{"as_of":"2026-08-10T09:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a4f94b537c922910f8360ace42288e0fb2ab3c0c763d229ee931090c769937dd","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:07:26.158821Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T04:05:50.160214Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-15T05:19:45.749504Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"cited_work":{"arxiv_id":"2506.00592","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.00592","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mit- igating plasticity loss in continual reinforcement learning by reducing churn","venue":null,"work_id":"562065aa-db59-45c6-afef-7744bf9d7bfa","year":2025},"citing_paper":{"arxiv_id":"2605.12484","last_updated":"2026-05-14T17:49:32Z","snapshot_observed_at":"2026-08-02T06:23:20.104772Z","submitted_at":"2026-05-12T17:58:20Z","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-13T05:00:31.452781Z"},"links":{"cited_paper":"/paper/2506.00592","citing_paper":"/paper/2605.12484"},"observation_digest":"sha256:21827e0ebd454e93f36cd0ae843093fbd9d02f32f59a12c3b8749be17128a5d6","observation_id":"1296f4cd-21ea-4efc-b362-ee2d8912cd13","resolution":{"observed_at":"2026-05-13T05:07:18.519778Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"cited_work":{"arxiv_id":"2506.00592","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.00592","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mit- igating plasticity loss in continual reinforcement learning by reducing churn","venue":null,"work_id":"562065aa-db59-45c6-afef-7744bf9d7bfa","year":2025},"citing_paper":{"arxiv_id":"2605.12484","last_updated":"2026-05-14T17:49:32Z","snapshot_observed_at":"2026-08-02T06:23:20.104772Z","submitted_at":"2026-05-12T17:58:20Z","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-15T05:19:05.368681Z"},"links":{"cited_paper":"/paper/2506.00592","citing_paper":"/paper/2605.12484"},"observation_digest":"sha256:f7932f1d939800ea2e5537567b1e0d1fc10f80c2f6e55b16d647e089559fc100","observation_id":"11e621f7-d694-4e05-a863-04a03e4ad03a","resolution":{"observed_at":"2026-05-15T05:19:45.752173Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.00592","snapshot_observed_at":"2026-07-31T04:05:50.160214Z","title":"Mitigating plasticity loss in continual reinforcement learning by reducing churn.arXiv preprint arXiv:2506.00592,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.24996","last_updated":"2026-07-27T18:53:00Z","snapshot_observed_at":"2026-08-06T23:30:24.623782Z","submitted_at":"2026-07-27T18:53:00Z","title":"Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning","version":1},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-07-31T04:05:50.160214Z"},"links":{"cited_paper":"/paper/2506.00592","citing_paper":"/paper/2607.24996"},"observation_digest":"sha256:a5fef7b9bba1c16cf9639979b9b040f04044957a84f98229e6ad1f9f6f633e49","observation_id":"0153ae16-e631-4f4e-af06-b8b37d5699ba","resolution":{"observed_at":"2026-07-31T04:05:50.160214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.00592/citation-record","integrity":"/paper/2506.00592/integrity","json":"/paper/2506.00592/citation-record.json","paper":"/paper/2506.00592"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:26.681386Z","title":null,"venue":null,"work_id":"5e52534a-5978-4698-8bf5-9972b5b31ac5","year":2020},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:26.158821Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:f3b59e96a8d891f95f1864d383ea122f981296f4fe727b1dfecf5c1fadf4911f","observation_id":"ffcaccea-3f58-4563-841a-8a9cd5a74986","resolution":{"observed_at":"2026-08-07T12:07:26.720173Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13812","last_updated":"2024-04-09T21:01:56Z","snapshot_observed_at":"2026-08-09T08:28:52.261575Z","submitted_at":"2023-06-23T23:19:21Z","title":"Maintaining Plasticity in Deep Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13812","snapshot_observed_at":"2026-08-07T12:07:24.694086Z","title":"F., Rahman, P., Sutton, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.694086Z"},"links":{"cited_paper":"/paper/2306.13812","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:709f8bffcf49748c81716d66596d352fb7561355c1af8d4508476931ad412585","observation_id":"b5fd2619-5719-453a-b739-20d862b81eea","resolution":{"observed_at":"2026-08-07T12:07:24.694086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17113","last_updated":"2024-12-22T18:01:08Z","snapshot_observed_at":"2026-08-09T12:28:58.586021Z","submitted_at":"2024-12-22T18:01:08Z","title":"Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17113","snapshot_observed_at":"2026-08-07T12:07:24.760896Z","title":"T., Lupu, A., Goldie, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.760896Z"},"links":{"cited_paper":"/paper/2412.17113","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:f0701073505b45a10c458708b07caa47e3b2d72e33f801274c364697029559c6","observation_id":"23362e2a-c199-4b18-a865-c8c6fecf0b92","resolution":{"observed_at":"2026-08-07T12:07:24.760896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00781","last_updated":"2024-04-30T22:52:33Z","snapshot_observed_at":"2026-08-04T23:46:40.606781Z","submitted_at":"2024-03-31T19:57:38Z","title":"Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00781","snapshot_observed_at":"2026-08-07T12:07:24.841166Z","title":"and