{"as_of":"2026-08-11T21:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1a8388a630b8f31b4fc2c5392632506c950bc7e84e808620c849b9acffed1fe5","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:40:37.317137Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2412.16409/citation-record","integrity":"/paper/2412.16409/integrity","json":"/paper/2412.16409/citation-record.json","paper":"/paper/2412.16409"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:40:38.364614Z","title":"Continual evidential deep learning for out- of-distribution detection","venue":null,"work_id":"ec604061-5abd-42bc-976e-da06fd9f52fd","year":2023},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.014551Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:6e0bef205e5f0b8f4af20847f245aee28391a5dac4ab1b95a98bd9853774f1e5","observation_id":"952f19cc-6fc6-4b30-b5e6-cf2c878c15ba","resolution":{"observed_at":"2026-08-11T10:40:38.369881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.348037Z","title":"Ahuja, Ibrahima J","venue":null,"work_id":"b55d7558-b1e7-42b2-a470-068b39e12fe2","year":2019},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.023989Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:6624e8e92587436e314ab8dc3e1c3374f51fd6a6d9aec9ea3f15ee148cfcfccb","observation_id":"607b5e8d-1bec-4325-9000-c34e8f241a4c","resolution":{"observed_at":"2026-08-11T10:40:38.353189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-11T10:40:37.031762Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.031762Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:3eb4fb725d2ada4f7d02711339cab175eda1aaf09bb2fa8f4530136434461a44","observation_id":"6c40e523-a18a-440c-ae01-b3277a074ba9","resolution":{"observed_at":"2026-08-11T10:40:37.031762Z","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":"2026-08-11T10:40:38.331338Z","title":"Continual novelty detection","venue":null,"work_id":"9c96ea7a-da6b-460f-839a-06af353d68e3","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.038298Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:6c4023a6cb5128dd05f14d6ad09191722a13dff4928d04852369ce440db2b08b","observation_id":"232f911e-d8b1-42cb-9251-25d678972c16","resolution":{"observed_at":"2026-08-11T10:40:38.336606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.312549Z","title":"Few-shot continual active learning by a robot","venue":null,"work_id":"e2188f09-b167-46a0-b395-73c9e17aa3cc","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.044265Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:c21bf8a5b3ea55a75f884379cde130b0bf9290107d6a93f96c8c7de0e1655e37","observation_id":"3eda32ed-21b1-45c6-a1ee-2508a1d40097","resolution":{"observed_at":"2026-08-11T10:40:38.318870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.290901Z","title":"Few-shot continual active learning by a robot","venue":null,"work_id":"40e26f58-14c2-4571-bebf-b14e1fc633b8","year":2024},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.050398Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:bd43b230d609e02b18d0c97c564ce36dcb173d4c41f16e10cfcec8f02ddeca76","observation_id":"ea390997-100a-4443-99e1-db96a52fe1a1","resolution":{"observed_at":"2026-08-11T10:40:38.296877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.14307","last_updated":"2022-08-30T14:44:41Z","snapshot_observed_at":"2026-08-11T01:50:09.951544Z","submitted_at":"2022-08-30T14:44:41Z","title":"Beyond Supervised Continual Learning: a Review","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.14307","snapshot_observed_at":"2026-08-11T10:40:37.056930Z","title":"Beyond supervised continual learning: a review","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.056930Z"},"links":{"cited_paper":"/paper/2208.14307","citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:356c4591ae0d431930413420a51ae8996a08b4cb04891ff576b869d936a77795","observation_id":"be942391-09ef-4132-ba58-e65517d3a20c","resolution":{"observed_at":"2026-08-11T10:40:37.056930Z","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":"2026-08-11T10:40:38.272440Z","title":"Mixmatch: A holistic approach to semi-supervised learning","venue":null,"work_id":"fa171b14-9d0d-4afc-b47a-303dd655c196","year":2019},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.062537Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:4ea37582d07ea7d8df6397295d6e400d13d90235718ef4fa19cf0317f8ac352b","observation_id":"7ce55093-4d45-4567-a205-f453bfa8dafe","resolution":{"observed_at":"2026-08-11T10:40:38.278494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.253850Z","title":"Continual semi-supervised learning through contrastive interpolation