{"as_of":"2026-08-12T22:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:291f85cc0dc890e752742dcf882d3d0eab59fb3b5ebfd0989885ba0a9e2b43cf","coverage":[{"denominator":68,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":68,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-19T20:37:36.030165Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2605.17017/citation-record","integrity":"/paper/2605.17017/integrity","json":"/paper/2605.17017/citation-record.json","paper":"/paper/2605.17017"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":"2303.08774","doi":"10.1002/tea.20265","metadata_source":"pith","pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPT-4 Technical Report","venue":"cs.CL","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","year":2023},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:1991a18495e06e894d7502b2d6586e98e28208b17bf42905d5707b211bb1e390","observation_id":"d0d4d1fa-72ac-44a1-8a10-08e9089a879f","resolution":{"observed_at":"2026-05-19T20:37:45.227469Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Policy optimization for strictly batch imitation learning","venue":null,"work_id":"6fa0b10c-a2b1-4424-b7fa-c1a60e8ea7e6","year":2024},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:b97060727222415548e4c6b914d4ab398fae29f9d10645c9a53f1a2362644461","observation_id":"6b594a39-a039-41c1-93d5-66378995b896","resolution":{"observed_at":"2026-05-19T20:37:45.480886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.07942","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Balance equation-based distributionally robust offline imitation learning","venue":null,"work_id":"8eecf44f-11c4-420f-a82e-26497c89989d","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:af3ea683f2c6ac3d2a0df017ad623a29cfaa5e22042828e6bbc0c0e284e128bf","observation_id":"ad85ab56-bc0c-41dc-85da-32727521cdb4","resolution":{"observed_at":"2026-05-19T20:37:45.243799Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Markov balance satisfac- tion improves performance in strictly batch offline imitation learning","venue":null,"work_id":"17015320-fc1b-450b-a03e-cd47cdfa93dd","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:f44c3ca8952210e2d27796399125b2a3807c49a2ba757957b2688f5a71be88a9","observation_id":"302bfe2f-eefd-4144-8475-bf65bba86c62","resolution":{"observed_at":"2026-05-19T20:37:45.384325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Conditional kernel imi- tation learning for continuous state environments","venue":null,"work_id":"1fd3a3df-eca8-4ac0-bb01-37dc1c7fc700","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:131d598906dfbba5790588a11bd0c7418f47844dd5e19d543a184b6d789b97c0","observation_id":"ee1ed0ad-ad01-46bb-929b-e16a65c7d7ee","resolution":{"observed_at":"2026-05-19T20:37:45.420817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"The reality gap in robotics: Challenges, solutions, and best practices.Annual Review of Control, Robotics, and Autonomous Systems, 9","venue":null,"work_id":"12bca985-89bf-4e22-af56-f28e4275b302","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:66801f514cf3544831bc6665f68bdc6d647f4358ad355d60f13cde2e2f4df6c7","observation_id":"487ef4bc-5f82-4855-8dec-f8c8aad86676","resolution":{"observed_at":"2026-05-19T20:37:45.467158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-09T14:16:17.494845Z","title":"Routledge","venue":null,"work_id":"6d380337-c390-4034-b15e-fb283cee5fcc","year":2021},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:1ca231d8fddbed3a67b5b067726908f7b818ae64420f3bec2da7a8b07e6f4944","observation_id":"a41a4d34-9230-4bcf-a88b-e3b3e55c4217","resolution":{"observed_at":"2026-05-19T20:37:45.460648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2504.03515","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T13:19:50.317507Z","title":"Dexterous manipulation through imitation learning: A survey.arXiv preprint arXiv:2504.03515","venue":null,"work_id":"d5aa3e02-20df-4f98-ae26-e411884f1944","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:bb89fff4108508f71d77b53e8e525748c79c06259c54c7be0869e5513d4cb57a","observation_id":"5dfda726-48b1-4576-a946-4737464ce04f","resolution":{"observed_at":"2026-05-19T20:37:45.247159Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-12T08:59:05.030983Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":"1607.06450","doi":"10.1007/978-3-319-32025-0","metadata_source":"pith","pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Layer