Mahmood, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.841166Z"},"links":{"cited_paper":"/paper/2404.00781","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:839479ead5bc055b1e03583501b09feef29d83fd5a53f114e846d5c608f79680","observation_id":"db57ee08-4494-48de-9065-11345ab9a021","resolution":{"observed_at":"2026-08-07T12:07:24.841166Z","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":"2026-08-07T12:07:27.924843Z","title":null,"venue":null,"work_id":"c3595518-e966-49c1-bdc4-f067cc892156","year":2024},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.878499Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:3d1ef5cf35a7d1f2e291761daaae3485a87f3ce742b2ac5aa696cca932fb9aa2","observation_id":"48e55a01-91f5-41f9-ab6d-9dd2ebb39426","resolution":{"observed_at":"2026-08-07T12:07:28.016634Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T12:07:27.733374Z","title":"Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T","venue":null,"work_id":"76eedea4-3fd6-49e6-85e0-a6922b69115d","year":2007},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.311840Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:875882527889c241e103840e38fdbee42903a901794775d6e0f5dd2e728e5873","observation_id":"1c4802a7-b563-4996-b8b4-12d37db6f373","resolution":{"observed_at":"2026-08-07T12:07:27.833015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18762","last_updated":"2024-02-29T00:02:33Z","snapshot_observed_at":"2026-08-10T04:15:31.920517Z","submitted_at":"2024-02-29T00:02:33Z","title":"Disentangling the Causes of Plasticity Loss in Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18762","snapshot_observed_at":"2026-08-07T12:07:25.099245Z","title":"V ., Pas- canu, R., Martens, J., and Dabney, W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.099245Z"},"links":{"cited_paper":"/paper/2402.18762","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:b829c7578295ec584790eea08c32f648948c2a0c65cd6fcec7afecfa1855d824","observation_id":"d5a79819-a857-405e-9ff8-3747ae614d44","resolution":{"observed_at":"2026-08-07T12:07:25.099245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07418","last_updated":"2024-05-19T19:04:31Z","snapshot_observed_at":"2026-08-05T20:04:39.463011Z","submitted_at":"2023-10-11T12:05:34Z","title":"Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07418","snapshot_observed_at":"2026-08-07T12:07:25.165280Z","title":"Revisiting plasticity in visual reinforcement learning: Data, modules and training stages.arXiv preprint, arXiv:2310.07418,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.165280Z"},"links":{"cited_paper":"/paper/2310.07418","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:32f57aecb8ef85fe256b7e1dde9ce51817a656a6cab1981ca46d512fa5ba4aa1","observation_id":"fbe77174-476f-4664-a2db-db10933858d9","resolution":{"observed_at":"2026-08-07T12:07:25.165280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T12:07:25.392028Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.392028Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:510c759ac9f82f18a49b1f3431484d51e9b5aad807a159340d65b26652d25b1e","observation_id":"ed7c0b0f-544b-4b8a-bc29-ae11cb31f517","resolution":{"observed_at":"2026-08-07T12:07:25.392028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.00690","last_updated":"2018-01-02T15:48:14Z","snapshot_observed_at":"2026-08-01T20:24:08.300098Z","submitted_at":"2018-01-02T15:48:14Z","title":"DeepMind Control Suite","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.00690","snapshot_observed_at":"2026-08-07T12:07:25.491932Z","title":"P., and Riedmiller, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.491932Z"},"links":{"cited_paper":"/paper/1801.00690","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:bc740028fc49b7eab6d26401e88b9f0a7fc098769fb5e533dce95a5c412ac3fe","observation_id":"16b43741-5e71-4deb-b037-c7410b7eb4ad","resolution":{"observed_at":"2026-08-07T12:07:25.491932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.02648","last_updated":"2018-12-06T16:36:20Z","snapshot_observed_at":"2026-07-06T07:19:38.028327Z","submitted_at":"2018-12-06T16:36:20Z","title":"Deep Reinforcement Learning and the Deadly Triad","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.02648","snapshot_observed_at":"2026-08-07T12:07:25.603506Z","title":"Deep reinforcement learning and the deadly triad.arXiv