{"as_of":"2026-08-09T18:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b633bddb1ab329ef9c7bba5c97d1d0b3661b0e3c2244853876326e1c2331a9cc","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":16,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T20:08:49.459978Z","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":"2026-07-04T12:59:53.026159Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2302.11550","last_updated":"2023-02-22T18:47:51Z","snapshot_observed_at":"2026-08-05T16:16:51.134330Z","submitted_at":"2023-02-22T18:47:51Z","title":"Scaling Robot Learning with Semantically Imagined Experience","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-17T18:59:10.352342Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2302.11550"},"observation_digest":"sha256:0661f8528617e7c9b924ba9daf2c5c6dd9683752cd29f57d718bb0334f9a7d4e","observation_id":"89bba9c8-d48e-41a7-95db-f60a6ee79ec5","resolution":{"observed_at":"2026-05-17T18:59:10.590174Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2407.04620","last_updated":"2025-08-31T18:32:59Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-05T16:23:20Z","title":"Learning to (Learn at Test Time): RNNs with Expressive Hidden States","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-15T05:20:12.134340Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2407.04620"},"observation_digest":"sha256:d74adc6a2b81884892b09889c31b25808020a4978530c2b03ef488d3ba341c41","observation_id":"499a9b7b-54d9-46aa-8874-7446a3e7ea03","resolution":{"observed_at":"2026-05-15T05:20:12.296330Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","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:ea0c8099dbaa4bccdda52b7331e8ea23633d4a86104b5dbb9f9cd3b683f0d4e8","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":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","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":"2026-08-07T10:47:32.867428Z","title":"Self-supervised policy adaptation during deployment.arXiv preprint arXiv:2007.04309,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05418","last_updated":"2025-06-05T00:36:54Z","snapshot_observed_at":"2026-08-09T00:14:33.104955Z","submitted_at":"2025-06-05T00:36:54Z","title":"Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:47:32.867428Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2506.05418"},"observation_digest":"sha256:c3c661c8b860ced778deb1076b492e48cd1cafc1badd48c2de3eb2b5bd516828","observation_id":"d5bd7f9e-98a1-4ef3-ab1c-1c6adcfe02c9","resolution":{"observed_at":"2026-08-07T10:47:32.867428Z","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":"2026-08-09T01:41:50.099348Z","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":"2026-08-07T10:46:33.337563Z","title":"A.; Pinto, L.; and Wang, X","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.05419","last_updated":"2025-06-05T00:39:03Z","snapshot_observed_at":"2026-08-09T08:43:55.942351Z","submitted_at":"2025-06-05T00:39:03Z","title":"Dream to Generalize: Zero-Shot Model-Based Reinforcement Learning for Unseen Visual Distractions","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T10:46:33.337563Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2506.05419"},"observation_digest":"sha256:2c4fb5f0c413ec4efbc2c8dc4bbc66f3d2847a4abc68fc0eda7813616d5ab253","observation_id":"afbb0b57-1947-452d-a646-5d279a3277af","resolution":{"observed_at":"2026-08-07T10:46:33.337563Z","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":"2026-08-09T01:41:50.099348Z","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":"2026-08-07T00:32:07.450534Z","title":"Self-supervised policy adaptation during deployment","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.13750","last_updated":"2025-06-16T17:56:22Z","snapshot_observed_at":"2026-08-09T16:56:18.576968Z","submitted_at":"2025-06-16T17:56:22Z","title":"Test3R: Learning to Reconstruct 3D at Test Time","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T00:32:07.450534Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2506.13750"},"observation_digest":"sha256:02c603c68847e0b3c7176010db605ae645c11fffdf6fcfee3dbc5f7dc6f99869","observation_id":"6191a1ef-ce87-4cd1-8462-4548b21a191f","resolution":{"observed_at":"2026-08-07T00:32:07.450534Z","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":"2026-08-09T01:41:50.099348Z","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":"2026-08-07T00:26:25.133611Z","title":"Self-supervised policy adaptation during deployment","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.14287","last_updated":"2025-06-17T07:59:07Z","snapshot_observed_at":"2026-08-09T04:06:15.863490Z","submitted_at":"2025-06-17T07:59:07Z","title":"Steering Robots with Inference-Time Interactions","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T00:26:25.133611Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2506.14287"},"observation_digest":"sha256:a18c7da490217854cee0b588fe2391e73a7ba707854f515f3d009a612cd26d8d","observation_id":"5c9cf636-220d-4e52-b5ad-886b3f6632fa","resolution":{"observed_at":"2026-08-07T00:26:25.133611Z","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":"2026-08-09T01:41:50.099348Z","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":"2026-08-06T21:08:32.168052Z","title":"Self-supervised policy adaptation during deployment,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.00984","last_updated":"2025-07-01T17:36:09Z","snapshot_observed_at":"2026-08-06T20:58:46.284108Z","submitted_at":"2025-07-01T17:36:09Z","title":"Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:08:32.168052Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2507.00984"},"observation_digest":"sha256:4e314ac5da49c7f12cf9101faa367a3b1072378124f90b9e866740f45f4d84de","observation_id":"3a5cf096-a024-4f53-8581-36c7cc0126ed","resolution":{"observed_at":"2026-08-06T21:08:32.168052Z","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":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2507.13662","last_updated":"2026-04-09T03:13:00Z","snapshot_observed_at":"2026-08-03T04:10:35.033322Z","submitted_at":"2025-07-18T05:13:02Z","title":"Iteratively