Normalization","venue":"stat.ML","work_id":"20a2d720-0046-4c7c-bcd6-327ec8143f69","year":2016},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:427b48783193b244cb75302caefee2775cc43987299d93aa50fcc224ee5c473a","observation_id":"228cb673-8608-4c24-82b6-5d25f4f6eadc","resolution":{"observed_at":"2026-05-19T20:37:45.208281Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Successor features for transfer in reinforcement learning.Advances in neural information processing systems, 30","venue":null,"work_id":"083e2c2b-9ce1-4b66-92db-4b6e2e32fcb0","year":2017},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:ab4f2bfb299040f9868e2d1d9ad82870914a424445d051cba4aac0315561a29a","observation_id":"92ae4e73-b072-47c6-a1e8-26fa7684f8fc","resolution":{"observed_at":"2026-05-19T20:37:45.457022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2101.07123","last_updated":"2021-01-18T15:33:26Z","snapshot_observed_at":"2026-08-09T14:57:50.184993Z","submitted_at":"2021-01-18T15:33:26Z","title":"Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint","version":1},"cited_work":{"arxiv_id":"2101.07123","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2101.07123","snapshot_observed_at":"2026-07-01T06:55:29.516438Z","title":"arXiv preprint arXiv:2101.07123 , year=","venue":null,"work_id":"39690949-732b-4e57-ad99-9cb8cedf5239","year":2021},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2101.07123","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:55e481db3f26b0c25d80d8797793d4915328a959c10fa02d1d4319697f21e00d","observation_id":"fb48cde2-afe1-4053-a90d-38dd781e6fc2","resolution":{"observed_at":"2026-05-19T20:37:45.250688Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"03a4c7bb-9f81-4d75-87ac-25fa0a98ffe1","year":2026},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:1ec97d26a7319ff98bf0df425e8d503e9e9d07db410dcda32d0bbf61cc31ace1","observation_id":"64dd5017-b8dc-4058-beee-a8b6ce7b01de","resolution":{"observed_at":"2026-05-19T20:37:45.473195Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.07626","last_updated":"2018-12-18T20:01:41Z","snapshot_observed_at":"2026-07-06T07:22:04.461006Z","submitted_at":"2018-12-18T20:01:41Z","title":"Universal Successor Features Approximators","version":1},"cited_work":{"arxiv_id":"1812.07626","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.07626","snapshot_observed_at":"2026-06-29T22:24:00.767730Z","title":"Universal Successor Features Approximators","venue":"cs.LG","work_id":"078d8d23-c0c6-48dc-98c4-6bd0fc86aacd","year":2018},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/1812.07626","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:650d553a2e69c1b8cb340a254a72b97bfd83ba552d8aba691600fb0dff1a56ec","observation_id":"1ab9efc2-4114-43f8-8069-bfd5fc90ee35","resolution":{"observed_at":"2026-05-19T20:37:45.218909Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Cambridge university press","venue":null,"work_id":"42881ce8-e7a2-4884-98e7-0f8e31eb0410","year":2004},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:c493d218c65845520c8ca35d06e50d1c94af52615f51a7d8ed3fd4586fccb137","observation_id":"2e29fd85-b5c7-453c-a1ea-42ad438d8318","resolution":{"observed_at":"2026-05-19T20:37:45.471280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.12894","last_updated":"2018-10-30T17:44:42Z","snapshot_observed_at":"2026-08-12T12:07:37.369391Z","submitted_at":"2018-10-30T17:44:42Z","title":"Exploration by Random Network Distillation","version":1},"cited_work":{"arxiv_id":"1810.12894","doi":"10.48550/arxiv.1810.12894","metadata_source":"pith","pith_arxiv_id":"1810.12894","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Exploration by Random Network Distillation","venue":"cs.LG","work_id":"5a87fef6-96e2-4d5b-91ec-1a7c9a43cab9","year":2018},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/1810.12894","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:8f34eb0bea5db4d17af08641153c8050f7e1fa3de1b421a5f06e872424765aa3","observation_id":"5652da79-0a06-42a5-bcc0-dd081280835f","resolution":{"observed_at":"2026-05-19T20:37:45.204809Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Robust imitation learning against variations in environment