{"as_of":"2026-08-15T12:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c941265b522c18c40b8c203be72cb0edac7997df3a4fd46c5a562c2256feefee","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":"2026-08-10T18:59:40.020356Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2501.10861/citation-record","integrity":"/paper/2501.10861/integrity","json":"/paper/2501.10861/citation-record.json","paper":"/paper/2501.10861"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T18:59:40.727384Z","title":"Learning in nonsta- tionary environments: A survey,","venue":null,"work_id":"b8e5eec2-8c19-41a6-af59-5a4a7cf088df","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.794729Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:6f1b2fdb2276876fe6b3e3aae6e93d2cc51884e45374bb81fb004145e99faf0a","observation_id":"60443b66-da0f-4b18-b658-e716560faca9","resolution":{"observed_at":"2026-08-10T18:59:40.745503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.677802Z","title":"Overcoming catastrophic forgetting in neural networks,","venue":null,"work_id":"b00e88e9-a5b1-437e-943d-6d8002517199","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.799319Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:184a4ec7fae9bb96c6413fa26b0b098bcc5a383f6ad5e8e38a018577e956f73f","observation_id":"20ffe475-991b-4065-99a1-43c7b46792af","resolution":{"observed_at":"2026-08-10T18:59:40.697769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.645572Z","title":"Continual lifelong learning with neural networks: A review,","venue":null,"work_id":"f4b8141b-38d9-4607-82cb-8533257d6997","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.806624Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:350a273a65b9e206ba437675444fa0ac87c2a007a98b00dac862af0d8ee78db7","observation_id":"f52c0fd6-f2e8-4ed2-b161-1f539240418a","resolution":{"observed_at":"2026-08-10T18:59:40.666875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.613660Z","title":"Catastrophic interference in connec- tionist networks: The sequential learning problem,","venue":null,"work_id":"9ad79253-b289-4a32-b486-367e61bfd405","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.832542Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:1016ac46e0e14c8e10244ccc541cb00415570399229354a3f20db8edab71016a","observation_id":"cb6eee71-9f9c-4856-82c2-5abc9cb30289","resolution":{"observed_at":"2026-08-10T18:59:40.619195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.600561Z","title":"Catastrophic forgetting in connectionist networks,","venue":null,"work_id":"4a42f687-21f3-4cbd-aac8-183c5ab2036d","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.842013Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:281c2449e8d5edbdbfe44c074fce699e496b88a3c07c91886fe5225f60123a9a","observation_id":"751102ad-9ba0-45e0-a69a-0aca491cb9f5","resolution":{"observed_at":"2026-08-10T18:59:40.605134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.573459Z","title":"Continual learning through synaptic intelligence","venue":null,"work_id":"807d6d93-58ac-42b5-aa67-229ccc443137","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.850567Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:deb9871280ad8bb3f1b8db88130f37cff119ef39f066cc0061c1c03ad5f2d852","observation_id":"1821a2d0-109e-47c6-b7c7-6a4e9db26217","resolution":{"observed_at":"2026-08-10T18:59:40.582995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.554637Z","title":"Memory aware synapses: Learning what (not) to forget","venue":null,"work_id":"42d0100f-3659-43da-9954-2f855d6c1e68","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.864627Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:136bd7060bfb79cbf3550f71b0870d0fa971a4d2a73d38fb853f473a538ebf16","observation_id":"c4393439-51a7-4492-b688-fbe81eebe03a","resolution":{"observed_at":"2026-08-10T18:59:40.560033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.534519Z","title":"Overcoming catas- trophic forgetting with hard attention to the task","venue":null,"work_id":"0e672e86-af21-4680-ba95-cf02a0199957","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.884550Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:cb6a78045eb6a629322808a75504aebe65d37118f17d0a728367b336a1eef428","observation_id":"5543d8b7-489d-4302-be8a-87e7b33a0ca2","resolution":{"observed_at":"2026-08-10T18:59:40.544035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.01547","last_updated":"2018-06-11T09:03:44Z","snapshot_observed_at":"2026-08-14T20:43:10.027743Z","submitted_at":"2017-08-04T15:14:31Z","title":"Lifelong Learning with Dynamically Expandable Networks","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.01547","snapshot_observed_at":"2026-08-10T18:59:39.897485Z","title":"Lifelong Learning with Dynamically Expandable Networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.897485Z"},"links":{"cited_paper":"/paper/1708.01547","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:6f2e9180695649f6871eade054066da76d213170521bbf17324a475026391fbe","observation_id":"0adc2153-440e-42e4-8814-d8b982f7ddd8","resolution":{"observed_at":"2026-08-10T18:59:39.897485Z","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":"2026-08-14T06:05:49.699878Z","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":"2026-08-10T18:59:39.903894Z","title":"Progressive Neural Networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.903894Z"},"links":{"cited_paper":"/paper/1606.04671","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:954045b8238159af46d50c8324e0c2b473d28ed844095c6c97fda1d064dc8d01","observation_id":"7acfe8d5-77f9-4d0e-af69-f426ff292e92","resolution":{"observed_at":"2026-08-10T18:59:39.903894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.07725","last_updated":"2017-04-14T16:41:02Z","snapshot_observed_at":"2026-08-14T21:29:01.299545Z","submitted_at":"2016-11-23T10:24:11Z","title":"iCaRL: Incremental Classifier and Representation Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.07725","snapshot_observed_at":"2026-08-10T18:59:39.911736Z","title":"iCaRL: Incremental Classifier and Representation Learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.911736Z"},"links":{"cited_paper":"/paper/1611.07725","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:17853e7124221e5ffe88a1dbac6624ce24912b89858f8c89e40aa84afdaed608","observation_id":"0a89e8b7-0eae-4855-a716-7de9d789387b","resolution":{"observed_at":"2026-08-10T18:59:39.911736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01145","last_updated":"2023-10-03T11:10:08Z","snapshot_observed_at":"2026-08-13T15:11:55.001126Z","submitted_at":"2022-07-04T00:09:33Z","title":"Memory Population in Continual Learning via Outlier Elimination","version":3},"cited_work":{"arxiv_id":"2207.01145","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.01145","snapshot_observed_at":"2026-08-10T18:59:40.221280Z","title":"Memory Population in Continual Learning via Outlier Elimination","venue":"cs.LG","work_id":"525d3b80-6082-4dfb-923d-dd15083726c5","year":2022},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.917139Z"},"links":{"cited_paper":"/paper/2207.01145","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:60fa59e9524ba46af659eb131a9e6cb2d9108b369f4eea5866eaac5c037ce072","observation_id":"fc86b6a6-7a0a-4880-a0cb-dfed71c17970","resolution":{"observed_at":"2026-08-10T18:59:40.230865Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.499108Z","title":"IL2M: Class Incremental Learning With Dual Memory,","venue":null,"work_id":"1b630c5c-2307-490d-8bca-90c728ab460a","year":2019},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.923681Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:304352f1b540f573ff817e0c356b950b9f71cebbb5082c7c407abc3085f6e91b","observation_id":"f73d44ad-7645-43eb-b848-b8b3f526bb37","resolution":{"observed_at":"2026-08-10T18:59:40.518953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10563","last_updated":"2018-02-23T20:32:27Z","snapshot_observed_at":"2026-08-14T20:09:09.461532Z","submitted_at":"2017-11-28T21:26:15Z","title":"FearNet: Brain-Inspired Model for Incremental Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10563","snapshot_observed_at":"2026-08-10T18:59:39.928087Z","title":"FearNet: Brain-Inspired Model for Incre- mental Learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.928087Z"},"links":{"cited_paper":"/paper/1711.10563","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:4326a2974c4e4d37994c7748aa17c77f285575be14099ebd9d2dc9bce6ac836d","observation_id":"97c94511-f623-4123-8520-e6f584b05e4d","resolution":{"observed_at":"2026-08-10T18:59:39.928087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.00420","last_updated":"2019-01-09T11:11:47Z","snapshot_observed_at":"2026-08-15T06:05:04.839393Z","submitted_at":"2018-12-02T16:39:19Z","title":"Efficient Lifelong Learning with A-GEM","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.00420","snapshot_observed_at":"2026-08-10T18:59:39.937968Z","title":"Efficient Lifelong Learning with A-GEM,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.937968Z"},"links":{"cited_paper":"/paper/1812.00420","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:3c2db9d324560dfe0cec764617295e9e02e47207db93ca1786ad55a958a9f6d3","observation_id":"d885c3dd-cf0c-40c0-a549-d7d448edc263","resolution":{"observed_at":"2026-08-10T18:59:39.937968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.08840","last_updated":"2022-09-13T14:47:52Z","snapshot_observed_at":"2026-08-14T20:51:36.916558Z","submitted_at":"2017-06-26T14:53:34Z","title":"Gradient Episodic Memory for Continual Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.08840","snapshot_observed_at":"2026-08-10T18:59:39.942030Z","title":"Gradient Episodic Memory for Contin- ual Learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.942030Z"},"links":{"cited_paper":"/paper/1706.08840","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:fbc12a816a9123d58ba354c94a809387398a62b078cf513c601bd06511e34897","observation_id":"7e29fb89-877a-43d5-8d9d-976b06a28f74","resolution":{"observed_at":"2026-08-10T18:59:39.942030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.08690","last_updated":"2017-12-12T02:14:21Z","snapshot_observed_at":"2026-08-14T20:58:31.577053Z","submitted_at":"2017-05-24T10:37:38Z","title":"Continual Learning with Deep Generative Replay","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.08690","snapshot_observed_at":"2026-08-10T18:59:39.951961Z","title":"Continual Learning with Deep Generative Replay,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.951961Z"},"links":{"cited_paper":"/paper/1705.08690","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:57ddc2be3de7b7df48cd37af69c34b6b197a78ef9ecdba3181ba0c727316374f","observation_id":"0fc52e5f-5ed0-4137-99b5-36f0da311ae1","resolution":{"observed_at":"2026-08-10T18:59:39.951961Z","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":"2026-08-10T18:59:40.476188Z","title":"PremiUm-CNN: Propagating uncertainty towards ro- bust convolutional neural networks,","venue":null,"work_id":"c0cca74b-8e38-4efc-8834-7c293f6e171d","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.958730Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:a5a8fefd39654f24b818200bfabf78a212b8e44c017f1325f58fe975b4058180","observation_id":"a344e6a6-2c9b-4348-9e0d-9e48035c3da9","resolution":{"observed_at":"2026-08-10T18:59:40.481784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.455494Z","title":"Auto-encoding variational bayes","venue":null,"work_id":"a5fcdad1-8663-4a84-bc8e-d2bef7823588","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.962827Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:b75b22925041b2be012561de02a564d165ccc51efaa7264649c34f60b9668d3b","observation_id":"58c47387-690e-4ce0-89d6-4c4dbb6cfb7c","resolution":{"observed_at":"2026-08-10T18:59:40.466963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.419289Z","title":"Weight uncertainty in neural network,","venue":null,"work_id":"dd53a3a0-b2c9-452a-9156-8ff2fcc0304e","year":1938},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.970987Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:affb5be1b8bb05fd97512ad658c8723d9fe3a8bfeb3497bf8a0dbeb44e247cd2","observation_id":"36a4a333-9ccf-4893-a2b9-3e255a781c17","resolution":{"observed_at":"2026-08-10T18:59:40.424918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.399443Z","title":"Keeping the neural networks simple by minimizing the description length of the weights,","venue":null,"work_id":"3160183f-b0f2-47fc-ad3a-6e1ff6d95c01","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.980626Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:3710112f222d4289cd98b98846778a47510809c6ac701e7c139cf8577ae8e261","observation_id":"b68929c2-457c-4df7-946d-931bdb2c1989","resolution":{"observed_at":"2026-08-10T18:59:40.407689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.15078","last_updated":"2020-06-26T16:15:49Z","snapshot_observed_at":"2026-08-07T14:34:51.547816Z","submitted_at":"2020-06-26T16:15:49Z","title":"Continual Learning from the Perspective of Compression","version":1},"cited_work":{"arxiv_id":"2006.15078","doi":"10.48550/arxiv.2006.15078","metadata_source":"pith","pith_arxiv_id":"2006.15078","snapshot_observed_at":"2026-08-11T00:16:15.729268Z","title":"Continual Learning from the Perspective of Compression","venue":"cs.LG","work_id":"3c2f7ba2-949c-43f7-a97d-a6342ae5e349","year":2020},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:39.995426Z"},"links":{"cited_paper":"/paper/2006.15078","citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:801b6bd675527fbcd437bb9e2af76d4c9a34515132ede8539c334cc9bad57f58","observation_id":"1e954ec6-8325-4cb2-a8af-a2c325ffd3c3","resolution":{"observed_at":"2026-08-10T18:59:40.061320Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.376220Z","title":"Three types of incremental learning,","venue":null,"work_id":"8ab4208e-a4b0-44ca-893f-43bc25bf5488","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:40.003833Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:fc28a05247d79db78d9dee6e34fe772e447a7da87bf421a8f68e3497544869ab","observation_id":"e2d49bd3-6d2b-4840-8106-c9edc356c5ce","resolution":{"observed_at":"2026-08-10T18:59:40.381418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.356510Z","title":"Uncertainty- guided continual learning with bayesian neural networks","venue":null,"work_id":"db8d8928-bcb0-419c-9be5-1888a44ced05","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:40.009747Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:884d5bbb87e4a49062a5b28329d775cef828f6d522c81e086c98b707434839ef","observation_id":"b8099587-056b-451c-bc9f-9e46a47a1feb","resolution":{"observed_at":"2026-08-10T18:59:40.362697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.339190Z","title":"Uncertainty-based continual learning with adaptive regularization,","venue":null,"work_id":"b5395672-38bc-4f75-85c1-9155995c77bc","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:40.014332Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:8fbeadde2a2b101343cffe68ceabe80f5bd39e7de516a5bff5a7b3993c371f79","observation_id":"a66185a5-ce50-42ed-9d9e-45427a0d1e49","resolution":{"observed_at":"2026-08-10T18:59:40.345667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-10T18:59:40.321682Z","title":"Variational continual learning","venue":null,"work_id":"3cebb36c-2edc-442e-a3f9-1b8a8dd34b69","year":null},"citing_paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T18:59:40.020356Z"},"links":{"citing_paper":"/paper/2501.10861"},"observation_digest":"sha256:556c8c6637d8ed85f5e81bb9dd4ac7e5b143c7fcecd4b0106427dbe1ee02d14a","observation_id":"229eca34-ee81-4a1b-9a66-4c3d3c39d33e","resolution":{"observed_at":"2026-08-10T18:59:40.326103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.10861","last_updated":"2025-01-18T19:58:53Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T01:45:17.466383Z","submitted_at":"2025-01-18T19:58:53Z","title":"Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":17},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2501.10861."}