consistency","venue":null,"work_id":"148721fc-1c45-4be8-ae33-ac238305f812","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.067598Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:ccf2b91117cf6307bd4fb3111ea9f5f3f4ed448fd79e95d8e18004fe0fdb5b4a","observation_id":"11497a44-f370-4b2e-b2a3-7a26d6a86d73","resolution":{"observed_at":"2026-08-11T10:40:38.259110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.234912Z","title":"Continual semi-supervised learning through contrastive interpolation consistency","venue":null,"work_id":"1450c058-583c-46bb-940e-e6d2d4b88324","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.072497Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:2186fabf40e126ed051448f7a1282867116fd47f883805882c9d246284fcdc73","observation_id":"05e6c474-920b-4dd5-b4b5-16088c9fc40d","resolution":{"observed_at":"2026-08-11T10:40:38.241571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.213453Z","title":"Incorporating diversity in active learning with support vector machines","venue":null,"work_id":"610812b9-751d-44e1-a14f-e0ce8f0dd153","year":2003},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.078137Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:6b8a3ea2e68450d6e814bef616dd7a250130da579e06ada1d3aaad3af7f6c903","observation_id":"a7f09976-0548-42a4-b4e9-f55ec1bdb4d8","resolution":{"observed_at":"2026-08-11T10:40:38.220085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.191331Z","title":"Rethinking experience replay: a bag of tricks for continual learning","venue":null,"work_id":"9b763c0e-c075-4c63-9a46-e5ee19b56228","year":2021},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.083270Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:90d0daf53fbf6a5191da9f1991785925c33157af4181074186a36a2d02eaa3e3","observation_id":"7aa716e9-409a-4cc0-ae86-2e7c767741fe","resolution":{"observed_at":"2026-08-11T10:40:38.198638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.173397Z","title":"Unsupervised learning of visual features by contrasting cluster assignments","venue":null,"work_id":"22a749a2-ed97-4b1e-87bd-28ac966c54b9","year":2020},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.088495Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:c42d3e914ed8b1c0591c276bb349a2fcc77004b79f17a369654d1144bb92d258","observation_id":"2e6ef21b-fcf8-4c7f-b667-c5c09137793e","resolution":{"observed_at":"2026-08-11T10:40:38.178787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.093377Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.093377Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:0c61ecc5eb5d3d70aa6b437333755af38172c98bc03a7a157ecb744912ccc498","observation_id":"8f27530b-3f67-4e9f-8cdc-8757bbf55a24","resolution":{"observed_at":"2026-08-11T10:40:37.093377Z","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":"2026-08-11T10:40:38.133642Z","title":"Superposition of many mod- els into one","venue":null,"work_id":"5c99a969-caa9-4a1a-a01f-2a90d583d683","year":2019},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.098581Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:31947b779138dc6c60b20ef02ae32c49e676e1d312cf7c43495142b3afc103b2","observation_id":"6ab6111d-a430-4c09-a390-eaaca173eeac","resolution":{"observed_at":"2026-08-11T10:40:38.141619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.111718Z","title":"Breaking the closed world assump- tion in text classification","venue":null,"work_id":"a3db9200-95e1-4f0e-a808-7814f4a234d9","year":2016},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.103999Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:a639d46ab3c6725b5d06a488ad7f053fbfcd4c428914b44f2dea80a8ec8ae725","observation_id":"ebbc9bdd-e9ea-4d47-967e-ce99cd6b400b","resolution":{"observed_at":"2026-08-11T10:40:38.118366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.088119Z","title":"Catastrophic forgetting in connectionist networks","venue":null,"work_id":"f6cec539-04e4-499b-aaf4-aa67b40dabd3","year":1999},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.110368Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:ed4cb7def36564b2300236308a9bb4a0ea2d91eb66eb16c415396a3837b295e0","observation_id":"cb95c65f-72a3-4fba-9e89-5fafeee2c664","resolution":{"observed_at":"2026-08-11T10:40:38.096383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.068313Z","title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","venue":null,"work_id":"d9282d92-00a8-4d43-8cc6-6b04fce58127","year":2016},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.115620Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:4875fe58add5246a0cf8da021483d32c61bfa965594abf0e20d1a6e9853a7ea5","observation_id":"b3579f1b-4f96-4a53-a2a9-9520e618080b","resolution":{"observed_at":"2026-08-11T10:40:38.073862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.046332Z","title":"Deep