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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
  • 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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:30:24.065632Z digest=sha256:4cd441efc960165cffdeae3105521d996f15156824eba162ec1937a5fa5f05ed

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=sha256:32e61230815a0e7171d7e261d9db0f1f174d50bdfc2ac6389e07ae10f64e4132

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=sha256:45fec1d0c23cba3304cbce7437db4c782afa6f0c00b3e368df3023498336f2ed

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:a1f52cb93e0fa10a86a32f9f608f57e21a52fca67db176121aa74bb12d19ea97

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=sha256:0e6b1ac3755b17e799731fb8ecf9ffb234e947ec1b7c521405318d017a223dc2

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:e619b332ada07768882d22e7c2707b56a47ae4ee277194f1056a2f8ea5b19b8f

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=sha256:7c0d28429ecb1a6dc30a2fa525906746dd12cfeb726c137277e0e5545701fad9

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=sha256:5c7a9060ca1ded90416815bd417b14f619be1552abe50697e10544b6ef420277

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=sha256:975bbb771fece59443a13722a14b597e6825b49912264c6fc921f819aa1e6089

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=sha256:272e1762186a45f01726a594cd91f3fd3fe84fd4f72b1879a6db0ff8e92dbedb

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=sha256:278aec8fb24bde51f1ecdc5ae2c6d9a0ef15c6085831c1a9cff65976f3a2c183

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:ac27b1a8e250722e660ffb1140ee25f1f01b6df7c99adfe85079d73de6a1f894

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=sha256:9979d3322bec68784615699738e34e6944c94d33f92778b135c7aed661d892da

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=sha256:703cf0d7b52585d562029bfe97e6ca95ccc846eb755bc558497da053ee8e627c

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=sha256:20534273e7f80986f67c17eb68a12f5ff42cca6d5ebcb2a591c1a177ed91ce25

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:be06ea2ed213627f8b5c6c7f8bba157f69e7a9848599361f17786168b4889352

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=sha256:29e29c67fe905099b40e254fea17fde8a8d67894b0aa218241e6ca7a2647728d

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=sha256:787a255d351a7eca76eba1bddd05a7829b08a2a908fdbff0e9410d7af1dd5fee

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:db7620d5f5481ae21dd5036d5c5e64c444158853b5c1b70b2870082c4a17539e

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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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:b2fb21779f3a0ecbfebe46f4a8e7b09699c3e232c0ff470868aedc44c51b5474

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=sha256:01806ab4296dcf2ddd1f8ca9e0842788d242f5099133c1f84f116850a0fc948b

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=sha256:7ef4eef0ecad0a1ff67cc4fe20d8c3e738bedbcdb30b796358aefd103bc2da02

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=sha256:3e9dea56dd18b2f3a7a86f905fabcb7470825a940825f1180da227ea107428c4

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=sha256:1f1779218bb8c1d64da1fc7ff8f6fcf69709f183ba461fa07396a492e6f08fcb

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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raw_fallback, observed 2026-08-15T19:30:24.716886Z

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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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raw_fallback, observed 2026-08-15T19:30:24.698912Z

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=2026-08-15T19:30:24.188590Z digest=sha256:e7faefb0760a6a8c94a15a4c055e7c8e090699f4ffa424875e9e6ce9ea4cd7fc

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=sha256:9fb2169ecf66dec74ffd0a3a8d7fa54ff52abc9158ff2b30058448c99dc40469

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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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=sha256:03076452fa1220b4c94a90b989a12e247e73b53e83b5416d15d3e6a3869a317f

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=sha256:6041d26243dacfeb48c92a8f082d79ea8f32a8ceca218a28c2cc8c67e17bf5a2

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=sha256:5aec584ec19dbdeb2a0c7ec0eeb996e04908e520cdf5a09b0901f1450356fa64

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=sha256:0a43fda032d728b8f1717f62ca3061b67f202916c46872144768adf69f542ac3

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=sha256:0e2075e8f5b4fd43057a2df4811a436b619b1a40283dbadc03fd820786d3f99d