dynamics","venue":null,"work_id":"685465bc-5bff-450d-ae9a-2e5bf6e20747","year":2022},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:4d6926d0f535ba7ce4708a44b310fdccea9a9a81c80bd2e6adae7ed3dca83862","observation_id":"9e877939-3746-4fa2-a1fb-32e6723da8fb","resolution":{"observed_at":"2026-05-19T20:37:45.399415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Meta-controller: Few- shot imitation of unseen embodiments and tasks in continuous control.Advances in Neural Information Processing Systems, 37:134250–134286","venue":null,"work_id":"c7cfaf2f-13a7-4a10-9489-b974ada4c34e","year":2024},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:06e7714f176f0a5c69f4491b61b968728e2bb34b94056f97d5037fe34a31cc95","observation_id":"4afc57c0-b27d-4c63-a0ff-626bf1971e85","resolution":{"observed_at":"2026-05-19T20:37:45.405710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Exploring the limitations of behavior cloning for autonomous driving","venue":null,"work_id":"2c3904e7-911d-4104-9e76-be885d3e2645","year":2019},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:4ffce32cb04a7d265eb736dc511783b50012b76871a4850327a20e8e3bd7c0dc","observation_id":"6228e72e-00b0-4b5e-ad24-1202315af337","resolution":{"observed_at":"2026-05-19T20:37:45.476994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Improving generalization for temporal difference learning: The successor repre- sentation.Neural computation, 5(4):613–624","venue":null,"work_id":"17bf38b0-5a7b-480d-9dc0-ed2a4953d88a","year":1993},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:36202824a31a101a38ed15aee0ede33ee53884148dd32df2cb6c012b1756f64a","observation_id":"b87c1f00-298d-46dd-8336-9eeb1327cb38","resolution":{"observed_at":"2026-05-19T20:37:45.386414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.02894","last_updated":"2020-07-14T06:01:03Z","snapshot_observed_at":"2026-08-12T06:29:57.887606Z","submitted_at":"2020-03-05T19:56:23Z","title":"Distributional Robustness and Regularization in Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2003.02894","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.02894","snapshot_observed_at":"2026-07-03T09:57:56.240528Z","title":"Distributional robustness and regularization in reinforcement learning","venue":null,"work_id":"3b671203-828d-407d-82e6-72f32ea375ac","year":2020},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2003.02894","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:9e202ec9c89f7e81940baa27b6705b9786e7a42f67424bf7a9e09de1762f8ed3","observation_id":"24e13565-6d03-4ccc-97e2-e510605aff4e","resolution":{"observed_at":"2026-05-19T20:37:45.201601Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"One-shot imitation learning.Advances in neural information processing systems, 30","venue":null,"work_id":"e766cdfe-3e16-4e02-b68b-3a980bc6066e","year":2017},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:215155f3cc86b2a7738c6b774f4485613a2a991745b63b9adfa6de29fc080dd5","observation_id":"f5046859-1083-4769-b3e5-ef14f7a7de0c","resolution":{"observed_at":"2026-05-19T20:37:45.440659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"One-shot visual imitation learning via meta-learning","venue":null,"work_id":"af11ed3f-cc15-43ff-a2f5-d5645568e954","year":2017},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:713e6b24f44e1d666acef00b40b9e6f870fd45418677ed3a3dd2d443f312cd3d","observation_id":"c5452bd8-cae7-435e-92e9-1edcc1050f52","resolution":{"observed_at":"2026-05-19T20:37:45.382009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Off-policy deep reinforcement learning without exploration","venue":null,"work_id":"30c7bb79-e595-4720-96cf-bff0557b4a1f","year":2052},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:e11ee2d1c5dd15ace51da755f233dd26fe5cb4b8d13c89ffbbecd610635cf661","observation_id":"6ba05ca4-bd91-4d6d-aff7-57f2953542dd","resolution":{"observed_at":"2026-05-19T20:37:45.426795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-06T18:32:48.647830Z","title":"Generative adversarial imitation learning.Advances in neural information processing systems, 29","venue":null,"work_id":"f44162e0-e706-482a-b06e-52b0bf858c2d","year":2016},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:8e677818da26092bc8241f0b9b250ef757f4ed751ce000169b7d70f90a9153ee","observation_id":"99cec878-a7fa-4459-a115-744b1b939329","resolution":{"observed_at":"2026-05-19T20:37:45.479093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Impact of static friction on sim2real in robotic reinforcement