{"as_of":"2026-08-13T04:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a57d8735f094e44cc1a7600c8232fa62a2673232c0f0888a28f3662c9da8480a","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:04:33.476637Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"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/2411.16532/citation-record","integrity":"/paper/2411.16532/integrity","json":"/paper/2411.16532/citation-record.json","paper":"/paper/2411.16532"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.995765Z","title":"Cells 10(4), 735 (2021)","venue":null,"work_id":"6886cd29-6662-4460-a9f0-30a9090c086e","year":2021},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.302448Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:d8cee9d9fecf9b3278bbef524d3f4b96dfb0ac205813ba7c8451b0cf27a099c9","observation_id":"3d6f0111-0dc4-4dad-ac00-972585748772","resolution":{"observed_at":"2026-08-12T13:04:34.000303Z","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":"2026-08-12T13:04:33.983858Z","title":null,"venue":null,"work_id":"266efb78-dcef-4d5b-9489-f823c3f4d928","year":2018},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.306664Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:483e0a5dbfa596e2d6b0c9bfa9e6e6c8a04acbda7ded8e0c36bfbecfecc8bcc4","observation_id":"5ab6beb6-2e3c-4c75-aa1c-f29ddc6b8f8b","resolution":{"observed_at":"2026-08-12T13:04:33.987797Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.972128Z","title":"Science 245(4918), 605–615 (1989) 30","venue":null,"work_id":"fc0184c4-c0b9-49e9-bb79-616e0bce284f","year":1989},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.310553Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:1a3c486349b809a2f3f1aa96a3df047a98f3761663da69afdd01eec2b97caa9b","observation_id":"508ab118-4129-42c4-9261-d7d65c13c3b5","resolution":{"observed_at":"2026-08-12T13:04:33.975964Z","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":"2026-08-12T13:04:33.960876Z","title":"Neural networks 113, 54–71 (2019)","venue":null,"work_id":"5e1834cb-2680-428b-9f69-dff2a693d659","year":2019},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.314578Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:fb838f95d2bd3547a2479dbbaa68231cf6b25001b64ed3c6dcc89809cf9bc68a","observation_id":"1b08ebbc-5438-45d4-95fd-863080473ee9","resolution":{"observed_at":"2026-08-12T13:04:33.964520Z","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":"2026-08-12T13:04:33.949560Z","title":"Journal of Artificial Intelligence Research 61, 523–562 (2018)","venue":null,"work_id":"525c0bd2-d9b4-4b71-acd0-2ae11611fc12","year":2018},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.318584Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:333431e2cf02fb0c3516a4e80cd467d318cbbd18ea112ae68f066cec7270db2a","observation_id":"7cee9194-7588-4155-8b8d-bcf239d5864d","resolution":{"observed_at":"2026-08-12T13:04:33.953167Z","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":"2304.09355","last_updated":"2023-11-21T13:12:21Z","snapshot_observed_at":"2026-08-12T03:51:54.282651Z","submitted_at":"2023-04-19T00:33:59Z","title":"To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.09355","snapshot_observed_at":"2026-08-12T13:04:33.323479Z","title":"arXiv preprint arXiv:2304.09355 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.323479Z"},"links":{"cited_paper":"/paper/2304.09355","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:d4818028d8080c2444f34710b95b9f5cf4d20c29d9eb3e602a9738c6448aec9c","observation_id":"8bc7dc0f-5713-488e-97aa-023518d0b04b","resolution":{"observed_at":"2026-08-12T13:04:33.323479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.05363","last_updated":"2017-05-15T17:56:22Z","snapshot_observed_at":"2026-07-06T05:42:42.520454Z","submitted_at":"2017-05-15T17:56:22Z","title":"Curiosity-driven Exploration by Self-supervised Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.05363","snapshot_observed_at":"2026-08-12T13:04:33.328097Z","title":"arXiv preprint arXiv:1705.05363 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.328097Z"},"links":{"cited_paper":"/paper/1705.05363","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:d651c6f580cf73d48a7fd1cb45243debacf54e038657abfabaa1d60e46fa2697","observation_id":"59fa3a3f-e423-4c6b-916e-96f4b5a60a01","resolution":{"observed_at":"2026-08-12T13:04:33.328097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.938898Z","title":"Advances in Neural Information Processing Systems 34, 20516–20530 (2021)","venue":null,"work_id":"4058b8b7-4aaa-42a6-9c59-1a4efcff526b","year":2021},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.331989Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:237c1750a9571e4793b5ff63f7461f7e8fc5a5810941472fda9a0129f16d52a2","observation_id":"bfe4ed04-c549-4203-957d-74e71ba5c79c","resolution":{"observed_at":"2026-08-12T13:04:33.942572Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.335646Z","title":"Advances in neural information processing systems 12 (1999)","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.335646Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:f8f10fa1a03eab1080fe314c61c25efdb56a737be1c186fd3a819a44ae44f146","observation_id":"13fa37b2-2ff8-49f2-a231-366d5667748f","resolution":{"observed_at":"2026-08-12T13:04:33.335646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.921130Z","title":"Advances in Neural Information Processing Systems 33, 11734–11743 (2020)","venue":null,"work_id":"c0618017-44ea-4bdc-a6ba-ac7abcf9ad34","year":2020},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.338866Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:d23bef5582e11b1a68ae7ace97c884f87f731f3e9a2f3171f910f071fc35ba25","observation_id":"c1019052-cfe8-4249-957e-95ac70dc885a","resolution":{"observed_at":"2026-08-12T13:04:33.925079Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.342505Z","title":"Proceedings of the national academy of sciences 114(13), 3521–3526 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.342505Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:c09a3a36cf39a202f32085bcdaf5ff7fcbb113b4932aeda24796f089753e55a2","observation_id":"e668401b-a5e1-4d5d-b45c-9c77a738700e","resolution":{"observed_at":"2026-08-12T13:04:33.342505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.04671","last_updated":"2022-10-22T14:34:44Z","snapshot_observed_at":"2026-08-13T00:19:22.520801Z","submitted_at":"2016-06-15T08:20:51Z","title":"Progressive Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.04671","snapshot_observed_at":"2026-08-12T13:04:33.346040Z","title":"arXiv preprint arXiv:1606.04671 (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.346040Z"},"links":{"cited_paper":"/paper/1606.04671","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:e8059f960cc94fe4278f1931493fe7c57071f1e4545f54d468f2f9a6be7fbba1","observation_id":"f9854f95-356f-43d3-85b2-5734f2bd88bf","resolution":{"observed_at":"2026-08-12T13:04:33.346040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.904466Z","title":"In: International Conference on Machine Learning, pp","venue":null,"work_id":"1ecfb9bc-e7d8-4b0f-9d6e-361c0541de71","year":2018},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.349859Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:9b4b5d52b8d4c4f472d5feff7ddd32d60b25aeaa91f3b709fb35c00cd11579ea","observation_id":"e2910c15-50c8-489d-99e8-d097661aa008","resolution":{"observed_at":"2026-08-12T13:04:33.907919Z","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":"1803.10123","last_updated":"2019-02-12T12:21:07Z","snapshot_observed_at":"2026-07-06T06:30:28.648560Z","submitted_at":"2018-03-27T15:11:08Z","title":"Task Agnostic Continual Learning Using Online Variational Bayes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.10123","snapshot_observed_at":"2026-08-12T13:04:33.353425Z","title":"arXiv preprint arXiv:1803.10123 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.353425Z"},"links":{"cited_paper":"/paper/1803.10123","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:aeef60db5b959fb3c198d51508c9e73eef6a1f691af28a356447b179638d2e06","observation_id":"b04eb15e-0154-4f2b-9c76-8394f0ab9f72","resolution":{"observed_at":"2026-08-12T13:04:33.353425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.893009Z","title":"In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 31 pp","venue":null,"work_id":"b8fdfdbe-c4dc-448e-ab9f-3930f670660c","year":2022},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.357158Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:13b67d782b8843c21efe124f7681765aa3252cbf4dc7430f58ebacb9fe467893","observation_id":"4dbb8b79-7737-40ae-90da-92c40a2cd1d4","resolution":{"observed_at":"2026-08-12T13:04:33.897375Z","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":"1207.4708","last_updated":"2013-06-21T18:07:06Z","snapshot_observed_at":"2026-08-11T00:52:46.808243Z","submitted_at":"2012-07-19T15:33:25Z","title":"The Arcade Learning Environment: An Evaluation Platform for General Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1207.4708","snapshot_observed_at":"2026-08-12T13:04:33.360905Z","title":"Journal of Artifi- cial Intelligence Research 47, 253–279 (2013) https://doi.org/10.1613/jair.3912","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.360905Z"},"links":{"cited_paper":"/paper/1207.4708","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:3b8f7429f3445cdf903734e5229404aa9f8777a7d7d9dbc79b27b76c40591d0c","observation_id":"d4a4c9f3-7bd1-4092-8ec8-7a5b96d9b95b","resolution":{"observed_at":"2026-08-12T13:04:33.360905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.882745Z","title":"In: ICLR (2016)","venue":null,"work_id":"82fbf792-88f8-4b41-89ba-c95ffb07facb","year":2016},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.364692Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:ce69fdec5ab36f3a1cd5404038225bf9a2c4f3f8e6d6a60a5c4b0b5d50f22c81","observation_id":"0961907f-0f71-4b7b-ad9b-a7d81d5bc46c","resolution":{"observed_at":"2026-08-12T13:04:33.886216Z","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":"2026-08-12T13:04:33.871881Z","title":"In: The 22nd International Conference on Artificial Intelligence and Statistics, pp","venue":null,"work_id":"851c9426-948b-4634-88cf-af5e65941604","year":2019},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.368406Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:227be0f843d718fe25d9cee5a7a61e721e7cfdcc99d7f61a010d2722e8aac914","observation_id":"c5d5095a-d4ef-413f-b0f0-087cb09207d1","resolution":{"observed_at":"2026-08-12T13:04:33.875748Z","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":"2026-08-12T13:04:33.859352Z","title":"Advances in Neural Information Processing Systems 34, 6920–6933 (2021)","venue":null,"work_id":"a5bba8f9-7005-4bc5-9ada-517d79da8443","year":2021},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.371848Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:62b52a2fd33716b5673624c80b639807995282b3a72048f19c04039fc5eee02b","observation_id":"df9801a2-ea65-4026-ad22-c81c6ac4f70b","resolution":{"observed_at":"2026-08-12T13:04:33.863519Z","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":"1912.12630","last_updated":"2019-12-29T11:10:37Z","snapshot_observed_at":"2026-07-06T08:47:28.564867Z","submitted_at":"2019-12-29T11:10:37Z","title":"Real-time Policy Distillation in Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.12630","snapshot_observed_at":"2026-08-12T13:04:33.375549Z","title":"arXiv preprint arXiv:1912.12630 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.375549Z"},"links":{"cited_paper":"/paper/1912.12630","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:5723ceb5211b8d2cfafa4d306c1846fc0591b7c18070593c2dc2b5af997bfb4a","observation_id":"3f39f943-d78a-4820-8275-10afc997d5ca","resolution":{"observed_at":"2026-08-12T13:04:33.375549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.846709Z","title":"In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp","venue":null,"work_id":"ac8b78c9-3103-42ab-8a7d-d93846e21a4a","year":2021},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.379369Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:90a5a1177e5158a41000d1f92bcd56c8a56f8011bbda0951a36954871704fddc","observation_id":"0a932111-dc06-400f-801e-52eee6581911","resolution":{"observed_at":"2026-08-12T13:04:33.851039Z","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":"2026-08-12T13:04:33.834920Z","title":"In: 2017 International Joint Conference on Neural Networks (IJCNN), pp","venue":null,"work_id":"513fd705-6124-4740-876e-fdac10c3cbdc","year":2017},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.382803Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:e3e53807733f5f03c1b0de02c6cd5a1907f2016d1040875e5b31ac957d0ceffc","observation_id":"200a8345-0f36-4f0e-a2ea-ee66be6519cc","resolution":{"observed_at":"2026-08-12T13:04:33.838922Z","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":"2026-08-12T13:04:33.824031Z","title":"IEEE Transactions on Cognitive and Developmental Systems (2023)","venue":null,"work_id":"15c09556-9494-45eb-9649-572032bc966e","year":2023},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.386277Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:ae5488ecf976e45e6d525953f574a367b1197431b5dac451accd770a96a5e1d3","observation_id":"18edfbd2-871e-425a-9738-021deae3083d","resolution":{"observed_at":"2026-08-12T13:04:33.827785Z","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":"2026-08-12T13:04:33.812164Z","title":"In: International Conference on Learning Representations (2018)","venue":null,"work_id":"f43b898a-983e-4780-a891-9a7320a5847a","year":2018},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.389631Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:2a3b4c136f8ce093c6b9dd810121aaaa7b54ff35decc0a2052e538b2ab720b92","observation_id":"6f89f528-1589-44b8-840e-43b2b44fee02","resolution":{"observed_at":"2026-08-12T13:04:33.816581Z","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":"2026-08-12T13:04:33.800755Z","title":"Nature