preprint, arXiv:1812.02648,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.603506Z"},"links":{"cited_paper":"/paper/1812.02648","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:a73d86e4d6d424bb8b00e14405ffe416ec4a146055f1777f9641299843cef822","observation_id":"0b56d979-6340-4f05-80d1-27ccb30156dc","resolution":{"observed_at":"2026-08-07T12:07:25.603506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01364","last_updated":"2024-02-07T07:14:39Z","snapshot_observed_at":"2026-08-04T18:35:41.659861Z","submitted_at":"2024-02-02T12:34:09Z","title":"Continual Learning for Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01364","snapshot_observed_at":"2026-08-07T12:07:25.660754Z","title":"Continual learning for large language models: A survey","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.660754Z"},"links":{"cited_paper":"/paper/2402.01364","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:4ffb9349393d71cd1d183c6dc6a145d0039bedaffa07b8b37194b74233b31e45","observation_id":"456c8a55-bdc7-4a0e-aca0-dcc8c5803e59","resolution":{"observed_at":"2026-08-07T12:07:25.660754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.03176","last_updated":"2019-06-07T00:36:50Z","snapshot_observed_at":"2026-07-06T07:37:51.655095Z","submitted_at":"2019-03-07T20:34:36Z","title":"MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.03176","snapshot_observed_at":"2026-08-07T12:07:25.752483Z","title":"and Tian, T","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.752483Z"},"links":{"cited_paper":"/paper/1903.03176","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:f1eace638395aaee1441fc174cffcc0a709baacb6eecf1ddf7cbefeb37821859","observation_id":"4d8cfb7f-872f-499e-a4b3-67211ff87b2d","resolution":{"observed_at":"2026-08-07T12:07:25.752483Z","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":"2026-08-07T12:07:27.509776Z","title":"Experimental Details A.1","venue":null,"work_id":"47c6c669-567b-4022-92b5-f4ebb1194cea","year":2024},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.843649Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:c1bc4d5154ddfa9681fa78341bead7a9c0276c684ac6c416ac16c3380cbc0c61","observation_id":"a7bb5d2b-2af0-440f-9716-0454207ceab0","resolution":{"observed_at":"2026-08-07T12:07:27.584006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T12:07:27.304129Z","title":"Therefore, we useσ= 0.02for MountainCar-v0","venue":null,"work_id":"34acb7a5-4051-4448-93ea-8d31da9f44c2","year":2024},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.940013Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:73e23db5b0e7e021c707f48491210993b28d407e760cfcbe50795d728ab27434","observation_id":"9706fc3c-9988-4285-a5b3-3964fe89aa71","resolution":{"observed_at":"2026-08-07T12:07:27.419347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T12:07:27.115425Z","title":"The values of conventional hyperparameters are taken from the recommended values inCleanRL","venue":null,"work_id":"49f6affd-67fa-4e52-800d-089bd3aa34d4","year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.979456Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:12115c03a52f93f5f890144c8227abb5c3914836d926462484072961087c77a3","observation_id":"5ef0ff6e-5ef5-4654-a8e1-83b8d66dd2d6","resolution":{"observed_at":"2026-08-07T12:07:27.232688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T12:07:26.940182Z","title":"The values of conventional hyperpa- rameters are taken from the recommended values in (Young & Tian, 2019)","venue":null,"work_id":"9fcc49be-7c13-4b49-bbee-aa47d9cafaa3","year":2019},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:26.046554Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:2b2bc9b365de336e8767d0bce540bef31fad3c32432da8e054dedd96d062b3de","observation_id":"4f1d25d3-7542-4daa-9e50-6e8bc22be371","resolution":{"observed_at":"2026-08-07T12:07:27.029517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T12:07:26.791645Z","title":null,"venue":null,"work_id":"0a8c76f3-52de-42ff-b909-de19619a757e","year":2024},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:26.112993Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:3c20b2d6ac0ee0e859fc854e1aeb758fd391ca37b1e77bf3926a953797381240","observation_id":"9c52f6e0-8f13-4e70-9bf1-6672d71ced88","resolution":{"observed_at":"2026-08-07T12:07:26.880531Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T12:07:25.216383Z","title":"URL http://www.amazon.com/exec/obidos/ redirect?tag=citeulike07-20&path= ASIN/1449319793","venue":null,"work_id":null,"year":1928},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.216383Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:aa97bdcb3f978108fc1f758d60641cf0c2c88d36ddc0fecb4cd710209736ecfa","observation_id":"5a6f8cff-d641-494b-80b6-54bb3dc2d75a","resolution":{"observed_at":"2026-08-07T12:07:25.216383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:24.948826Z","title":"Kumar, A., Agarwal, R., Ghosh, D., and