learning","venue":null,"work_id":"aa749aa7-1317-44b0-86f4-b1a11aa88917","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:0be32895019e09665afb14097850216d083a9bcd63ae8a1c67e12bb8b3d28907","observation_id":"0d91f97e-a76b-4d5d-a47d-c38da7fe7d7e","resolution":{"observed_at":"2026-05-19T20:37:45.483082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.03062","last_updated":"2021-11-04T17:59:56Z","snapshot_observed_at":"2026-08-07T23:36:51.059335Z","submitted_at":"2021-11-04T17:59:56Z","title":"Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning","version":1},"cited_work":{"arxiv_id":"2111.03062","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.03062","snapshot_observed_at":"2026-07-04T06:59:38.340560Z","title":"Generalization in dexterous manipulation via geometry-aware multi-task learning","venue":null,"work_id":"1946cbb8-0ae5-4610-9255-6bbbc23e4baf","year":2021},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2111.03062","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:20147917c73400546ff7364af395328474de85d4db702f3ba1217ecfa719a4d4","observation_id":"6c5ba828-78d9-42db-a5e2-8cb2bbf7ee81","resolution":{"observed_at":"2026-05-19T20:37:45.240783Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Robust dynamic programming.Mathematics of Operations Research, 30(2): 257–280","venue":null,"work_id":"98914933-f51a-4b92-9397-02a3075a1b51","year":2005},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:a937bea1d8dadd25ada15725b77432e7dd1828b238cb692471edef069060acdd","observation_id":"0d047c0d-3735-4310-bd08-cfe3c3680000","resolution":{"observed_at":"2026-05-19T20:37:45.448336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Task-embedded control networks for few-shot imitation learning","venue":null,"work_id":"2bab571f-7b96-4f3d-8b29-b45c62f7a2fa","year":2018},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:ff1430ff3398cd83a893eb6626aeeafee0e90ddb51f66f7d20a5919d27f63d4d","observation_id":"98381b2b-238d-4c93-851f-71cae5e58330","resolution":{"observed_at":"2026-05-19T20:37:45.436718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Zero-shot reinforcement learning from low quality data.Advances in Neural Information Processing Systems, 37:16894–16942","venue":null,"work_id":"c1856c2e-0d2a-403c-884a-e74b229c3916","year":2024},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:223a7ebac72f4873ace4ffaabfefbf164f536b92de9f452a3a84b3386646f7a7","observation_id":"378fad2b-e5ab-4632-8947-ff4dff9d057b","resolution":{"observed_at":"2026-05-19T20:37:45.446451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15446","last_updated":"2025-06-18T13:18:36Z","snapshot_observed_at":"2026-08-06T23:53:19.876337Z","submitted_at":"2025-06-18T13:18:36Z","title":"Zero-Shot Reinforcement Learning Under Partial Observability","version":1},"cited_work":{"arxiv_id":"2506.15446","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.15446","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zero-shot reinforcement learning under partial observability","venue":null,"work_id":"c88f3800-d1f6-478d-b79e-6250c8b36fcc","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2506.15446","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:8371d09f108c33198b314dd262798ca6ec25ad866fad18078fc7deb6f019a66b","observation_id":"50626c59-d028-4721-9d66-d21892112808","resolution":{"observed_at":"2026-05-19T20:37:45.263201Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"DemoDICE: Offline imitation learning with supplementary imperfect demonstrations","venue":null,"work_id":"39a5023a-1d74-4663-8037-b414076d8b77","year":2022},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:8e62481dd4e9da146119db8cc157997e8268f442d5133b01a9f156b62cdcd7b4","observation_id":"cf8a8014-4bfd-45e0-bdb5-3388cfdcd377","resolution":{"observed_at":"2026-05-19T20:37:45.465187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:ae1b81c016766a424e6f44bc7dfbe7582872cd13981abec3e36e5672c343553c","observation_id":"4ad56f9f-6830-4ff8-b09c-952e52aeb184","resolution":{"observed_at":"2026-05-19T20:37:45.211845Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Imitation learning via off-policy dis- tribution matching","venue":null,"work_id":"645c92ba-a204-47b1-856c-c1b7acf29215","year":2020},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:9f4ca5993849dbedba9283cf46cf0de8566571862aa8e85cf98b456c0d2fd406","observation_id":"db98591e-ab21-47e6-acf0-34767a58ea0b","resolution":{"observed_at":"2026-05-19T20:37:45.388740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Dart: Noise injection for robust imitation learning","venue":null,"work_id":"8801cba1-18a1-4ddc-861f-27684bc2ab0e","year":2017},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:f103febfff338106f37f70431a40648c11628748bb11e2114d11c0836973e5d7","observation_id":"f2a2b538-4dc1-4432-9774-c4c15e14b055","resolution":{"observed_at":"2026-05-19T20:37:45.444273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Aps: Active pretraining with successor features","venue":null,"work_id":"348e75ba-4dfb-408d-8973-3e98f9af12c9","year":2021},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:decacd4531366fcf93c3fce76a06122cdf61317bff4e8fb57f3034caa7ece1bb","observation_id":"6479bbc1-6330-49b3-a89c-6476e218d57e","resolution":{"observed_at":"2026-05-19T20:37:45.432581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"ODICE: Revealing the mystery of distribution correction estimation via orthogonal-gradient update","venue":null,"work_id":"85275a84-ab02-4d4a-8b16-807dc257b140","year":2024},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:26925979385d283c0343fa04d4cd18ca106454a9e44d667e9b84af4acbf63a7b","observation_id":"75cd7414-a16c-4562-963a-64584be505f7","resolution":{"observed_at":"2026-05-19T20:37:45.428799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Robust control of markov decision processes with uncertain transition matrices.Operations Research, 53(5):780–798","venue":null,"work_id":"86a097ee-2c90-4386-b91d-9b94e1b441b0","year":2005},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:5e392f15efc8280d67f3d5d15cdbd01395a1889c46ba02b069dba92d5e59f8dd","observation_id":"9cdbc2f0-11e1-49fd-a1b2-c6a2ac670762","resolution":{"observed_at":"2026-05-19T20:37:45.407854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Robust rein- forcement learning using offline data.Advances in neural information processing systems, 35: 32211–32224","venue":null,"work_id":"810502d0-90c7-4abb-953f-6dcc187cfa23","year":2022},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:15eaeec38540229311ef566cd5992541068761fdbc4edfa16b898b34f3f7f2a1","observation_id":"fa3fb249-47d4-4c57-8b27-b7f15190c9fa","resolution":{"observed_at":"2026-05-19T20:37:45.422808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Distributionally robust behavioral cloning for robust imitation learning","venue":null,"work_id":"79a000bf-a4aa-48a7-bc50-a47fbc94f35e","year":2023},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:c6aa836845a4ccccbe15c39033549d905448e27fde844d012d30b8c93e6abfaa","observation_id":"fb042fcd-6bd1-43e9-b11c-e517380ea1f0","resolution":{"observed_at":"2026-05-19T20:37:45.442452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Bridging distributionally robust learning and offline rl: An approach to mitigate distribution shift and partial data coverage","venue":null,"work_id":"ae8ab25d-3847-4bd9-bac9-84253f46ae39","year":null},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:4d19d246f96845b970ca9a37937fc4ca98ef98fce6a105a3f90a2af4982fbff3","observation_id":"62cd58ac-d32e-4a0f-9343-f87a6f9bfd63","resolution":{"observed_at":"2026-05-19T20:37:45.418899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"d4950fc1-8873-4937-804f-a919c79c76cc","year":null},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:514e6ec7f3b66c81c3ceb78b28497be4e7edb7a6370e64ae5c4d284492eb7489","observation_id":"574bbc35-79a0-496e-86ad-02e0a0e73f6b","resolution":{"observed_at":"2026-05-19T20:37:45.379771Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15567","last_updated":"2024-05-26T17:44:52Z","snapshot_observed_at":"2026-08-07T05:14:40.258185Z","submitted_at":"2024-02-23T19:09:10Z","title":"Foundation Policies with Hilbert Representations","version":2},"cited_work":{"arxiv_id":"2402.15567","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.15567","snapshot_observed_at":"2026-07-10T16:47:24.471563Z","title":"Foundation policies with hilbert representa- tions","venue":"cs.LG","work_id":"2fb27f1f-1c8d-4966-9179-8be232c321ee","year":2024},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2402.15567","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:6bf80dcffe4b3025e17e1a17d0b8d12af568efc7ed5417f81c02983ed4bded89","observation_id":"e75e58e1-1c3f-4fb7-9b0e-6ae28fec41b7","resolution":{"observed_at":"2026-05-19T20:37:45.260277Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Sim-to-real