{"as_of":"2026-08-08T11:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:914604d9badf45df2f1ab0886efb09928adc3e4e932d167876ed65f009553424","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T20:08:49.728766Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.09923/citation-record","integrity":"/paper/2502.09923/integrity","json":"/paper/2502.09923/citation-record.json","paper":"/paper/2502.09923"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1710.11041","last_updated":"2018-02-26T16:54:14Z","snapshot_observed_at":"2026-07-06T06:06:46.772265Z","submitted_at":"2017-10-30T16:17:34Z","title":"Unsupervised Neural Machine Translation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.11041","snapshot_observed_at":"2026-08-07T20:08:49.420837Z","title":"Unsupervised neural machine translation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.420837Z"},"links":{"cited_paper":"/paper/1710.11041","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:8a8fdf551db1fba861fbbbd9598a970beb991814059cc099507bfc69d1a5a35a","observation_id":"942ca25d-8e92-49bb-9204-fc1b43badce0","resolution":{"observed_at":"2026-08-07T20:08:49.420837Z","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":"2026-08-07T20:08:50.572299Z","title":"Generalization in re- inforcement learning by soft data augmentation","venue":null,"work_id":"3da93941-7bb1-4854-aefa-1d75e32dcf24","year":2021},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.455151Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:704c9878b45ef0fdb6ea15ff5e71b371619de959198b63c0c3caf811f75b6830","observation_id":"1a6730e5-1d50-4b85-8c34-ecc9c8eb9a66","resolution":{"observed_at":"2026-08-07T20:08:50.600025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.03511","last_updated":"2020-09-22T08:58:24Z","snapshot_observed_at":"2026-08-04T09:24:06.397404Z","submitted_at":"2020-06-05T15:28:01Z","title":"Unsupervised Translation of Programming Languages","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.03511","snapshot_observed_at":"2026-08-07T20:08:49.469960Z","title":"Unsupervised translation of pro- gramming languages","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.469960Z"},"links":{"cited_paper":"/paper/2006.03511","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:e0e7be22b9cf910a85d2600b03bff10b0969fa4e09e2c36124a36f6ba7250f58","observation_id":"0c7b37a6-3f4b-4208-a5ce-f8379f87c4db","resolution":{"observed_at":"2026-08-07T20:08:49.469960Z","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":"2026-08-07T20:08:50.158932Z","title":null,"venue":null,"work_id":"25fbfe95-4c64-4102-b3ed-32aa2e23a166","year":2024},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.718727Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:111b0263f2d54e71a0cdb84ce60c6f5a46206d6ddcf13b1a8ad48cb50b9071bc","observation_id":"cc6f5490-b39c-42d6-9ffa-9c035bc426d0","resolution":{"observed_at":"2026-08-07T20:08:50.163345Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.12601","last_updated":"2022-11-18T05:57:09Z","snapshot_observed_at":"2026-08-02T11:48:59.131827Z","submitted_at":"2022-03-23T17:55:09Z","title":"R3M: A Universal Visual Representation for Robot Manipulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.12601","snapshot_observed_at":"2026-08-07T20:08:49.546331Z","title":"R3m: A universal visual representation for robot manipulation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.546331Z"},"links":{"cited_paper":"/paper/2203.12601","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:9987bc55dd23a46d831bb0c44e279a304e4c67c2e57779aa508f3dedb26d8093","observation_id":"cf1b2602-fd6c-4e17-bb60-bded98aa959e","resolution":{"observed_at":"2026-08-07T20:08:49.546331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13817","last_updated":"2022-12-18T09:50:58Z","snapshot_observed_at":"2026-07-06T13:14:33.576055Z","submitted_at":"2022-05-27T08:07:39Z","title":"Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13817","snapshot_observed_at":"2026-08-07T20:08:49.571611Z","title":"Isolating and leveraging controllable and noncon- trollable visual dynamics in world models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.571611Z"},"links":{"cited_paper":"/paper/2205.13817","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:d5cc6eda55753018034433f6e3133c4fcd19929a1d443fd5bbd0daf21e2f7887","observation_id":"18b46bf0-4617-47e0-8732-471c0a5cc3a7","resolution":{"observed_at":"2026-08-07T20:08:49.571611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.12507","last_updated":"2023-06-14T14:04:50Z","snapshot_observed_at":"2026-07-06T14:45:48.434072Z","submitted_at":"2023-01-29T18:21:05Z","title":"Distilling