bayesian active learning with image data","venue":null,"work_id":"f8d97c4e-15e0-4f9f-a502-440511ac6d66","year":null},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.121244Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:38eeea08a6848e4ea7053abe42f7f72ff764ddd2ec27b722a3ecbd51c4ce8abd","observation_id":"067749bf-cf5c-4430-8dd9-10493c9bff45","resolution":{"observed_at":"2026-08-11T10:40:38.053597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:38.027034Z","title":"Learning to discover novel visual categories via deep transfer cluster- ing","venue":null,"work_id":"98c5b86a-37bc-4c7a-89fc-d5c14e41aa08","year":2019},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.126860Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:882b8f1c3060cac75982c2ad57003f1e3410a276fccc7c2c5b6be0c622db3512","observation_id":"af7c5339-8494-4144-b236-5aa8c770444c","resolution":{"observed_at":"2026-08-11T10:40:38.032831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.132361Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.132361Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:e76ee03de3ead06ea732b510dfa0c6251ab89e661c67d8f6f032728a723c0df9","observation_id":"2bef2bd5-24d9-46e2-b244-ed1287970113","resolution":{"observed_at":"2026-08-11T10:40:37.132361Z","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":"2026-08-11T10:40:37.990720Z","title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019","venue":null,"work_id":"69d595d1-b290-4994-a589-8ad470ba0801","year":2019},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.137727Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:ecac2596b0e093d12e31033abbd0db105bcf6ef86539426ec3b2289635053120","observation_id":"d49091be-bb15-4622-b5e6-efd3b2b25463","resolution":{"observed_at":"2026-08-11T10:40:37.996609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.972556Z","title":"A baseline for detect- ing misclassified and out-of-distribution examples in neural networks","venue":null,"work_id":"ee41af20-6c9a-4cf6-83ca-67e69c010bb2","year":2017},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.142955Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:59cf7273644ca402f0a14ebacdeb6102baa9fa0421e8e755ff54753a16d53049","observation_id":"6fb8067d-7fcf-4073-b327-bd1d90bc22a5","resolution":{"observed_at":"2026-08-11T10:40:37.978464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.953445Z","title":"The inaturalist species classification and de- tection dataset, 2018","venue":null,"work_id":"73501315-851d-4946-bd30-80b9dfee52ac","year":2018},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.147759Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:689d4efa4b8b857206cb104ddf466257af7bc648b2bee9b02b94e87db1530b60","observation_id":"1147a8cb-7a18-4916-9492-1c1d32d0333a","resolution":{"observed_at":"2026-08-11T10:40:37.958920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.935429Z","title":"A soft nearest-neighbor framework for con- tinual semi-supervised learning","venue":null,"work_id":"5f7b5e1a-ba45-4767-bd11-9e2397b851f1","year":2023},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.152825Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:a26fde82f559039dbf23a2f84368fb22f58966912fe1f5c48a134510f05e83a2","observation_id":"e4538f1a-60f9-417b-9d33-c750b5b9dcf3","resolution":{"observed_at":"2026-08-11T10:40:37.941422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.158076Z","title":"Overcoming catastrophic forgetting in neu- ral networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.158076Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:d8733664a6e73b065634ff31cefbcbc168b3c7cc96d1c6c1497c02edb8479604","observation_id":"7f08388c-e1c0-439b-b5f3-9dea1b58617d","resolution":{"observed_at":"2026-08-11T10:40:37.158076Z","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":"2026-08-11T10:40:37.901794Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"390ce07a-6e5a-4a7a-9b7b-e393e64e4f01","year":2012},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.163190Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:8df3a690c625f67384478e4510617d16a1f2a6c10f8101425e88c30372d03358","observation_id":"727b887e-afd9-4d0e-8fbe-54d60ab478df","resolution":{"observed_at":"2026-08-11T10:40:37.907739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.881676Z","title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","venue":null,"work_id":"9d8d4b14-4dfc-440e-9542-ea2fb99bbed2","year":2018},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.168078Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:d75581e93d510427ba886981d94fdc94a38990c94ae6868ebef4d6014ba753dc","observation_id":"a89dacae-7763-4a0a-8de0-f8be44ed10fe","resolution":{"observed_at":"2026-08-11T10:40:37.887676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.865003Z","title":"Lewis