transfer of robotic control with dynamics randomization","venue":null,"work_id":"956e4197-1b22-43fc-82f5-230ef24b895a","year":2018},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:87064e9a51f6a486c9a2ada9cd7a425879a02e136cd988c152b4047fdc47aa09","observation_id":"943e97fa-5103-46ed-a4e8-f0fd51a4797d","resolution":{"observed_at":"2026-05-19T20:37:45.450503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Fast imitation via behavior foundation models","venue":null,"work_id":"afb3d30f-956f-467d-ba55-df636c81f82e","year":2024},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:f924d538e4917c33f071be87355babd8f573c347c3afff7216113d840a228dc0","observation_id":"6296c59f-de0d-4fcc-a7ff-564032571456","resolution":{"observed_at":"2026-05-19T20:37:45.416519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Alvinn: An autonomous land vehicle in a neural network.Advances in neural information processing systems, 1:305–313","venue":null,"work_id":"b8fde929-a6a8-4b7c-a808-a0f6939742c3","year":1988},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:00fa9243c8f7b30fce70d392d0313018f44e1b6e4499f91fea97a6b3819a174f","observation_id":"78a8279a-4c1b-4e6b-8394-f162292754f2","resolution":{"observed_at":"2026-05-19T20:37:45.475117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-09T08:56:07.242949Z","title":"John Wiley & Sons","venue":null,"work_id":"9b533f83-b0be-457d-b7c4-50ed0d2e17cf","year":2014},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:69c88b8f4e0c2a2be45bf03583fcc650d02a5fcdc59a518d516154856baa0a15","observation_id":"d12b7caa-c795-4f9b-bde8-bbe684d72502","resolution":{"observed_at":"2026-05-19T20:37:45.455086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-08T08:54:50.108470Z","title":"Direct preference optimization: Your language model is secretly a reward model","venue":null,"work_id":"5d2e1082-e73f-4c1b-a18c-bf0ba1772c23","year":2023},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:202300ff3c435104ab008eb61a5992576da8fc0f1fc8facfb0052523b122685a","observation_id":"01daf410-b0ef-4a1b-8d5f-76a6ea800942","resolution":{"observed_at":"2026-05-19T20:37:45.469388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Efficient reductions for imitation learning","venue":null,"work_id":"e62c9fd7-a55b-4c9f-b694-137e1ebf3765","year":2010},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:c3aeee02a0f235d7fbd0182c59410978556b4409668788249afa21c381bfe1f8","observation_id":"f35253c3-497b-45e8-83da-eb0a5e51bb58","resolution":{"observed_at":"2026-05-19T20:37:45.414549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"A reduction of imitation learning and structured prediction to no-regret online learning","venue":null,"work_id":"d12ac32c-aa32-41ed-9794-9ebf3ad31bea","year":2011},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:4182b96564e89e25de8fe60219a044ae5bf2d9043bb8d59a69d6af835d824197","observation_id":"a28e4146-e314-41d0-a4dd-85afdb424bed","resolution":{"observed_at":"2026-05-19T20:37:45.393345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.20264","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimistic task inference for behavior foundation models","venue":null,"work_id":"7a15a57c-8421-4617-8bb2-88f68306c918","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:4c24c2ee28e544702d8d94699b24424b87dfe63674fea350db944601c4350dde","observation_id":"7c383441-5117-4f1e-ab5f-f8714705626f","resolution":{"observed_at":"2026-05-19T20:37:45.253766Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Universal value function ap- proximators","venue":null,"work_id":"cb3f5172-c38f-4fda-abe7-1dcbcf97c588","year":2015},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:4093c5812b487716805f78c489b5d38958010b91bdddc9d5216bb07cbe6a55a8","observation_id":"fc100694-45d1-4f1b-9c80-ef3002ea55cd","resolution":{"observed_at":"2026-05-19T20:37:45.434432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Mitigating covariate shift in behavioral cloning via robust stationary distribution correction","venue":null,"work_id":"e80c9d90-e83e-46ef-951e-2e28e45bf829","year":2024},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:76f9f950be8d6ffd4008f14757d914ce13e6d35f8927e862e33172c3765ad630","observation_id":"d99a6ea9-0443-419f-932f-0e0dd452b71f","resolution":{"observed_at":"2026-05-19T20:37:45.412160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Robust imitation learning from noisy