Internet-Scale Vision-Language Models into Embodied Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.12507","snapshot_observed_at":"2026-08-07T20:08:49.576070Z","title":"Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, and Ishita Dasgupta","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.576070Z"},"links":{"cited_paper":"/paper/2301.12507","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:bb66000c67caa0e462761a70795ba327a1ef2f3c2dc06df5a0b27e442c4588de","observation_id":"02680cba-74f2-4dc8-bf39-d10659a2786e","resolution":{"observed_at":"2026-08-07T20:08:49.576070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00690","last_updated":"2018-01-02T15:48:14Z","snapshot_observed_at":"2026-08-01T20:24:08.300098Z","submitted_at":"2018-01-02T15:48:14Z","title":"DeepMind Control Suite","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00690","snapshot_observed_at":"2026-08-07T20:08:49.580007Z","title":"Deepmind control suite","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.580007Z"},"links":{"cited_paper":"/paper/1801.00690","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:a52372d0c607d771bc716a621f1002bb1a4bec6104fbb959e6323b3bd654f144","observation_id":"d2b0a6ae-571d-47cf-9568-3c9d58e618cf","resolution":{"observed_at":"2026-08-07T20:08:49.580007Z","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":"2026-08-07T20:08:50.352371Z","title":"A survey on unsu- pervised transfer clustering","venue":null,"work_id":"f752c019-b33c-411b-b325-16e63e519811","year":2021},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.589266Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:73794567002bcd98944d7dda40161827a3be967c12f6db663b1b4b60d24c8ba6","observation_id":"2e253a36-5036-4ab5-ac3b-6cdba3d585ab","resolution":{"observed_at":"2026-08-07T20:08:50.484607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.09777","last_updated":"2023-11-27T05:09:29Z","snapshot_observed_at":"2026-07-06T16:19:57.322692Z","submitted_at":"2023-09-18T13:58:42Z","title":"DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.09777","snapshot_observed_at":"2026-08-07T20:08:49.593951Z","title":"Drivedreamer: Towards real-world-driven world models for autonomous driving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.593951Z"},"links":{"cited_paper":"/paper/2309.09777","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:964d85f08592db547e8159c5b0a81c2cbdc71c524c7359c6099a3a53930dcf9f","observation_id":"eca9d332-f1f9-4322-9707-0375bd9bd9be","resolution":{"observed_at":"2026-08-07T20:08:49.593951Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14073","last_updated":"2024-11-18T14:06:10Z","snapshot_observed_at":"2026-07-06T18:18:16.530605Z","submitted_at":"2024-05-23T00:35:23Z","title":"PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2405.14073","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.14073","snapshot_observed_at":"2026-08-07T20:08:49.869084Z","title":"PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning","venue":"cs.LG","work_id":"a5e3b4a5-5f26-4a02-ba50-fe33b02cf5d1","year":2024},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.666436Z"},"links":{"cited_paper":"/paper/2405.14073","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:234c10ce13c2a65f51e40cccfaaaec83a7cc7b9c49b7e7336d1f31b8aa8f31d5","observation_id":"6942f956-bf95-4c60-bcac-c43633f7fbb3","resolution":{"observed_at":"2026-08-07T20:08:49.876088Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.06635","last_updated":"2022-12-20T13:59:51Z","snapshot_observed_at":"2026-08-03T16:19:17.382695Z","submitted_at":"2022-07-14T03:08:33Z","title":"EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.06635","snapshot_observed_at":"2026-08-07T20:08:49.696725Z","title":"Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.696725Z"},"links":{"cited_paper":"/paper/2207.06635","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:26c87befb9c4e0c16ede1a4498ce4cfda17dcafcd1a6dbee34d7193781f09dc9","observation_id":"4c6cfeee-9bca-424e-9b50-6dcae8e7b978","resolution":{"observed_at":"2026-08-07T20:08:49.696725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.12293","last_updated":"2025-01-18T02:57:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-09-25T15:32:31Z","title":"robosuite: A Modular Simulation Framework and Benchmark for Robot Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.12293","snapshot_observed_at":"2026-08-07T20:08:49.705306Z","title":"robosuite: A modular simulation framework and