{"as_of":"2026-08-09T23:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1c8435615d3fc9d2361be8c3f00e43b5d4439c534ed53148dd2f05943652f577","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T04:01:20.831727Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.29592/citation-record","integrity":"/paper/2607.29592/integrity","json":"/paper/2607.29592/citation-record.json","paper":"/paper/2607.29592"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:13.851517Z","title":"In or out? fixing imagenet out-of-distribution detection evaluation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:13.851517Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:b4b5d1c07c4b905ef03c265bbd5eaccf672b38ee01f7ed70a7a50abaa70c1e37","observation_id":"35f35ce0-81b6-4662-a1f7-77dd6f14ebab","resolution":{"observed_at":"2026-08-03T04:01:13.851517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:13.985119Z","title":"D ark E xperience R eplay for C ontinual L earning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:13.985119Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:38aa2f146aa8c1a1066f0c0f64d3042cf24f299c5596f4223419770dcbca414b","observation_id":"b34d0c2f-ec14-4a13-8c69-113de43d9ac1","resolution":{"observed_at":"2026-08-03T04:01:13.985119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:14.139903Z","title":"Efficient lifelong learning with A-GEM","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:14.139903Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:8044d23dd5c3e20a91ba5e7a000e4495d3397a86e3b69c936a0de95f64984511","observation_id":"56b531cc-1a46-46a0-8ceb-12794a7cee95","resolution":{"observed_at":"2026-08-03T04:01:14.139903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.10486","last_updated":"2019-06-04T07:59:35Z","snapshot_observed_at":"2026-08-07T08:44:38.959537Z","submitted_at":"2019-02-27T12:34:19Z","title":"On Tiny Episodic Memories in Continual Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.10486","snapshot_observed_at":"2026-08-03T04:01:14.259521Z","title":"O n tiny episodic memories in continual learning","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:14.259521Z"},"links":{"cited_paper":"/paper/1902.10486","citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:d765ff8fb1d218b52cccbfca164ff3aef1f13749cca04aa25fd37b12487b795f","observation_id":"af5294b7-598d-4663-b209-8fb59fe809c0","resolution":{"observed_at":"2026-08-03T04:01:14.259521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:14.406577Z","title":"Describing textures in the wild","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:14.406577Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:d40765dee0c0a9feb27e6bdfc2aac4896f53b0cc0397733e9f8a659210c85fec","observation_id":"885b20d9-7aee-4778-b4af-a7ff58a1ef68","resolution":{"observed_at":"2026-08-03T04:01:14.406577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:14.535694Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:14.535694Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:2e8062f7f63c35350ffbf6f27b8a4423ac1665e1b57d59035882cea7f502a881","observation_id":"bad4a737-0d92-4307-8cd7-99e2627f40cc","resolution":{"observed_at":"2026-08-03T04:01:14.535694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:14.646015Z","title":"Extremely simple activation shaping for out-of-distribution detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:14.646015Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:97e24d6735dfa085a239d6dcc7777bcc17a60aaa1e93eff47eb0127fbc65fe28","observation_id":"8e3ceb68-70c6-4bbd-8a27-959ed6258893","resolution":{"observed_at":"2026-08-03T04:01:14.646015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.10652","last_updated":"2022-09-21T20:49:26Z","snapshot_observed_at":"2026-07-06T13:54:56.779166Z","submitted_at":"2022-09-21T20:49:26Z","title":"Toy Models of Superposition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.10652","snapshot_observed_at":"2026-08-03T04:01:14.775625Z","title":"Toy models of superposition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:14.775625Z"},"links":{"cited_paper":"/paper/2209.10652","citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:bda86b0536aa046d831ae4d4728d8b9d1f3e8f3628c536a2f5e5847ceeb1afcb","observation_id":"6d0aa7bd-c506-4edb-855e-c57535580ead","resolution":{"observed_at":"2026-08-03T04:01:14.775625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:14.916278Z","title":"An introduction to roc analysis","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:14.916278Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:f542d7871570a3a597cabbd2e1368a237db859b64602b175761967302332a6cb","observation_id":"8918ae05-b03a-4ceb-b17f-0287206b405a","resolution":{"observed_at":"2026-08-03T04:01:14.916278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:15.039737Z","title":"Weinberger","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.039737Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:d33096121337678365fd0d609376c0258cadd194b32bc67ba51d2308699c6945","observation_id":"e690eb86-c730-4588-89ce-f2f427a40881","resolution":{"observed_at":"2026-08-03T04:01:15.039737Z","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":"2512.19725","doi":"10.48550/arxiv.2512.19725","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Out-of-distribution