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Paper Citation Record · LEDGER

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control

As of 17 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2506.16565.

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

pith.paper-citation-record.v1
2506.16565 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:30:24.212485Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:50:47.113217Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:44.875570Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact2
  • verified fuzzy13
  • unresolved16
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation d20fab1e-5adc-46d7-b6c4-8905d180cc3a · outbound

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

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Cosmos World Foundation Model Platform for Physical AI

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:30:24.065632Z digest=sha256:fb9903a4e248a2ec0336e227937e90aa2d35715df5cfeb536b48ee11bc4c09a5

Observation 197503eb-b3b5-437b-be95-2d04afb2fd99 · outbound

This paper cites RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning

Reference 2

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source=pdf_text observed=2026-08-15T19:30:24.071321Z digest=sha256:61e0a30ea431a17747a6e95aeebdd738488518169d8daa91628822b5b3139cb0

Observation cdff10da-e50f-4438-a819-9d6512ef8618 · outbound

This paper cites Dif- fusion policy: Visuomotor policy learning via action diffusion.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Dif- fusion policy: Visuomotor policy learning via action diffusion

Reference 3

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source=pdf_text observed=2026-08-15T19:30:24.076656Z digest=sha256:1234761019c5419a3938bb8f6465c022b7baa26391f21a3c321da729ddb66d3a

Observation a1c84413-1946-4026-bb19-7fb10991f367 · outbound

This paper cites Improving Transformer World Models for Data-Efficient RL.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Improving Transformer World Models for Data-Efficient RL

Reference 4

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source=pdf_text observed=2026-08-15T19:30:24.081678Z digest=sha256:1b3c2e2988da0a1aa3589a03ef0562e0dbf9db7582e63d4d6d2101c58d36043c

Observation 75f96b34-3e20-4636-a879-76642ce7c04b · outbound

This paper cites Learning task informed abstractions.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Learning task informed abstractions

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.087081Z digest=sha256:cd717037a41cb306e148d236a287318f1d7f19c798b8c20aac095434120a604a

Observation ef8f099c-203d-4a5e-9590-bde8ac5ba555 · outbound

This paper cites Flip: Flow-centric generative planning as general-purpose manipulation world model.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Flip: Flow-centric generative planning as general-purpose manipulation world model

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.091868Z digest=sha256:c0bb21c0972e22157f979b0ea991229503e707973b27e79e9fa3b836cce57cf9

Observation b8f9fb9e-2b09-437f-b15c-10c28376adec · outbound

This paper cites Recurrent world models facilitate policy evolution.Advances in neural information processing systems, 31, 2018.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Recurrent world models facilitate policy evolution.Advances in neural information processing systems, 31, 2018

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.096689Z digest=sha256:858870e8d9077fc028a5759849ff7315cddb352f42a01bcc4bed1bf5ba829eda

Observation 171bb5be-add2-4f07-9a49-c7928b3fe600 · outbound

This paper cites Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust

Reference 8

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source=pdf_text observed=2026-08-15T19:30:24.101858Z digest=sha256:1df8faa23068d0bdaee1e21d44e1817cafd150dba82a18c5f175b993044ca3fd

Observation 639be2fd-cd0f-41cb-94f1-c4b2ea23b0d4 · outbound

This paper cites 1x world model: Evaluating bits, not atoms.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control 1x world model: Evaluating bits, not atoms

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.106650Z digest=sha256:77a47c46236da87e333c4d3190300fdd04b1726845bc82f67b4922afa2f87bc1

Observation 80ec3b25-41e3-4537-9141-3582b3e46a4b · outbound

This paper cites Leveraging separated world model for exploration in visually distracted environments.Advances in Neural Information Processing Systems, 37:82350–82374, 2024.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Leveraging separated world model for exploration in visually distracted environments.Advances in Neural Information Processing Systems, 37:82350–82374, 2024

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.111451Z digest=sha256:fdbf9812d377116fda56a460383fbdf466f81ec90392e89eea54112cb305ecb2

Observation 3a93c65d-a386-4b47-9256-42cea6d16c6e · outbound

This paper cites Planning with learned dynamics: Probabilis- tic guarantees on safety and reachability via lipschitz constants.IEEE Robotics and Automation Letters, 6(3): 5129–5136, 2021.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Planning with learned dynamics: Probabilis- tic guarantees on safety and reachability via lipschitz constants.IEEE Robotics and Automation Letters, 6(3): 5129–5136, 2021

Reference 11

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Observation ddfa2ed5-f1ab-4c53-b10e-0741277e8f2f · outbound

This paper cites ROSO: Improving Robotic Policy Inference via Synthetic Observations.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control ROSO: Improving Robotic Policy Inference via Synthetic Observations

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 360908d7-6f89-4d34-aff1-79b9fdd3e49b · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control DINOv2: Learning Robust Visual Features without Supervision

Reference 13

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source=pdf_text observed=2026-08-15T19:30:24.125779Z digest=sha256:2a72744d544af031404fe1fa6dbfdb5be95f38b4e5d4b60c1cdcaf59ab8b440a

Observation 1cc6517a-1a21-4424-b42b-abc791e8bd17 · outbound

This paper cites Strengthening generative robot policies through predic- tive world modeling.arXiv preprint arXiv:2502.00622, 2025.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Strengthening generative robot policies through predic- tive world modeling.arXiv preprint arXiv:2502.00622, 2025

Reference 14

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Observation f5122529-093e-42c0-a63d-d5eb471b66c3 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 15