benchmark for robot learning","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.705306Z"},"links":{"cited_paper":"/paper/2009.12293","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:7e73953f6320d64879c00dd8a364edf1f12b22cae7061d63c44f5df96f155b0e","observation_id":"6f71f3ad-28f6-4521-85c3-42f9ed0c8330","resolution":{"observed_at":"2026-08-07T20:08:49.705306Z","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":"2026-08-07T20:08:50.326323Z","title":null,"venue":null,"work_id":"60ab8b9a-bbd6-40a9-8901-31dad21d0491","year":2021},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.709734Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:b5f5c6f422242de808313370869cd1c71c73d2fea3b20e930958c9a79fe848be","observation_id":"b99c81ee-2d18-4396-b97a-af81c45a2f7e","resolution":{"observed_at":"2026-08-07T20:08:50.330374Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T20:08:50.171560Z","title":"A.4 Self-Consistent Model-based Adaptation Below we provide a detailed derivation of SCMA’s adaptation loss","venue":null,"work_id":"2d70153c-badc-455b-8c6f-00922c41dae3","year":2017},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.714537Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:8b7f6b2b785603401c8dacd7c08f8f893305233e4df67e6ae77e6b5c917f49af","observation_id":"aaf91980-6856-4238-b2d1-38ee6bdc1a67","resolution":{"observed_at":"2026-08-07T20:08:50.255344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T20:08:50.145568Z","title":"Following Yuan et al","venue":null,"work_id":"70bb44c3-e710-44da-8d7b-8e4b4d617d79","year":2024},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.723892Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:0c78258a2bba0163b25b1baeb4489b2734b7feb0a44a91caa911d4b0cd72e80b","observation_id":"fd5c6030-fcec-4de4-8257-f1d658906b92","resolution":{"observed_at":"2026-08-07T20:08:50.149605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T20:08:50.077759Z","title":null,"venue":null,"work_id":"a6e2d744-f7bc-4d3f-8adf-406105bdcd0f","year":2020},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.728766Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:d65a14603ffc812304d85fa99cb74e5eabc896d5b770a7ab409793c5315ccd81","observation_id":"11699a66-121b-488a-9e99-729c14425546","resolution":{"observed_at":"2026-08-07T20:08:50.136830Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08576","last_updated":"2023-10-12T17:59:23Z","snapshot_observed_at":"2026-08-05T14:38:36.869054Z","submitted_at":"2023-10-12T17:59:23Z","title":"Learning to Act from Actionless Videos through Dense Correspondences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08576","snapshot_observed_at":"2026-08-07T20:08:49.465586Z","title":"Learning to act from actionless videos through dense correspondences","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.465586Z"},"links":{"cited_paper":"/paper/2310.08576","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:1d9a837d53c3d90c4669bfde2792428ee714172fc9f90d2bf8f9c6011d2c50cf","observation_id":"24052ae1-99a5-4399-beeb-237e10d3fe62","resolution":{"observed_at":"2026-08-07T20:08:49.465586Z","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":"2026-08-07T20:08:50.339489Z","title":null,"venue":null,"work_id":"e05f630e-8412-4821-8b6f-afd961b7dc61","year":2017},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.701251Z"},"links":{"citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:b76b512588926b976cd3bc62d56e402f827bfe1a7ddf0947aef65843cdf7a77e","observation_id":"2e2611b2-a444-424c-9d4e-e1d5c540e249","resolution":{"observed_at":"2026-08-07T20:08:50.343946Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07775","last_updated":"2021-11-15T14:16:28Z","snapshot_observed_at":"2026-07-06T12:08:35.761426Z","submitted_at":"2021-11-15T14:16:28Z","title":"Learning Representations for Pixel-based Control: What Matters and Why?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07775","snapshot_observed_at":"2026-08-07T20:08:49.584832Z","title":"Learning representations for pixel- based control: What matters and why? arXiv preprint arXiv:2111.07775,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.584832Z"},"links":{"cited_paper":"/paper/2111.07775","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:15422b567d3ea2cf8f659e554e09f7a663c0ebb6c77d65040d7a7c973b40eb0a","observation_id":"14f11395-a60e-4ff9-8d9d-edc336d66f40","resolution":{"observed_at":"2026-08-07T20:08:49.584832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04104","last_updated":"2024-04-17T17:41:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-01-10T18:12:16Z","title":"Mastering