communications 11(1), 4069 (2020)","venue":null,"work_id":"67aaae2e-76d2-4b2f-a13a-fddfee4a9c75","year":2020},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.392888Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:86508fb4f7df3519f370c5d0abb860ec02c2c62aa94b8abe1abbd91ae34f471b","observation_id":"3a55fb09-7745-44f9-afc3-2754b00c7c39","resolution":{"observed_at":"2026-08-12T13:04:33.804356Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.396185Z","title":"IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.396185Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:79fef0249b1300e33c3b20bdaac40d3efcb8d851ea677443395495540898517b","observation_id":"72f21cca-6515-4f37-88c2-7312c816baee","resolution":{"observed_at":"2026-08-12T13:04:33.396185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.783476Z","title":"Advances in neural information processing 32 systems 32 (2019)","venue":null,"work_id":"402f5f81-4d5d-4f75-b98f-8c03c1d22346","year":2019},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.399706Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:2805e1fdd4d633d1e8b7afe348a062a883fb3a11184f1d801f060a291fb44a93","observation_id":"84193407-894a-4158-a799-733714799c99","resolution":{"observed_at":"2026-08-12T13:04:33.787414Z","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":"2110.06976","last_updated":"2022-04-05T02:33:03Z","snapshot_observed_at":"2026-08-08T05:03:26.906938Z","submitted_at":"2021-10-13T18:38:06Z","title":"Representational Continuity for Unsupervised Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.06976","snapshot_observed_at":"2026-08-12T13:04:33.403044Z","title":"arXiv preprint arXiv:2110.06976 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.403044Z"},"links":{"cited_paper":"/paper/2110.06976","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:960eea699c64ef8090c56a6306fd7de3cae9b4940c7909f21bdc8858929e3777","observation_id":"f4fae2a1-5c9a-434d-bac5-5f9cb3089932","resolution":{"observed_at":"2026-08-12T13:04:33.403044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.772115Z","title":"Journal of Neuroscience 35(3), 1319–1334 (2015)","venue":null,"work_id":"170444ba-b47a-4a0c-aeb2-f7aa8d6260b9","year":2015},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.406484Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:0a5d2d7f12ed5ae0f20bf56d2559d24dc612d13312ed51dd15634a2e62d1e7f1","observation_id":"82020a5a-f4a8-4ade-8d5b-ebe7363f646c","resolution":{"observed_at":"2026-08-12T13:04:33.775722Z","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":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-12T13:04:33.409953Z","title":"arXiv preprint arXiv:1503.02531 (2015)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.409953Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:ad3464792154ec19ee5fdc58557054c42cde075114b2cf380ce3101b5b635c1c","observation_id":"466d7de1-1ff0-461e-8bea-d9da57b2bba7","resolution":{"observed_at":"2026-08-12T13:04:33.409953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.761358Z","title":"Advances in neural information processing systems 17 (2004)","venue":null,"work_id":"8f9c3bb3-f37f-499b-9a75-fe6a62fcac5e","year":2004},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.413941Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:8738fd10933f5024921b805a015b43fe76fdd55c160760314580d0c44dc2b5d0","observation_id":"e3b32040-905c-48e9-9248-3a85e909a8f8","resolution":{"observed_at":"2026-08-12T13:04:33.764965Z","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":"2026-08-12T13:04:33.750267Z","title":"Neuron 36(2), 285–298 (2002)","venue":null,"work_id":"45a731f3-b36b-4273-b6cd-e448b9667746","year":2002},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.417530Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:3e1616fc1ec6c63b959332a8b0bb6caf9ace16a2f1c8bc276869482629b4b972","observation_id":"a73f815f-afdf-450b-8c0a-4496db8d9564","resolution":{"observed_at":"2026-08-12T13:04:33.754102Z","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":"2026-08-12T13:04:33.738514Z","title":"In: 2017 Joint IEEE Inter- national Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp","venue":null,"work_id":"d9f85b2c-591e-4766-9815-ba59c59e3a8c","year":2017},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.420796Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:d3ecf46ac0decbdfd4f2af12dceaf340ccead2f348fdf6086945bea71b6fceae","observation_id":"f1adc505-7f3d-43bb-be03-3b96eb5aca1b","resolution":{"observed_at":"2026-08-12T13:04:33.742826Z","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":"2026-08-12T13:04:33.726693Z","title":"In: Proc","venue":null,"work_id":"8780db58-7b76-426c-8a77-2ac0d9702e50","year":1991},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.424393Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:66c79dd33a0c6c19f42809daef91af0b4a15a0990b02b8f732979a465d49e542","observation_id":"24e8eeb7-bb33-4bb6-98ce-8d77ec2cb8aa","resolution":{"observed_at":"2026-08-12T13:04:33.730510Z","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":"1312.5602","last_updated":"2013-12-19T16:00:08Z","snapshot_observed_at":"2026-08-12T14:28:10.961989Z","submitted_at":"2013-12-19T16:00:08Z","title":"Playing Atari with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5602","snapshot_observed_at":"2026-08-12T13:04:33.428019Z","title":"arXiv (2013)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.428019Z"},"links":{"cited_paper":"/paper/1312.5602","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:8652f2ef18632c7827c988d90e020cdbcaacf430c4ffb0f2e4aafaf1ec49a1c6","observation_id":"f2d877f1-c5a5-41d2-9ef3-d808eded0a87","resolution":{"observed_at":"2026-08-12T13:04:33.428019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.07274","last_updated":"2018-11-26T04:56:31Z","snapshot_observed_at":"2026-07-06T05:27:30.168672Z","submitted_at":"2017-01-25T11:52:11Z","title":"Deep Reinforcement Learning: An Overview","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.07274","snapshot_observed_at":"2026-08-12T13:04:33.432291Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.432291Z"},"links":{"cited_paper":"/paper/1701.07274","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:a940b1ee02af18ed212cfb7cd90cbf0cb476bd7b4dca205830e184a7088c206c","observation_id":"903530e3-5c2a-4613-bf72-072787bbcfcb","resolution":{"observed_at":"2026-08-12T13:04:33.432291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.715017Z","title":"In: International Conference on Machine Learning, pp","venue":null,"work_id":"4ffa40d1-9abf-47ed-869b-065226c22f81","year":2016},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.436012Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:bb1904e05d64a8d259f1a6267ea8f36901143a3c31a83d1283fcf25b366026de","observation_id":"a4727dc8-b876-48ca-ad64-1e0c6ff400f8","resolution":{"observed_at":"2026-08-12T13:04:33.718636Z","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":"2026-08-12T13:04:33.704125Z","title":"Advances in neural information processing systems 30 (2017)","venue":null,"work_id":"062205c2-55bb-4ea6-a128-bf44447c1a9d","year":2017},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.439530Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:b33601d79f4a18230fdc658e3b3a5b32b00cf7f86e78f7e61d35da3f8b0d2927","observation_id":"761d1af0-06e1-459c-8e8c-a7cd51bb8761","resolution":{"observed_at":"2026-08-12T13:04:33.707913Z","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":"1506.02438","last_updated":"2018-10-20T18:55:07Z","snapshot_observed_at":"2026-08-12T21:29:36.308819Z","submitted_at":"2015-06-08T11:12:48Z","title":"High-Dimensional Continuous Control Using Generalized Advantage Estimation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.02438","snapshot_observed_at":"2026-08-12T13:04:33.442693Z","title":"arXiv preprint arXiv:1506.02438 (2015)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.442693Z"},"links":{"cited_paper":"/paper/1506.02438","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:a635f8f4cb845112a5838b6e767c1cce0889cd6bb888ce95676c87c0cd7af78c","observation_id":"2f092a20-d89f-4343-9afd-1b09ddd698ce","resolution":{"observed_at":"2026-08-12T13:04:33.442693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.00487","last_updated":"2024-02-06T09:12:09Z","snapshot_observed_at":"2026-08-12T22:41:20.493324Z","submitted_at":"2023-01-31T11:34:56Z","title":"A Comprehensive Survey of Continual Learning: Theory, Method and Application","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.00487","snapshot_observed_at":"2026-08-12T13:04:33.446520Z","title":"arXiv preprint arXiv:2302.00487 33 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.446520Z"},"links":{"cited_paper":"/paper/2302.00487","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:56883674dc589ee732e8a4d8ab1c3e91d7701ccea6666614a6ffb90335eeef19","observation_id":"b4e07829-0e75-460c-b752-d65fbe8009a0","resolution":{"observed_at":"2026-08-12T13:04:33.446520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.692937Z","title":"The Royal Society (2017)","venue":null,"work_id":"211da7a2-e258-4d61-a702-f2fb7babbd11","year":2017},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.450381Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:ceee15f02c5030617e031c11bf947f26e343a69e83d60629c7a00682677fc2a5","observation_id":"963beb16-2806-4317-ae76-541026cf762e","resolution":{"observed_at":"2026-08-12T13:04:33.696586Z","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":"1904.07734","last_updated":"2019-04-15T12:22:36Z","snapshot_observed_at":"2026-08-12T16:57:46.530292Z","submitted_at":"2019-04-15T12:22:36Z","title":"Three scenarios for continual learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.07734","snapshot_observed_at":"2026-08-12T13:04:33.453841Z","title":"arXiv preprint arXiv:1904.07734 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.453841Z"},"links":{"cited_paper":"/paper/1904.07734","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:eb682880285aa04a606f24668c26cff40870a2dfee4ee783de2eb1cb1c729153","observation_id":"6b8dbcde-d17d-481b-880b-40507494b369","resolution":{"observed_at":"2026-08-12T13:04:33.453841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.681752Z","title":"In: 2020 IEEE Conference on Games (CoG), pp","venue":null,"work_id":"b326b147-dcc7-49cd-9b6f-1b3d1c608da5","year":2020},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.457913Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:7e34a616a973357e5f8edb7699fe920b6985d694abf30b879754ca09e0203b60","observation_id":"14362b97-6061-4366-a6b7-176c781f394f","resolution":{"observed_at":"2026-08-12T13:04:33.685442Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.461674Z","title":"Advances in neural information processing systems 32 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.461674Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:d4d6e0e6805e20e4b57b43815a1472a9d67425271e6d47ec3118db0a80c77a70","observation_id":"32a9bff2-f1d3-4806-89fd-485e27cad26a","resolution":{"observed_at":"2026-08-12T13:04:33.461674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.663812Z","title":"GitHub (2018)","venue":null,"work_id":"34c96bc7-77af-4bd7-9c45-e5287aa58334","year":2018},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.465196Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:943969f554f8658513129492f00e7902a26b59762d5b49bc69cd46d7d9042c75","observation_id":"357f10ae-bf2a-469c-a1a8-98053f6a1c91","resolution":{"observed_at":"2026-08-12T13:04:33.667839Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:04:33.469035Z","title":"Journal of Machine Learning Research 22(268), 1–8 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.469035Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:733b22e4b25a4c02103f55e6dba00c9c06622780b4efe7bd25e74d7eb0ebd331","observation_id":"13d9c609-195f-4662-bbdf-d02a6844f5fb","resolution":{"observed_at":"2026-08-12T13:04:33.469035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.13657","last_updated":"2022-03-24T14:32:41Z","snapshot_observed_at":"2026-07-06T12:42:19.212566Z","submitted_at":"2022-02-28T10:01:22Z","title":"Avalanche RL: a Continual Reinforcement Learning Library","version":2},"cited_work":{"arxiv_id":"2202.13657","doi":"10.48550/arxiv.2202.13657","metadata_source":"pith","pith_arxiv_id":"2202.13657","snapshot_observed_at":"2026-08-12T18:16:22.223912Z","title":"Avalanche RL: a Continual Reinforcement Learning Library","venue":"cs.LG","work_id":"2c9adb67-ad6a-4093-ab2d-47806e2d3803","year":2022},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.472852Z"},"links":{"cited_paper":"/paper/2202.13657","citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:0e1fc47222ae5c5f404f64475c7e23f3ae1ef84468dc8cd7bfffa864d4b26665","observation_id":"b0eab509-9570-4048-9417-1017022ca7ef","resolution":{"observed_at":"2026-08-12T13:04:33.511484Z","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":"2026-08-12T13:04:33.646868Z","title":"IEEE transactions on pattern analysis and machine intelligence 44(10), 6715–6728 (2021) 34","venue":null,"work_id":"73926c61-f177-4099-a261-eceefa5b8667","year":2021},"citing_paper":{"arxiv_id":"2411.16532","last_updated":"2024-11-25T16:18:39Z","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T13:04:33.476637Z"},"links":{"citing_paper":"/paper/2411.16532"},"observation_digest":"sha256:7bb87a7aabbc55cf5d36f92144b3741d830e99fca4da06e64e8f0cb992c5deda","observation_id":"ef5160b8-72b1-44a0-9221-c0237abc6824","resolution":{"observed_at":"2026-08-12T13:04:33.650548Z","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":"2411.16532","last_updated":"2024-11-25T16:18:39Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T22:42:14.235496Z","submitted_at":"2024-11-25T16:18:39Z","title":"Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":48},"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 13 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2411.16532."}