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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 18 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
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:30:24.065632Z digest=sha256:9c7e57843b498e2a73a981320a99dd87ae2844217f0e0f9e58fd720d3d0471a3

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=sha256:0f05bd7327d6cd8cc76be1956bd31ef7b411e50ad19320369b1f2e900604f5d3

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=sha256:51130f16bde756e218a06062d44f29fd15cf9d766c401cadb556798b82ead06f

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=sha256:513ea51129fe6ccc262d91948313c3e051555b3b01634642e659cf2227cc4b74

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:e6a74ae82de2aac1291311fe00288b2ab3edce625cf26ccb9653f2b6bda86f96

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:b6525126caf453164bb20e895bf7ddc3175d3ee5e7b5d7809267b3d50ac9a6ff

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:d0545b57a75d2ff0ece996315d77ac29d62e6dd324ba06c5c85265963646fafa

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:e9ff2a55ded7da135a34c393929ccb4c9ad47b6c56c2fc50fba603ea875182a1

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:a896e26411e8969953424faeacf11253a5b7c7ecf72a354e5d37165517ba2b3c

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:b9d7f25ea75522723adc982f0c304769d461ffacfcae5406d8e8b27f8636b4d1

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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source=pdf_text observed=2026-08-15T19:30:24.115989Z digest=sha256:0381f9efef1b23df04191ba78c5b8afbddb4896cd3baa4a61f44b120d8aec14d

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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local_arxiv, observed 2026-08-15T19:30:24.550117Z

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=2026-08-15T19:30:24.120898Z digest=sha256:25b4176c86c07c93edf959012b9fd0fd2293d37b3f46415283bda2a541ef6988

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:ffa0d40c164db2dcb270d97cc6eb00d211a32df9398d9af7772eabea7004ad02

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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source=pdf_text observed=2026-08-15T19:30:24.130570Z digest=sha256:bf592c7821c06d448b28870117ba263a15d2bd0d22a7238ae096e6ffc4d3be13

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=sha256:800e4c88e2809b86f3665aaaf7ef25615a5fe4d0cf6672a2c2471dad40f3f892

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.

source=pdf_text observed=2026-08-15T19:30:24.140793Z digest=sha256:a0601b5b40a03ee5acaa2a711811c1aabc161e333d7e944e286428de675c8dde

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.

source=pdf_text observed=2026-08-15T19:30:24.145649Z digest=sha256:357dcee34f1177391b386fb8b1396a68cfd51d8326dd509c494a5b8c4b6e4b28

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=sha256:714dfd2cc72a9751232a681c083e4fc2d1acd9229844216022140d380c1a4991

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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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=2026-08-15T19:30:24.155301Z digest=sha256:7e0d66a7454ae4b719aacaae9ca31f2fc1db8bf081479e7c35a25ac160947270

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-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=2026-08-15T19:30:24.160077Z digest=sha256:a6622145be4b438c8de3152da177cb680d9cb58d00b28ca20321eee96427082c

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=sha256:94b86da06600762738bf5c2fb4abaaba784edfe701754ff6f062cd427143531c

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=sha256:5a4e73b5eac84640a5946294ad98ad43cc7217fc6dcf783b541bcfd99645189a

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.

source=pdf_text observed=2026-08-15T19:30:24.174830Z digest=sha256:c0dbac658309fe7b145d2c372207aabe6ffbfac28900d1d38a9ca95ac8f55ce2

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:c6e05e631d4b81a73e62d84853103ac4035bf4440934af328ff640b4e37aabdd

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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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=2026-08-15T19:30:24.183776Z digest=sha256:b9f4f7f4b0e1343ffc835241b1dd266eed02901ebdcb90f7875f4650dcd7197b

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=sha256:2243d6f87160e550142ac52ccc7ec02114ac56bd231535d1a732fa07cf73094b

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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raw_fallback, observed 2026-08-15T19:30:24.682788Z

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=2026-08-15T19:30:24.198337Z digest=sha256:74fae2c0174a3f305363634da76812ce33b7e588a37aa80227c2ad8023af166c

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:e23289280908afe031952178c39b0fb4338d442f2f4967c70133e25e5449556a

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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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=2026-08-15T19:30:24.207608Z digest=sha256:889e01918849215b369a9eac0445ee90dc708c6d2bf00d3ea1f02633569e8bed

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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raw_fallback, observed 2026-08-15T19:30:24.634717Z

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=sha256:166ef3aa58e50aeb9e0fd4a6de02eebc9e78412d047318d2a297c6009be2b3d1

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=sha256:774ebf90635011fe872557270a98a99251d711bc5c8eecc7e9d9ec69fda7bce0