demonstrations","venue":null,"work_id":"fea0b3cd-9612-4a7a-a8f5-4c9faecf31da","year":2021},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:967790e248fdfc5e7561728ecbd8daead2a7653b447789b7550fc8398ec0d527","observation_id":"f5636eb2-1e3e-4256-845e-33a4420257d6","resolution":{"observed_at":"2026-05-19T20:37:45.395347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"1801.00690","doi":"10.48550/arxiv.1801.00690","metadata_source":"pith","pith_arxiv_id":"1801.00690","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeepMind Control Suite","venue":"cs.AI","work_id":"54294ef0-c651-4d5a-a72b-f85a88329a71","year":2018},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/1801.00690","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:1e0326a74c538f3fcb22229f5fe56cb61fa6642fefe8ea6e70d0b77c0635f874","observation_id":"6b9b9ee6-4c1e-4bcb-a4cc-03d53e307c5f","resolution":{"observed_at":"2026-05-19T20:37:45.221781Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Reinforcement learning in robotic systems: A review on sim-to-real transfer.Robotics and Autonomous Systems, page 105327","venue":null,"work_id":"5c54d079-8f8b-4613-a2fb-58050ecd21db","year":2026},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:37d48498e974b591441083a9144199429065afa9f3377b89ff4ae1a5179029a9","observation_id":"1c7145c7-2773-4847-adb6-c71c870065c9","resolution":{"observed_at":"2026-05-19T20:37:45.430698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Learning one representation to optimize all rewards.Advances in Neural Information Processing Systems, 34:13–23","venue":null,"work_id":"d6687d30-d15f-415e-b5aa-ce2ec18af448","year":2021},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:425d23a8401110b1b89f890a860ae1bc3ba86737ee6267bf3f14b71715eeece0","observation_id":"1052ee08-149a-4832-9563-f5105b549d23","resolution":{"observed_at":"2026-05-19T20:37:45.397343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14935","last_updated":"2023-03-01T18:01:09Z","snapshot_observed_at":"2026-08-09T18:51:25.703906Z","submitted_at":"2022-09-29T16:54:05Z","title":"Does Zero-Shot Reinforcement Learning Exist?","version":2},"cited_work":{"arxiv_id":"2209.14935","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.14935","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Does zero-shot reinforcement learning exist?","venue":null,"work_id":"894753e1-ffd8-41b6-af86-d05b466c2241","year":2022},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2209.14935","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:bcae13f5c6b25b5c69bfc4dc378759278c5f98a4fe8ce26d30b6ddab3acd265b","observation_id":"3433b48f-2f43-4459-b30c-2adcf64eba0e","resolution":{"observed_at":"2026-05-19T20:37:45.215435Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Does zero-shot reinforcement learning exist? InThe Eleventh International Conference on Learning Representations","venue":null,"work_id":"0755743d-896e-4ebc-862a-52ea93afae30","year":2023},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:3d19c2698fec03d9505e785a15fa06ce73757a671575755217d82d822b513767","observation_id":"3effa551-61e8-468e-9836-92311e9f06a6","resolution":{"observed_at":"2026-05-19T20:37:45.390719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.19250","last_updated":"2025-08-09T15:01:55Z","snapshot_observed_at":"2026-08-12T10:43:40.556538Z","submitted_at":"2025-06-24T02:19:08Z","title":"Robust Behavior Cloning Via Global Lipschitz Regularization","version":2},"cited_work":{"arxiv_id":"2506.19250","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.19250","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust behavior cloning via global lipschitz regularization","venue":null,"work_id":"7d536bd4-8624-4385-9e4a-d3adc9137870","year":2025},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2506.19250","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:5d56568f9fcd5c29960459fe2b6c9772fe259898f77b3a50c4f3f08f3559d453","observation_id":"eb69b31f-c4dc-4f5d-a0f6-7e2414c2811f","resolution":{"observed_at":"2026-05-19T20:37:45.237389Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Imitation learning from imperfect demonstration","venue":null,"work_id":"daf37b76-83d3-4346-96cf-5ea9681ca290","year":2019},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:eb4e2076403b45bb9d92cc0956ed924d2dfd56bf6cac6cf4b1b9cba772ed60b3","observation_id":"35f7b247-f627-4613-90f3-a994d1b43aa9","resolution":{"observed_at":"2026-05-19T20:37:45.424655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Reinforcement