and William A","venue":null,"work_id":"0cc1fd53-c456-483e-8180-471ea7e5d07f","year":1994},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.173357Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:c498282e86fd69a10e060046c9c9f7afa38fa1e95214f60bb9cdb075d4779812","observation_id":"bda8fd2e-09cf-4dec-a459-8bda32819d4d","resolution":{"observed_at":"2026-08-11T10:40:37.869908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.848409Z","title":"Enhancing the re- liability of out-of-distribution image detection in neural net- works","venue":null,"work_id":"b4343139-fea5-403e-9363-060514a164a7","year":2018},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.178508Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:ede2cb3091e05c48e6639a88003728d84165c977b81ec327d6d739fd5657b517","observation_id":"986e406d-a970-4554-ab45-3507c53f671c","resolution":{"observed_at":"2026-08-11T10:40:37.853901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.830084Z","title":"Active generalized category discovery","venue":null,"work_id":"56f39b9a-aa1e-4649-a1ff-50f1fc205ea1","year":2024},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.183460Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:4375019abc62b84b86fe80c26449bcd30da72439de025ec8bc6294cd15924f6b","observation_id":"a1e4674c-9831-433f-b4ce-7b991dc68fed","resolution":{"observed_at":"2026-08-11T10:40:37.837048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.04250","last_updated":"2020-12-08T07:07:11Z","snapshot_observed_at":"2026-08-11T06:44:39.556315Z","submitted_at":"2020-12-08T07:07:11Z","title":"Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.04250","snapshot_observed_at":"2026-08-11T10:40:37.189175Z","title":"Out-of-distribution detection with subspace techniques and probabilistic modeling of features","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.189175Z"},"links":{"cited_paper":"/paper/2012.04250","citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:f7fd1d839acd43a548278810d658181f95563d63cc0482817d225ca5137079e5","observation_id":"bfc4e0bb-45bd-40d1-9b7d-c398ef4fc279","resolution":{"observed_at":"2026-08-11T10:40:37.189175Z","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":"2026-08-11T10:40:37.810961Z","title":"Sub- space modeling for fast out-of-distribution and anomaly de- tection","venue":null,"work_id":"b92f62e6-e77c-4238-8c1a-e91c8fccd28a","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.195044Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:e950fa5cd80e680c9b3cfaede1ec01beaad76acb76782b248ea3f38eb0bde2e4","observation_id":"bbbb46a6-ba7d-41d4-82b8-b31d1bce4e8e","resolution":{"observed_at":"2026-08-11T10:40:37.816200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.200604Z","title":"Continual lifelong learning with neural networks: A review","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.200604Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:8ead56e1555ade2d4d6c0ce49847acaae33c4e30aefa53e754a6b9681ce3598d","observation_id":"4f871aa9-a570-49d2-9415-be8b5e868f6f","resolution":{"observed_at":"2026-08-11T10:40:37.200604Z","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":"2026-08-11T10:40:37.782302Z","title":"Open-world machine learning: appli- cations, challenges, and opportunities","venue":null,"work_id":"d0706f66-58e7-45fb-9f10-5166ed5d778e","year":2023},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.207361Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:5f5501cfdb08ce0c76f1171fc9c7ba5eaed44120de783904418c1a6a3b9ef035","observation_id":"60f2d810-52e8-4416-83bb-231dbcd54356","resolution":{"observed_at":"2026-08-11T10:40:37.787650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.765426Z","title":"icarl: Incremental classifier and representation learning","venue":null,"work_id":"23862843-2dce-4268-8ba5-66dacbd69732","year":2001},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.215255Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:ca78d594581724dcdfa8084d10a3732d5e5cc6a6afe6dd34624a5f62de65476f","observation_id":"22466378-0363-415f-b1e4-29603142a891","resolution":{"observed_at":"2026-08-11T10:40:37.770849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.748494Z","title":"Likelihood ratios