{"as_of":"2026-08-17T14:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e3cd38b7a26409b720d42fcc13224a2517979d379d91b8d47e7505d3dbf941d9","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:30:24.212485Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T08:50:47.113217Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T10:29:44.875570Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"cited_work":{"arxiv_id":"2506.16565","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.16565","snapshot_observed_at":"2026-07-04T10:29:44.875570Z","title":"Reimagination with test-time observation interven- tions: Distractor-robust world model predictions for visual model predictive control.arXiv preprint arXiv:2506.16565, 2025","venue":null,"work_id":"0dd818b7-ab48-4341-b936-bfb797d27003","year":2025},"citing_paper":{"arxiv_id":"2606.22998","last_updated":"2026-06-23T07:19:46Z","snapshot_observed_at":"2026-07-06T23:57:47.047975Z","submitted_at":"2026-06-22T08:14:35Z","title":"TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T08:50:47.113217Z"},"links":{"cited_paper":"/paper/2506.16565","citing_paper":"/paper/2606.22998"},"observation_digest":"sha256:0e2075e8f5b4fd43057a2df4811a436b619b1a40283dbadc03fd820786d3f99d","observation_id":"62c8bb92-94b3-4cff-bb87-422f41c7fa0b","resolution":{"observed_at":"2026-07-04T10:29:44.877548Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.16565/citation-record","integrity":"/paper/2506.16565/integrity","json":"/paper/2506.16565/citation-record.json","paper":"/paper/2506.16565"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.03575","last_updated":"2025-07-09T19:35:31Z","snapshot_observed_at":"2026-08-03T00:21:10.886100Z","submitted_at":"2025-01-07T06:55:50Z","title":"Cosmos World Foundation Model Platform for Physical AI","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.03575","snapshot_observed_at":"2026-08-15T19:30:24.065632Z","title":"Cosmos world foundation model platform for physical ai.arXiv preprint arXiv:2501.03575, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.065632Z"},"links":{"cited_paper":"/paper/2501.03575","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:4cd441efc960165cffdeae3105521d996f15156824eba162ec1937a5fa5f05ed","observation_id":"d20fab1e-5adc-46d7-b6c4-8905d180cc3a","resolution":{"observed_at":"2026-08-15T19:30:24.065632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.03403","last_updated":"2024-09-09T03:11:19Z","snapshot_observed_at":"2026-08-16T13:21:07.574304Z","submitted_at":"2024-09-05T10:39:15Z","title":"RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.03403","snapshot_observed_at":"2026-08-15T19:30:24.071321Z","title":"Rovi-aug: Robot and viewpoint augmentation for cross-embodiment robot learning.arXiv preprint arXiv:2409.03403, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.071321Z"},"links":{"cited_paper":"/paper/2409.03403","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:32e61230815a0e7171d7e261d9db0f1f174d50bdfc2ac6389e07ae10f64e4132","observation_id":"197503eb-b3b5-437b-be95-2d04afb2fd99","resolution":{"observed_at":"2026-08-15T19:30:24.071321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.076656Z","title":"Dif- fusion policy: Visuomotor policy learning via action diffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.076656Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:45fec1d0c23cba3304cbce7437db4c782afa6f0c00b3e368df3023498336f2ed","observation_id":"cdff10da-e50f-4438-a819-9d6512ef8618","resolution":{"observed_at":"2026-08-15T19:30:24.076656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01591","last_updated":"2025-07-16T18:06:24Z","snapshot_observed_at":"2026-08-16T00:04:03.391790Z","submitted_at":"2025-02-03T18:25:17Z","title":"Improving Transformer World Models for Data-Efficient RL","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01591","snapshot_observed_at":"2026-08-15T19:30:24.081678Z","title":"Im- proving transformer world models for data-efficient rl","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.081678Z"},"links":{"cited_paper":"/paper/2502.01591","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:a1f52cb93e0fa10a86a32f9f608f57e21a52fca67db176121aa74bb12d19ea97","observation_id":"a1c84413-1946-4026-bb19-7fb10991f367","resolution":{"observed_at":"2026-08-15T19:30:24.081678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.873974Z","title":"Learning task informed abstractions","venue":null,"work_id":"e59eb210-13d0-4486-9f6b-8af81ebcf935","year":2021},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.087081Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:0e6b1ac3755b17e799731fb8ecf9ffb234e947ec1b7c521405318d017a223dc2","observation_id":"75f96b34-3e20-4636-a879-76642ce7c04b","resolution":{"observed_at":"2026-08-15T19:30:24.880562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.857748Z","title":"Flip: Flow-centric generative planning as general-purpose manipulation world