Diverse Domains through World Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04104","snapshot_observed_at":"2026-08-07T20:08:49.450851Z","title":"Mastering diverse domains through world mod- els","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.450851Z"},"links":{"cited_paper":"/paper/2301.04104","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:7bdce90f11b0ba5d348aaf3fc008b11acc9845ed6ecfcc54db6cc76549c7347b","observation_id":"32a6c342-f233-43e1-976c-54683b374a78","resolution":{"observed_at":"2026-08-07T20:08:49.450851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.17046","last_updated":"2023-09-29T08:13:12Z","snapshot_observed_at":"2026-07-06T16:25:21.571679Z","submitted_at":"2023-09-29T08:13:12Z","title":"CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17046","snapshot_observed_at":"2026-08-07T20:08:49.474553Z","title":"Crossloco: Human motion driven control of legged robots via guided unsu- pervised reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.474553Z"},"links":{"cited_paper":"/paper/2309.17046","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:9e3c6704030f8462301289289603bbe176c4f1b4e1f2dc0d8dea1141c05d3531","observation_id":"198e2939-86d7-4fe9-9600-41551fdb98ea","resolution":{"observed_at":"2026-08-07T20:08:49.474553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-07-06T09:36:36.819365Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-08-07T20:08:49.459978Z","title":"Abbeel, Alexei A","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.459978Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:9582ad71e921bc7d8905f98767ee5b60cd82a8261570259958d1994d55f6b775","observation_id":"28f3bb1a-be3d-491b-9cf3-24fa803d024e","resolution":{"observed_at":"2026-08-07T20:08:49.459978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.00111","last_updated":"2023-11-20T05:38:13Z","snapshot_observed_at":"2026-08-05T19:35:53.885395Z","submitted_at":"2023-01-31T21:28:13Z","title":"Learning Universal Policies via Text-Guided Video Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.00111","snapshot_observed_at":"2026-08-07T20:08:49.440473Z","title":"Tenenbaum, Dale Schuurmans, and P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.440473Z"},"links":{"cited_paper":"/paper/2302.00111","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:47b9dabf26c73e7689df297816fd7f836758cbec7d32c7575f3cdb671a351290","observation_id":"cd0b4701-d5e3-44c8-90a3-b9791575d9bd","resolution":{"observed_at":"2026-08-07T20:08:49.440473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01603","last_updated":"2020-03-17T17:10:58Z","snapshot_observed_at":"2026-08-03T15:20:23.515607Z","submitted_at":"2019-12-03T18:57:16Z","title":"Dream to Control: Learning Behaviors by Latent Imagination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01603","snapshot_observed_at":"2026-08-07T20:08:49.446052Z","title":"Dream to control: Learning behaviors by la- tent imagination","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.446052Z"},"links":{"cited_paper":"/paper/1912.01603","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:62a47c97f37a8d61af3911212c2d2a0f4ac81d5d0bca4dda36a605e3a033c212","observation_id":"d4c3e22b-19a8-4862-a36a-52ccb222ddf6","resolution":{"observed_at":"2026-08-07T20:08:49.446052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05614","last_updated":"2025-03-24T07:52:21Z","snapshot_observed_at":"2026-07-06T14:50:40.930413Z","submitted_at":"2023-02-11T06:32:28Z","title":"Cross-domain Random Pre-training with Prototypes for Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.05614","snapshot_observed_at":"2026-08-07T20:08:49.509364Z","title":"Cross-domain random pre-training with prototypes for reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T20:08:49.509364Z"},"links":{"cited_paper":"/paper/2302.05614","citing_paper":"/paper/2502.09923"},"observation_digest":"sha256:f34e974a08675222d21e4046d8765088d1fd3498f39adfd5389d22962628cb41","observation_id":"4aa5ee30-ee26-46b8-80a0-d8e9134faf2c","resolution":{"observed_at":"2026-08-07T20:08:49.509364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.09923","last_updated":"2025-02-14T05:23:56Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T05:52:10.090372Z","submitted_at":"2025-02-14T05:23:56Z","title":"Self-Consistent Model-based Adaptation for Visual Reinforcement Learning"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":1,"verified_fuzzy":4},"total_outbound_references":26},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2502.09923."}