for out-of-distribution detec- tion","venue":null,"work_id":"99d50ab0-ed07-4e19-a539-bc474b401280","year":2019},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.222220Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:beae7030c2ed1bd3460922ec07dc15f5f09983333750167d03dac1ee9890bf74","observation_id":"f03eece0-78c9-4ae3-ba07-ec53349a2bcc","resolution":{"observed_at":"2026-08-11T10:40:37.753773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.730159Z","title":"Gupta, Xiaojiang Chen, and Xin Wang","venue":null,"work_id":"c0771f74-4d71-410f-a0c6-e7e858e29c8a","year":2021},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.228358Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:8028d92c00855770eabe169e9f595ba125f54ac65e34502564c9b804ed6c8dbc","observation_id":"7f899090-7e70-46f4-8282-145761399a30","resolution":{"observed_at":"2026-08-11T10:40:37.736222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.234064Z","title":"Imagenet-21k pretraining for the masses,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.234064Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:fa0fd2ee1386a684b3ed5cba293bbccff2cbf0d9e422d5949aaac4c0884064ca","observation_id":"7cdd0180-ce2e-4e46-84e4-5b70b81321f7","resolution":{"observed_at":"2026-08-11T10:40:37.234064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.01146","last_updated":"2020-09-28T21:44:55Z","snapshot_observed_at":"2026-07-06T07:12:23.549121Z","submitted_at":"2018-11-03T02:40:01Z","title":"Closed-Loop Memory GAN for Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.01146","snapshot_observed_at":"2026-08-11T10:40:37.240092Z","title":"Closed-loop memory gan for continual learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.240092Z"},"links":{"cited_paper":"/paper/1811.01146","citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:e72a64c8ad3bf392cbd63a8c5a573f794180d4353a68a0097abf4b31dd5f8420","observation_id":"2558926c-6830-4820-aad7-98ef89da1083","resolution":{"observed_at":"2026-08-11T10:40:37.240092Z","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":"2026-08-11T10:40:37.699384Z","title":"Lifelong learning without a task oracle","venue":null,"work_id":"041ae6e5-4496-41bc-94c4-a12e811fcd49","year":2020},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.245328Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:b2e180790d2018385ab3e12d8469861c6face8b2e10111fd4b6cc51d7417984b","observation_id":"583c0e2c-1456-4f73-90cc-885e5ccb7feb","resolution":{"observed_at":"2026-08-11T10:40:37.705021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.682753Z","title":"incdfm: Incremental deep feature modeling for continual novelty detection","venue":null,"work_id":"26283735-f644-4cd3-94ce-dc2f0d6b4bbd","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.250461Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:fa11dd986bc3cc8d0a6d5d65b7519a02ac6ce631d01548b55c810c30831bafa8","observation_id":"833e96f8-0706-4b5d-bd81-9be0dacf8323","resolution":{"observed_at":"2026-08-11T10:40:37.688004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.666075Z","title":"Experience replay for continual learning","venue":null,"work_id":"e895c099-514b-40a3-aa4d-a339e3b2b3bc","year":2019},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.256169Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:913eac5d753e4a5b6a0bf9c003ef22c2fcc14c492f874d0b0e448d6c7ef53975","observation_id":"7a7402a4-b0f0-4114-b1d5-bf8965f3de00","resolution":{"observed_at":"2026-08-11T10:40:37.671446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.262025Z","title":"Active learning for convolu- tional neural networks: A core-set approach","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.262025Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:b8f1865116de59552767097dc90229c4f53d8a1214939e094c96043ad3a9acd5","observation_id":"81eb4155-b9f8-409e-938b-e2e9499e7485","resolution":{"observed_at":"2026-08-11T10:40:37.262025Z","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":"2026-08-11T10:40:37.633107Z","title":"Active learning literature survey","venue":null,"work_id":"78d15607-a423-42f2-b780-13ac4df71898","year":2009},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.270028Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:3daa398822083987faeeba20f3b4bb5cd08979526c610a999ce10e716f30da89","observation_id":"c815abaa-5903-40f5-b0c9-1d00175cacdc","resolution":{"observed_at":"2026-08-11T10:40:37.638649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.612766Z","title":"Doc: Deep open classifica- tion of text