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source=pdf_text observed=2026-08-15T19:30:24.135554Z digest=sha256:5a394c2fca7cf2bb8b03593ab7275e7dafba1258ce02a8bd4cbf0433481bdbeb

Observation 974e2c10-ba87-4f40-a6c4-fea77d34b53e · outbound

This paper cites Less is more–the dispatcher/executor principle for multi-task reinforcement learning.arXiv preprint arXiv:2312.09120, 2023.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Less is more–the dispatcher/executor principle for multi-task reinforcement learning.arXiv preprint arXiv:2312.09120, 2023

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 09e43bc2-e23c-47df-802d-64cc58f6ab0b · outbound

This paper cites Semail: eliminating dis- tractors in visual imitation via separated models.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Semail: eliminating dis- tractors in visual imitation via separated models

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 48c32fcc-22dc-4790-a6da-1a44c7ecc953 · outbound

This paper cites Denoised MDPs: Learning World Models Better Than the World Itself.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Denoised MDPs: Learning World Models Better Than the World Itself

Reference 18

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source=pdf_text observed=2026-08-15T19:30:24.150230Z digest=sha256:d398811c8c4e9e6e3935ab5bc9acaee973939583a0ff7b93959a0a01f3a7c5f1

Observation fd06cda5-1ca8-4e78-86da-5a4b08e311e9 · outbound

This paper cites Ad3: Implicit action is the key for world models to distinguish the diverse visual distractors.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Ad3: Implicit action is the key for world models to distinguish the diverse visual distractors

Reference 19

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 09737a75-fc1c-40ec-ad6a-03ca4f7cf881 · outbound

This paper cites Image quality assessment: from error visibil- ity to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Image quality assessment: from error visibil- ity to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

Reference 20

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source=pdf_text observed=2026-08-15T19:30:24.160077Z digest=sha256:e4d16f99f8e3affc43fddfd08318d5122d1af1aaf6003f288f7eef7469f614ee

Observation 5e454f6a-206c-47b0-9d82-3f5920ced50a · outbound

This paper cites Daydreamer: World models for physical robot learning.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Daydreamer: World models for physical robot learning

Reference 21

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source=pdf_text observed=2026-08-15T19:30:24.164799Z digest=sha256:b026060f61d1b7ca278a3b79f489b43069907f1beec8d46168578f88ad461dd7

Observation 6c8347b5-3e3b-48c0-b552-14a67a35e014 · outbound

This paper cites From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment

Reference 22

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source=pdf_text observed=2026-08-15T19:30:24.169676Z digest=sha256:4c4e3a196f1a8e7b5a854f808d4aeb7b20eb61bf5b367526a8af87272884b61e

Observation 86ea749e-0124-4f29-9327-39d4840bf8db · outbound

This paper cites Transferring foundation models for generalizable robotic manipulation.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Transferring foundation models for generalizable robotic manipulation

Reference 23

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1b84a277-27fa-4c80-9c63-5c2c2990b398 · outbound

This paper cites Learning Invariant Representations for Reinforcement Learning without Reconstruction.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 24

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source=pdf_text observed=2026-08-15T19:30:24.179126Z digest=sha256:67e1d69e1306e056719b45be6ab1939046bf6896cd25e237f55649679091cf92

Observation ad688d57-79f5-4042-b97c-d8cde2f5cdb0 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control The unreasonable effectiveness of deep features as a perceptual metric

Reference 25

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5cf9c8bd-2cfc-4e8a-abcb-683c20746867 · outbound

This paper cites Learning 4d embodied world models.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Learning 4d embodied world models

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e67e92a6-1ed4-4aee-85e1-eb6999d1c29c · outbound

This paper cites DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Reference 27

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source=pdf_text observed=2026-08-15T19:30:24.193337Z digest=sha256:943b3a41c018388147fed80309922febda7ea0f71bebf2fa81ad247a7be1aed3

Observation 1ea23340-f4a1-4de5-a677-7e061fe289b7 · outbound

This paper cites Repo: Resilient model-based reinforce- ment learning by regularizing posterior predictability.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Repo: Resilient model-based reinforce- ment learning by regularizing posterior predictability

Reference 28

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.198337Z digest=sha256:b797cf11c891efca0375418c087e4986bb463de5a261b6cccfff14f7b91579d5

Observation a49e5417-5985-450d-9ea7-322ef395ac6a · outbound

This paper cites an unresolved cited work.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Unresolved cited work

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.202862Z digest=sha256:8ad2af0b228fd708a1ded5507e99e44ab1f8d7ae0efd1a37062f90ff7e20522e

Observation 649054dc-75a4-49f7-aa9b-6972307c504b · outbound

This paper cites an unresolved cited work.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Unresolved cited work

Reference 30

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.207608Z digest=sha256:474f6fc032ca77d01dd7dc1e1b599613e029db88104a8d03e7ffd680460d2398

Observation e798c976-23dc-4a61-9e58-f836bfe8a4a9 · outbound

This paper cites Look carefully at each numbered patch and determine if the corresponding object still present in img wm.

Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control Look carefully at each numbered patch and determine if the corresponding object still present in img wm

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T19:30:24.212485Z digest=sha256:02b0f0f79680d40a328cd08781cee738acee0917bca3fa60d37adf29861ba0f6

Pith citing papers

Observation 62c8bb92-94b3-4cff-bb87-422f41c7fa0b · inbound

TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation cites this paper.

TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control

Reference 3

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arxiv_id, observed 2026-07-04T10:29:44.877548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T08:50:47.113217Z digest=sha256:479495527e209ec630bdb85790838b182e5820924d13a0ff0d3b588509dbb41e