model","venue":null,"work_id":"0e5d8eb1-5010-4ed1-b7b1-e29f5a2005e3","year":null},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.091868Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:e619b332ada07768882d22e7c2707b56a47ae4ee277194f1056a2f8ea5b19b8f","observation_id":"ef8f099c-203d-4a5e-9590-bde8ac5ba555","resolution":{"observed_at":"2026-08-15T19:30:24.862794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.841782Z","title":"Recurrent world models facilitate policy evolution.Advances in neural information processing systems, 31, 2018","venue":null,"work_id":"4dcb6a7f-80fe-4093-8c2e-d418765a0c59","year":2018},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.096689Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:7c0d28429ecb1a6dc30a2fa525906746dd12cfeb726c137277e0e5545701fad9","observation_id":"b8f9fb9e-2b09-437f-b15c-10c28376adec","resolution":{"observed_at":"2026-08-15T19:30:24.846738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.01971","last_updated":"2024-10-02T19:29:24Z","snapshot_observed_at":"2026-08-16T13:13:11.991491Z","submitted_at":"2024-10-02T19:29:24Z","title":"Run-time Observation Interventions Make Vision-Language-Action Models More Visually Robust","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.01971","snapshot_observed_at":"2026-08-15T19:30:24.101858Z","title":"Run-time observation interventions make vision- language-action models more visually robust.arXiv preprint arXiv:2410.01971, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.101858Z"},"links":{"cited_paper":"/paper/2410.01971","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:5c7a9060ca1ded90416815bd417b14f619be1552abe50697e10544b6ef420277","observation_id":"171bb5be-add2-4f07-9a49-c7928b3fe600","resolution":{"observed_at":"2026-08-15T19:30:24.101858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.826684Z","title":"1x world model: Evaluating bits, not atoms","venue":null,"work_id":"74d17149-583f-40ad-87df-ecc69dd60fb4","year":2025},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.106650Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:975bbb771fece59443a13722a14b597e6825b49912264c6fc921f819aa1e6089","observation_id":"639be2fd-cd0f-41cb-94f1-c4b2ea23b0d4","resolution":{"observed_at":"2026-08-15T19:30:24.831382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.811763Z","title":"Leveraging separated world model for exploration in visually distracted environments.Advances in Neural Information Processing Systems, 37:82350–82374, 2024","venue":null,"work_id":"aab5453a-de89-4a80-93ce-2a4d464ed258","year":2024},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.111451Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:272e1762186a45f01726a594cd91f3fd3fe84fd4f72b1879a6db0ff8e92dbedb","observation_id":"80ec3b25-41e3-4537-9141-3582b3e46a4b","resolution":{"observed_at":"2026-08-15T19:30:24.816596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.115989Z","title":"Planning with learned dynamics: Probabilis- tic guarantees on safety and reachability via lipschitz constants.IEEE Robotics and Automation Letters, 6(3): 5129–5136, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.115989Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:278aec8fb24bde51f1ecdc5ae2c6d9a0ef15c6085831c1a9cff65976f3a2c183","observation_id":"3a93c65d-a386-4b47-9256-42cea6d16c6e","resolution":{"observed_at":"2026-08-15T19:30:24.115989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.16680","last_updated":"2023-11-29T05:16:40Z","snapshot_observed_at":"2026-08-16T14:39:41.834441Z","submitted_at":"2023-11-28T10:52:35Z","title":"ROSO: Improving Robotic Policy Inference via Synthetic Observations","version":2},"cited_work":{"arxiv_id":"2311.16680","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.16680","snapshot_observed_at":"2026-08-15T19:30:24.544673Z","title":"ROSO: Improving Robotic Policy Inference via Synthetic Observations","venue":"cs.RO","work_id":"ef23cfb1-81fb-42b5-a87f-81093098ca90","year":2023},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.120898Z"},"links":{"cited_paper":"/paper/2311.16680","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:ac27b1a8e250722e660ffb1140ee25f1f01b6df7c99adfe85079d73de6a1f894","observation_id":"ddfa2ed5-f1ab-4c53-b10e-0741277e8f2f","resolution":{"observed_at":"2026-08-15T19:30:24.550117Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-15T19:30:24.125779Z","title":"Dinov2: Learning robust visual features without supervision.arXiv preprint arXiv:2304.07193, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.125779Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:18d88d9792e4cb9b2d18bb5528fa6787cdd42c04eb3f2c080aa46bc356b3bd4a","observation_id":"360908d7-6f89-4d34-aff1-79b9fdd3e49b","resolution":{"observed_at":"2026-08-15T19:30:24.125779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.130570Z","title":"Strengthening generative robot policies through predic- tive world modeling.arXiv preprint arXiv:2502.00622, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.130570Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:703cf0d7b52585d562029bfe97e6ca95ccc846eb755bc558497da053ee8e627c","observation_id":"1cc6517a-1a21-4424-b42b-abc791e8bd17","resolution":{"observed_at":"2026-08-15T19:30:24.130570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14159","last_updated":"2024-01-25T13:12:09Z","snapshot_observed_at":"2026-07-06T17:20:25.138890Z","submitted_at":"2024-01-25T13:12:09Z","title":"Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14159","snapshot_observed_at":"2026-08-15T19:30:24.135554Z","title":"Grounded