learning with prototypical representations","venue":null,"work_id":"bd9c828c-896d-45dd-9f0b-d8e42660fe51","year":2021},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:2a3f57b5b42a2ef66eca744917e2b47435aeb29e4c43d36d996ae3989d7cd8e7","observation_id":"020a3c53-7333-4145-8845-3298c8553e63","resolution":{"observed_at":"2026-05-19T20:37:45.458883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.13425","last_updated":"2022-04-05T19:24:13Z","snapshot_observed_at":"2026-07-06T12:33:01.613365Z","submitted_at":"2022-01-31T18:39:27Z","title":"Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2201.13425","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2201.13425","snapshot_observed_at":"2026-07-04T06:09:37.556583Z","title":"Yarats, D","venue":null,"work_id":"fc05c902-2d32-46ff-8e32-4011f14da2fc","year":2022},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"cited_paper":"/paper/2201.13425","citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:0cc99cbc6cef987342390d85de049a73c98d4a430f0b6454f31738e1a10a2509","observation_id":"7559b3af-2cf4-49f2-a0d9-7b751ff62f0a","resolution":{"observed_at":"2026-05-19T20:37:45.256980Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Fast bellman updates for wasserstein distributionally robust mdps.Advances in Neural Information Processing Systems, 36:30554–30578","venue":null,"work_id":"b9b741c0-ac8d-41ae-9362-b200f4c65c8d","year":2023},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:f416a2c95fc59c7eb1edbce754cde17c4d19824383ab8ebcc74d49fcf6710da7","observation_id":"4a8932c8-14f1-4f78-a180-820a028518e6","resolution":{"observed_at":"2026-05-19T20:37:45.452636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Breeze: Towards robust zero-shot reinforcement learning","venue":null,"work_id":"3713dbe7-289d-41e4-9d81-206a06e12e18","year":2026},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:0f26c5eecd6ac970d1088305c1b363d903e1e224dfa8e01fa437a95c1267f6f5","observation_id":"03a0514b-17c6-4b7d-90ef-2e363b93740a","resolution":{"observed_at":"2026-05-19T20:37:45.403660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Watch, try, learn: Meta-learning from demonstrations and rewards","venue":null,"work_id":"fd08ff47-673c-4fe3-9d67-67433dce1d9b","year":null},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:4c7c2527471bbb67f42746b3dd43cd4db7e9809aec10726d86a02b3972f174f5","observation_id":"64fb654f-2f22-4e0d-93ca-ce25d5c5da15","resolution":{"observed_at":"2026-05-19T20:37:45.401664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"13 Appendices A Missing Proofs 16 B Extended Related Work 19 C Experimental Setup 20 C.1 ExORL Domains","venue":null,"work_id":"bfd7767c-0c08-49fa-9927-44b42399d7fe","year":null},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:251eb947f6b36ffb1f1f8793f75b7558665b5c8b83c42183a380b2cd2259b712","observation_id":"43c4731a-31b9-4401-8556-fb6e8b54f1a8","resolution":{"observed_at":"2026-05-19T20:37:45.438751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"17 Proof","venue":null,"work_id":"4d403a84-5afc-4d35-b86c-46d5e6a31396","year":null},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:2a80fc891c589d09d461d8c999f295a4def12e667e3299d8d35a30ac41441921","observation_id":"5e7491df-18cb-4e9d-adb8-d40f92e8c373","resolution":{"observed_at":"2026-05-19T20:37:45.462443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Pretrained on RND data","venue":null,"work_id":"0f5cdf31-01ba-4951-9427-e9e9f8b07c0b","year":null},"citing_paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-19T20:37:36.030165Z"},"links":{"citing_paper":"/paper/2605.17017"},"observation_digest":"sha256:d1c8c9b4c685766fa020ad5bff433544a9314183f03964218e63500fd11ba93b","observation_id":"619bf793-8a40-4e38-a338-b1102454590d","resolution":{"observed_at":"2026-05-19T20:37:45.410152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.17017","last_updated":"2026-05-16T14:33:34Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T20:07:50.121314Z","submitted_at":"2026-05-16T14:33:34Z","title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited"},"reference_resolution":{"displayed":68,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":2,"verified_exact":16,"verified_fuzzy":49},"total_outbound_references":68},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2605.17017."}