documents","venue":null,"work_id":"12a5ab5d-7ec7-462f-8e20-2a6534a5961c","year":2017},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.275409Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:f0621effd83fc89d308be41b123631303584e7fd797910c5fea2c75d88ccdf3e","observation_id":"3f308fa2-251a-4245-9c9d-30094e8f67bc","resolution":{"observed_at":"2026-08-11T10:40:37.619393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.592136Z","title":"Support vector machine ac- tive learning with applications to text classification.J","venue":null,"work_id":"aa46739a-78d6-483c-a5a8-3ebc4b840f51","year":2002},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.280973Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:5b4245263b83f4d9d251869a66021b965c9d2197e5dd3e8f1ec78150afc438e7","observation_id":"6bdac989-6eef-4d23-9adb-dbf102f69f18","resolution":{"observed_at":"2026-08-11T10:40:37.599432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.573189Z","title":"Generalized category discovery","venue":null,"work_id":"2f4d737e-9cb3-4457-be6d-9e2875e0ddf4","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.286070Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:7327e38746e6b6ecf7e80cef47e7d413e542a9db34e3910d12955d060e4f39af","observation_id":"6e69898c-fee9-4e25-9fd5-7143d5dc0d50","resolution":{"observed_at":"2026-08-11T10:40:37.578505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03923","last_updated":"2024-01-30T12:24:42Z","snapshot_observed_at":"2026-07-06T15:24:00.424056Z","submitted_at":"2023-05-06T04:11:03Z","title":"Active Continual Learning: On Balancing Knowledge Retention and Learnability","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03923","snapshot_observed_at":"2026-08-11T10:40:37.291795Z","title":"Active continual learning: On balancing knowledge retention and learnability","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.291795Z"},"links":{"cited_paper":"/paper/2305.03923","citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:24448a501229882c724e341cc334a8bbbaca1673ca6f11cdad8eb6cab659effc","observation_id":"4b660dff-49d8-42d4-86c6-c4c0035df043","resolution":{"observed_at":"2026-08-11T10:40:37.291795Z","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":"2026-08-11T10:40:37.555258Z","title":"Vim: Out-of-distribution with virtual-logit matching","venue":null,"work_id":"b1300ee0-8f1b-497b-bbf9-1c22c536f93b","year":2022},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.297610Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:3caa006af7517025a09c7540397979c9e0012a30e618f6ff66b61865213c5c00","observation_id":"a5d06529-2b40-4471-95a4-421811135776","resolution":{"observed_at":"2026-08-11T10:40:37.561117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.529399Z","title":"Beneficial perturbation network for designing general adap- tive artificial intelligence systems","venue":null,"work_id":"637b6d41-a91b-4759-8330-4289ed8a89ff","year":2021},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.303604Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:168b27a18241a303882c4e0cbd7444f89f4473dccdc937b8e11fb2fdf8479097","observation_id":"5e8467e9-d71e-4f81-8b09-8a07820c5e6c","resolution":{"observed_at":"2026-08-11T10:40:37.540291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.508565Z","title":"Learning loss for active learning","venue":null,"work_id":"ecc58d54-1b17-49e6-8591-ff6db84f50a0","year":null},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.309218Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:dffdfe4b1e38403bb8079ce15a743bca4d8008609e2722b4043af430f251f377","observation_id":"82c11345-bdea-4859-adf2-6836e6ab2bd4","resolution":{"observed_at":"2026-08-11T10:40:37.515182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T10:40:37.482823Z","title":"Places: A 10 million image database for scene recognition","venue":null,"work_id":"4d7a8c7c-8e2c-42db-ad69-c7aaaab0ea2c","year":2017},"citing_paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T10:40:37.317137Z"},"links":{"citing_paper":"/paper/2412.16409"},"observation_digest":"sha256:1e3c4ef686c711685b68f378f158d0d41c5192f6948f2e0ffea4e9fde40fee6d","observation_id":"ef79393e-3f4d-49da-98ef-29599c95484b","resolution":{"observed_at":"2026-08-11T10:40:37.492699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16409","last_updated":"2024-12-21T00:09:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T10:34:40.511508Z","submitted_at":"2024-12-21T00:09:20Z","title":"Uncertainty Quantification in Continual Open-World Learning"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":42},"total_outbound_references":53},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2412.16409."}