sam: Assembling open- world models for diverse visual tasks.arXiv preprint arXiv:2401.14159, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.135554Z"},"links":{"cited_paper":"/paper/2401.14159","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:20534273e7f80986f67c17eb68a12f5ff42cca6d5ebcb2a591c1a177ed91ce25","observation_id":"f5122529-093e-42c0-a63d-d5eb471b66c3","resolution":{"observed_at":"2026-08-15T19:30:24.135554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2312.09120","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.423296Z","title":"Less is more–the dispatcher/executor principle for multi-task reinforcement learning.arXiv preprint arXiv:2312.09120, 2023","venue":null,"work_id":"dfe24383-1c55-49cc-ac29-4f7841d74c5a","year":2023},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.140793Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:be06ea2ed213627f8b5c6c7f8bba157f69e7a9848599361f17786168b4889352","observation_id":"974e2c10-ba87-4f40-a6c4-fea77d34b53e","resolution":{"observed_at":"2026-08-15T19:30:24.435201Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.785252Z","title":"Semail: eliminating dis- tractors in visual imitation via separated models","venue":null,"work_id":"dfa7b178-cebb-4c0d-aede-1c70ddd44e55","year":2023},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.145649Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:29e29c67fe905099b40e254fea17fde8a8d67894b0aa218241e6ca7a2647728d","observation_id":"09e43bc2-e23c-47df-802d-64cc58f6ab0b","resolution":{"observed_at":"2026-08-15T19:30:24.790366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.15477","last_updated":"2023-04-06T23:56:38Z","snapshot_observed_at":"2026-08-16T16:49:08.874685Z","submitted_at":"2022-06-30T17:59:49Z","title":"Denoised MDPs: Learning World Models Better Than the World Itself","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.15477","snapshot_observed_at":"2026-08-15T19:30:24.150230Z","title":"Denoised mdps: Learning world models better than the world itself.arXiv preprint arXiv:2206.15477, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.150230Z"},"links":{"cited_paper":"/paper/2206.15477","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:787a255d351a7eca76eba1bddd05a7829b08a2a908fdbff0e9410d7af1dd5fee","observation_id":"48c32fcc-22dc-4790-a6da-1a44c7ecc953","resolution":{"observed_at":"2026-08-15T19:30:24.150230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.769285Z","title":"Ad3: Implicit action is the key for world models to distinguish the diverse visual distractors","venue":null,"work_id":"4c393f67-aa35-44e5-b5d9-dc459d9f429c","year":2024},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.155301Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:db7620d5f5481ae21dd5036d5c5e64c444158853b5c1b70b2870082c4a17539e","observation_id":"fd06cda5-1ca8-4e78-86da-5a4b08e311e9","resolution":{"observed_at":"2026-08-15T19:30:24.774569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.753749Z","title":"Image quality assessment: from error visibil- ity to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004","venue":null,"work_id":"3a8b2ecc-c1f8-4a62-a36f-2a09b84065d4","year":2004},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.160077Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:b2fb21779f3a0ecbfebe46f4a8e7b09699c3e232c0ff470868aedc44c51b5474","observation_id":"09737a75-fc1c-40ec-ad6a-03ca4f7cf881","resolution":{"observed_at":"2026-08-15T19:30:24.758950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.164799Z","title":"Daydreamer: World models for physical robot learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.164799Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:01806ab4296dcf2ddd1f8ca9e0842788d242f5099133c1f84f116850a0fc948b","observation_id":"5e454f6a-206c-47b0-9d82-3f5920ced50a","resolution":{"observed_at":"2026-08-15T19:30:24.164799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01828","last_updated":"2025-05-02T17:53:34Z","snapshot_observed_at":"2026-08-15T09:08:03.573863Z","submitted_at":"2025-02-03T21:11:02Z","title":"From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01828","snapshot_observed_at":"2026-08-15T19:30:24.169676Z","title":"From foresight to forethought: Vlm-in-the-loop policy steering via latent alignment.arXiv preprint arXiv:2502.01828, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.169676Z"},"links":{"cited_paper":"/paper/2502.01828","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:7ef4eef0ecad0a1ff67cc4fe20d8c3e738bedbcdb30b796358aefd103bc2da02","observation_id":"6c8347b5-3e3b-48c0-b552-14a67a35e014","resolution":{"observed_at":"2026-08-15T19:30:24.169676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.727905Z","title":"Transferring foundation models for generalizable robotic