47f3-88e2-f147a4071f11 · outbound

This paper cites an unresolved cited work.

In-Context World Modeling for Robotic Control Unresolved cited work

Reference 32

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Observation 9576160b-45d9-4763-82b9-8a9e0316e2b9 · outbound

This paper cites Mastering Diverse Domains through World Models.

In-Context World Modeling for Robotic Control Mastering Diverse Domains through World Models

Reference 33

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Observation 4574b55d-9165-4fbe-80a4-d7b5ebcf7bb6 · outbound

This paper cites FLARE: Robot Learning with Implicit World Modeling.

In-Context World Modeling for Robotic Control FLARE: Robot Learning with Implicit World Modeling

Reference 34

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Observation 8510c816-092d-4cd2-8017-c403cec1f0a9 · outbound

This paper cites Learning Universal Policies via Text-Guided Video Generation.

In-Context World Modeling for Robotic Control Learning Universal Policies via Text-Guided Video Generation

Reference 35

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Observation aafaf21b-b51c-4fad-92d5-7d888d9039c3 · outbound

This paper cites Zettlemoyer, Di- eter Fox, Jan Kautz, Scott Reed, Yuke Zhu, and Linxi Fan.

In-Context World Modeling for Robotic Control Zettlemoyer, Di- eter Fox, Jan Kautz, Scott Reed, Yuke Zhu, and Linxi Fan

Reference 36

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Observation d7c46a45-4884-4290-9bd4-133a15ab2c91 · outbound

This paper cites Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation.

In-Context World Modeling for Robotic Control Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation

Reference 37

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Observation de3919ae-247d-4613-8294-24c54513fe49 · outbound

This paper cites Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos.

In-Context World Modeling for Robotic Control Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

Reference 38

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Observation 16c0885e-318f-45c7-bc04-a7ca2733857c · outbound

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

In-Context World Modeling for Robotic Control Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 39

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Observation 5555dc63-1091-4ee1-84b3-c38efaa6a296 · outbound

This paper cites Unified Video Action Model.

In-Context World Modeling for Robotic Control Unified Video Action Model

Reference 40

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Observation d82f7f79-ef3c-490e-b713-4d4e372d2351 · outbound

This paper cites LIBERO: bench- marking knowledge transfer for lifelong robot learning.

In-Context World Modeling for Robotic Control LIBERO: bench- marking knowledge transfer for lifelong robot learning

Reference 41

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Observation 9724045d-f132-4d00-97ba-85db2827e6fa · outbound

This paper cites NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks.

In-Context World Modeling for Robotic Control NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks

Reference 42

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Observation d8dd1ae6-f5b5-48b0-adb1-ccae9916e19d · outbound

This paper cites an unresolved cited work.

In-Context World Modeling for Robotic Control Unresolved cited work

Reference 43

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Observation f3bf165e-ec03-4184-8d9e-a407469b9deb · outbound

This paper cites FAST: Efficient Action Tokenization for Vision-Language-Action Models.

In-Context World Modeling for Robotic Control FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 44

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Observation 0727ca1a-1557-404e-a60d-bff9e890baa1 · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

In-Context World Modeling for Robotic Control $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 45

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Observation e412e9f4-ed6f-4472-b26d-980540d45bd3 · outbound

This paper cites Qwen2.5-VL Technical Report.

In-Context World Modeling for Robotic Control Qwen2.5-VL Technical Report

Reference 46

Resolution: malformed identifier; no resolver link, observed 2026-07-12T12:05:57.682386Z

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Observation c11c39ad-f0ba-48f8-8b49-118b3895eb22 · outbound

This paper cites Put the toy on the box into the basket.

In-Context World Modeling for Robotic Control Put the toy on the box into the basket

Reference 47

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Observation 403dd563-3d78-4766-a734-332b3132287f · outbound

This paper cites Stack the yellow cup onto the red cup.

In-Context World Modeling for Robotic Control Stack the yellow cup onto the red cup

Reference 48

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Observation 02f44524-143e-4b00-8b43-ecce5a5e912a · outbound

This paper cites Lift the basket.

In-Context World Modeling for Robotic Control Lift the basket

Reference 49

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Observation d3564db7-25ab-4d11-8630-ba0d299882a5 · outbound

This paper cites Pick up the eggplant and place it onto the red plate.

In-Context World Modeling for Robotic Control Pick up the eggplant and place it onto the red plate

Reference 50

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Pith citing papers

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