Learning Muscle Memory for Legged Robots to Master Adaptive and High Precision Locomotion","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-19T04:51:03.733792Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2507.13662"},"observation_digest":"sha256:9035fe8c08c7189bc0934cff78cc6591229ba131090d36c0e17701ad6cfafc1c","observation_id":"ee1060ab-e2ec-417a-a17f-77581f96c995","resolution":{"observed_at":"2026-05-19T04:52:03.671968Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2605.05857","last_updated":"2026-06-09T16:00:28Z","snapshot_observed_at":"2026-08-08T21:37:55.736947Z","submitted_at":"2026-05-07T08:26:59Z","title":"Offline Reinforcement Learning for Rotation Profile Control in Tokamaks","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-08T14:49:07.819928Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2605.05857"},"observation_digest":"sha256:6cd21e5afadcd83d23fb59e2c78ef477f513fa21cb273b37cefac258f872d4be","observation_id":"61960401-845d-46b1-addc-bdcc24d71f1c","resolution":{"observed_at":"2026-05-11T18:41:09.854684Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2606.03127","last_updated":"2026-06-02T04:10:39Z","snapshot_observed_at":"2026-07-06T23:43:27.226603Z","submitted_at":"2026-06-02T04:10:39Z","title":"TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T10:09:08.056968Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2606.03127"},"observation_digest":"sha256:3732d4573bc5320c95efd946fd28ee93a13bbcd053897d91b06adc0065ab5759","observation_id":"21141447-d0a3-46dd-9454-6dd94c81277f","resolution":{"observed_at":"2026-07-02T03:26:28.505481Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2606.26515","last_updated":"2026-07-10T19:28:32Z","snapshot_observed_at":"2026-08-02T05:35:28.099281Z","submitted_at":"2026-06-25T01:40:10Z","title":"Forget, Anticipate and Adapt: Test Time Training for Long Videos","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-26T05:34:05.109347Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2606.26515"},"observation_digest":"sha256:29e93af0a96d70ec6e8bf7cf919e65fd126be71f5a4ff98188634d7a17e14e91","observation_id":"447d9c6e-ce0b-4bf5-9c88-3d3bc24b4a73","resolution":{"observed_at":"2026-07-04T12:59:53.027592Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2606.26515","last_updated":"2026-07-10T19:28:32Z","snapshot_observed_at":"2026-08-02T05:35:28.099281Z","submitted_at":"2026-06-25T01:40:10Z","title":"Forget, Anticipate and Adapt: Test Time Training for Long Videos","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-30T10:22:49.075651Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2606.26515"},"observation_digest":"sha256:d37dd1df4b54e13330ac8e4644e49fae3652cf896012e43fc5d2ecd8c60eb692","observation_id":"afbd6ade-3004-4a5a-88c6-de7ffa33a4ec","resolution":{"observed_at":"2026-06-30T11:54:38.959830Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","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":"2026-07-14T17:16:44.626166Z","title":"Self-supervised policy adaptation during deployment.arXiv preprint arXiv:2007.04309, 2020","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.26515","last_updated":"2026-07-10T19:28:32Z","snapshot_observed_at":"2026-08-02T05:35:28.099281Z","submitted_at":"2026-06-25T01:40:10Z","title":"Forget, Anticipate and Adapt: Test Time Training for Long Videos","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-14T17:16:44.626166Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2606.26515"},"observation_digest":"sha256:42bbdfe2283a32db2f50d180eb961a69dfff2c377ff3e14d92eb1f839811938b","observation_id":"71821db2-e7c4-4ab7-adc0-c194a512687e","resolution":{"observed_at":"2026-07-14T17:16:44.626166Z","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":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment","version":3},"cited_work":{"arxiv_id":"2007.04309","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.04309","snapshot_observed_at":"2026-07-04T12:59:53.026159Z","title":"//arxiv.org/abs/2007.04309","venue":null,"work_id":"cedaea11-8629-4d18-9037-b79982555120","year":2007},"citing_paper":{"arxiv_id":"2606.30192","last_updated":"2026-06-29T12:08:08Z","snapshot_observed_at":"2026-07-07T00:04:05.275293Z","submitted_at":"2026-06-29T12:08:08Z","title":"Domain Adaptation with Adaptive Imagination for Visual Reinforcement Learning under Limited Target Data","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-06-30T06:26:00.231068Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2606.30192"},"observation_digest":"sha256:41fba4cc683f441854fe7e9a9483aefd79baaa59584e332aaa020fe218b156db","observation_id":"431f7628-f1a3-4404-88ec-e137e840d027","resolution":{"observed_at":"2026-06-30T06:34:19.392578Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","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":"2026-07-14T06:33:12.974578Z","title":"Hansen, R","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.11167","last_updated":"2026-07-13T07:03:29Z","snapshot_observed_at":"2026-08-04T12:27:56.327233Z","submitted_at":"2026-07-13T07:03:29Z","title":"Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T06:33:12.974578Z"},"links":{"cited_paper":"/paper/2007.04309","citing_paper":"/paper/2607.11167"},"observation_digest":"sha256:7031e7488f83d58d1a014cdb56db8008c662665cced71534db45fff527b1feb5","observation_id":"a0cd3438-9cd5-41c7-bd17-a6063a10a94e","resolution":{"observed_at":"2026-07-14T06:33:12.974578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2007.04309/citation-record","integrity":"/paper/2007.04309/integrity","json":"/paper/2007.04309/citation-record.json","paper":"/paper/2007.04309"},"outbound":[],"paper":{"arxiv_id":"2007.04309","last_updated":"2021-04-09T02:47:39Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T01:41:50.099348Z","submitted_at":"2020-07-08T17:56:27Z","title":"Self-Supervised Policy Adaptation during Deployment"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2007.04309."}