manipulation","venue":null,"work_id":"6d5c17ae-b722-41a5-b82f-14887ca7c999","year":1999},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.174830Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:3e9dea56dd18b2f3a7a86f905fabcb7470825a940825f1180da227ea107428c4","observation_id":"86ea749e-0124-4f29-9327-39d4840bf8db","resolution":{"observed_at":"2026-08-15T19:30:24.733214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10742","last_updated":"2021-04-07T01:57:14Z","snapshot_observed_at":"2026-08-12T08:19:24.097653Z","submitted_at":"2020-06-18T17:59:35Z","title":"Learning Invariant Representations for Reinforcement Learning without Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10742","snapshot_observed_at":"2026-08-15T19:30:24.179126Z","title":"Learning invariant representa- tions for reinforcement learning without reconstruction","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.179126Z"},"links":{"cited_paper":"/paper/2006.10742","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:1f1779218bb8c1d64da1fc7ff8f6fcf69709f183ba461fa07396a492e6f08fcb","observation_id":"1b84a277-27fa-4c80-9c63-5c2c2990b398","resolution":{"observed_at":"2026-08-15T19:30:24.179126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.711596Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":"cf5fa12c-5f0e-4523-8bf9-197ad94d8ce8","year":2018},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.183776Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:1ee0300c3da0011c923b98851660565257906e2edcc9a7738e8b8baeeaba11f7","observation_id":"ad688d57-79f5-4042-b97c-d8cde2f5cdb0","resolution":{"observed_at":"2026-08-15T19:30:24.716886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.693814Z","title":"Learning 4d embodied world models","venue":null,"work_id":"98ba9f38-dab5-4ad4-aef4-79963a049c89","year":null},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.188590Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:e7faefb0760a6a8c94a15a4c055e7c8e090699f4ffa424875e9e6ce9ea4cd7fc","observation_id":"5cf9c8bd-2cfc-4e8a-abcb-683c20746867","resolution":{"observed_at":"2026-08-15T19:30:24.698912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.04983","last_updated":"2025-02-01T02:40:49Z","snapshot_observed_at":"2026-08-13T16:10:49.002039Z","submitted_at":"2024-11-07T18:54:37Z","title":"DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04983","snapshot_observed_at":"2026-08-15T19:30:24.193337Z","title":"Dino-wm: World models on pre-trained vi- sual features enable zero-shot planning.arXiv preprint arXiv:2411.04983, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.193337Z"},"links":{"cited_paper":"/paper/2411.04983","citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:9fb2169ecf66dec74ffd0a3a8d7fa54ff52abc9158ff2b30058448c99dc40469","observation_id":"e67e92a6-1ed4-4aee-85e1-eb6999d1c29c","resolution":{"observed_at":"2026-08-15T19:30:24.193337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.677765Z","title":"Repo: Resilient model-based reinforce- ment learning by regularizing posterior predictability","venue":null,"work_id":"0cc7b7b0-209c-487a-bd90-7faf9b8166cd","year":2023},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.198337Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:03076452fa1220b4c94a90b989a12e247e73b53e83b5416d15d3e6a3869a317f","observation_id":"1ea23340-f4a1-4de5-a677-7e061fe289b7","resolution":{"observed_at":"2026-08-15T19:30:24.682788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.661527Z","title":null,"venue":null,"work_id":"b7b2de7c-ef27-44ef-b211-88b3e3187b5a","year":null},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.202862Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:6041d26243dacfeb48c92a8f082d79ea8f32a8ceca218a28c2cc8c67e17bf5a2","observation_id":"a49e5417-5985-450d-9ea7-322ef395ac6a","resolution":{"observed_at":"2026-08-15T19:30:24.666362Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.645573Z","title":null,"venue":null,"work_id":"77fa4146-c970-423f-8aa6-663558797862","year":null},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.207608Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:5aec584ec19dbdeb2a0c7ec0eeb996e04908e520cdf5a09b0901f1450356fa64","observation_id":"649054dc-75a4-49f7-aa9b-6972307c504b","resolution":{"observed_at":"2026-08-15T19:30:24.650585Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T19:30:24.628716Z","title":"Look carefully at each numbered patch and determine if the corresponding object still present in img wm","venue":null,"work_id":"16eb556b-5aaa-4e26-8524-33403bcb5864","year":null},"citing_paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T19:30:24.212485Z"},"links":{"citing_paper":"/paper/2506.16565"},"observation_digest":"sha256:0a43fda032d728b8f1717f62ca3061b67f202916c46872144768adf69f542ac3","observation_id":"e798c976-23dc-4a61-9e58-f836bfe8a4a9","resolution":{"observed_at":"2026-08-15T19:30:24.634717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.16565","last_updated":"2025-06-19T19:41:29Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-16T05:34:16.021768Z","submitted_at":"2025-06-19T19:41:29Z","title":"Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":2,"verified_fuzzy":13},"total_outbound_references":31},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"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."}