Levine, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.948826Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:8e4d1f089d52c0e361bfc8a2506156073fa980894ba99f8b835c135fcda06018","observation_id":"3d7a8ee3-9fff-4ca4-a75a-0cfaa8c849e6","resolution":{"observed_at":"2026-08-07T12:07:24.948826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.00730","last_updated":"2022-10-20T19:29:24Z","snapshot_observed_at":"2026-07-06T13:16:32.021229Z","submitted_at":"2022-06-01T19:44:18Z","title":"The Phenomenon of Policy Churn","version":3},"cited_work":{"arxiv_id":"2206.00730","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.00730","snapshot_observed_at":"2026-08-07T12:07:26.364941Z","title":"The Phenomenon of Policy Churn","venue":"cs.LG","work_id":"cd033d22-1532-4705-9a7e-7a034be92c5e","year":2022},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.364666Z"},"links":{"cited_paper":"/paper/2206.00730","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:7cea20c3334e5b0e3243704c9a01efd3ba4e062fe2207b102f65ef9b7d628f5a","observation_id":"253ee046-0a55-4926-aabd-4d8766f4e933","resolution":{"observed_at":"2026-08-07T12:07:26.446753Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00042","last_updated":"2023-10-14T11:58:06Z","snapshot_observed_at":"2026-07-06T11:14:31.306862Z","submitted_at":"2021-05-31T18:21:06Z","title":"A study on the plasticity of neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00042","snapshot_observed_at":"2026-08-07T12:07:24.494023Z","title":"L., Pascanu, R., and Clopath, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.494023Z"},"links":{"cited_paper":"/paper/2106.00042","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:b2e985da804a39532c202357a0876c1e28790e0a08c06e4f7dd2b059a0c9ab93","observation_id":"c49b059f-2ede-4824-b1ea-b1fc61245a3d","resolution":{"observed_at":"2026-08-07T12:07:24.494023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01540","last_updated":"2016-06-05T17:54:48Z","snapshot_observed_at":"2026-08-09T23:24:21.399948Z","submitted_at":"2016-06-05T17:54:48Z","title":"OpenAI Gym","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01540","snapshot_observed_at":"2026-08-07T12:07:24.549328Z","title":"Openai gym","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.549328Z"},"links":{"cited_paper":"/paper/1606.01540","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:61b1ab9b75afe243a5309c1596fdcea86b1cdd4bae1485a960927228344c8502","observation_id":"9bb40a89-7900-44ad-a453-fde6ae77c769","resolution":{"observed_at":"2026-08-07T12:07:24.549328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.04345","last_updated":"2025-06-26T16:08:44Z","snapshot_observed_at":"2026-07-06T15:51:58.017259Z","submitted_at":"2023-07-10T05:06:41Z","title":"Continual Learning as Computationally Constrained Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.04345","snapshot_observed_at":"2026-08-07T12:07:25.020778Z","title":"J., Liu, Y ., and Van Roy, B","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:25.020778Z"},"links":{"cited_paper":"/paper/2307.04345","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:8cec5857f5dd86ac460570518099515b60fd31952fe4f0b5b56b58aedc19d99b","observation_id":"f76e2d38-6922-4e82-b427-0bb18df945f4","resolution":{"observed_at":"2026-08-07T12:07:25.020778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.08894","last_updated":"2019-03-21T09:42:41Z","snapshot_observed_at":"2026-08-04T21:37:15.275989Z","submitted_at":"2019-03-21T09:42:41Z","title":"Towards Characterizing Divergence in Deep Q-Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.08894","snapshot_observed_at":"2026-08-07T12:07:24.428368Z","title":"Towards charac- terizing divergence in deep q-learning.arXiv preprint, arXiv:1903.08894,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.428368Z"},"links":{"cited_paper":"/paper/1903.08894","citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:945dd371815f841da1532cb468e4f22c20ec7462917fccd7e429d35f3929fafa","observation_id":"97b49dac-aa77-4af6-b424-75d914f9fa9e","resolution":{"observed_at":"2026-08-07T12:07:24.428368Z","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":"2026-08-07T12:07:28.106512Z","title":"Lever- aging procedural generation to benchmark reinforcement learning","venue":null,"work_id":"c656f36b-cedc-4804-8490-38ff659033b9","year":null},"citing_paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T12:07:24.600466Z"},"links":{"citing_paper":"/paper/2506.00592"},"observation_digest":"sha256:79086395a21799f9d8755540468657cd4a65a50d25c39389dc22d9639d8b54f6","observation_id":"85313622-e286-4119-bcbd-195fa7782460","resolution":{"observed_at":"2026-08-07T12:07:28.195071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.00592","last_updated":"2025-05-31T14:58:22Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T04:26:18.189752Z","submitted_at":"2025-05-31T14:58:22Z","title":"Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":6},"total_outbound_references":26},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 3 inbound Pith citation observations for arXiv:2506.00592."}