detection for continual learning: Design principles and benchmarking","venue":"arXiv (Cornell University)","work_id":"9db9211c-709d-4515-a6f4-ff8a96744530","year":2025},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.132718Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:e4bcccb10eb3720c4ea64caed413ada51f006a58f4568bed3db9915ffea02f83","observation_id":"9243e2af-dff6-4108-8aca-08b56e31961f","resolution":{"observed_at":"2026-08-03T04:03:19.080687Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-03T04:01:15.298761Z","title":"Buffer-free class-incremental learning with out-of-distribution detection","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.298761Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:03f66bfef90187c54dfefadb195cb4accbbb93b2f4484375c38d1419c9085d8b","observation_id":"963f3c6b-a4b5-451f-a57d-bf294f89d4b9","resolution":{"observed_at":"2026-08-03T04:01:15.298761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:15.413457Z","title":"Controlling neural collapse enhances out-of-distribution detection and transfer learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.413457Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:d23a870394526fda015dc9a0d1bdf92dd313b09897258744bdb6d96013b36506","observation_id":"71a807bd-19af-4f31-a554-e217502c21ae","resolution":{"observed_at":"2026-08-03T04:01:15.413457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:15.588863Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.588863Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:28943023eae134300e209b1dadb30719d9bee904a5f890161c44319f5425358d","observation_id":"f49ec327-a234-4da5-97af-c42e95e95a3e","resolution":{"observed_at":"2026-08-03T04:01:15.588863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:15.739111Z","title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.739111Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:4f96d4cbb3f12bb922d038fad7a82cd50f9381a4896f00b6b474b45f09352855","observation_id":"9c4e82f5-8867-4775-b362-3be4d5522f6f","resolution":{"observed_at":"2026-08-03T04:01:15.739111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:15.813826Z","title":"MOS: towards scaling out-of-distribution detection for large semantic space","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.813826Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:562d24ed6fb985dcc0041babe4b146acee1c6a8728d09012382167694520f81e","observation_id":"c588ace3-2458-4ce4-ac6d-3270016fd2ec","resolution":{"observed_at":"2026-08-03T04:01:15.813826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:15.871005Z","title":"R obust statistics","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.871005Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:74314b2196b15f1d7dc41cc55afa4606352e779d586cadba4cf3e0ea8d34a01c","observation_id":"ec42b5cb-400c-48c2-bd81-092434ffa233","resolution":{"observed_at":"2026-08-03T04:01:15.871005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:15.956102Z","title":"Continual learning based on OOD detection and task masking","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:15.956102Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:1d2a2f932e74ea74d0260aca20ee1b771cac6cea24ced0bd00a4dc811262853e","observation_id":"53dc665b-921f-422a-ac8a-46b52935fda5","resolution":{"observed_at":"2026-08-03T04:01:15.956102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00796","last_updated":"2017-01-25T13:01:51Z","snapshot_observed_at":"2026-08-04T14:09:02.234596Z","submitted_at":"2016-12-02T19:18:37Z","title":"Overcoming catastrophic forgetting in neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.00796","snapshot_observed_at":"2026-08-03T04:01:16.011277Z","title":"Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:16.011277Z"},"links":{"cited_paper":"/paper/1612.00796","citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:b047929bea460e8847dd2cdefad0c27da2731017d87db80e5e387b88e53b44c7","observation_id":"baedce15-f267-45bf-8aa4-75afc69fa718","resolution":{"observed_at":"2026-08-03T04:01:16.011277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:16.137481Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:16.137481Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:3f53e81183c2782835b2222759ce812306b1d9722c278c81d00676ae7ffb919c","observation_id":"09acf2f4-7ecc-4803-8772-015734b74558","resolution":{"observed_at":"2026-08-03T04:01:16.137481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:16.340039Z","title":"L earning M ultiple L ayers of F eatures from T iny I mages","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:16.340039Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:279541e431bec170d02ec8f3c4b4d1752135e8e9526690ce531f3646ddf4a091","observation_id":"a12bacab-d2d5-4793-8394-4fffb045a7da","resolution":{"observed_at":"2026-08-03T04:01:16.340039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:16.479073Z","title":"Fine-tuning can distort pretrained features and underperform out-of-distribution","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:16.479073Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:8d96245c60c97977c11b18699aa4d3756c890ed575448e1b1fc277972de5e955","observation_id":"7f9dce3a-637a-4460-953b-9902d7f0a353","resolution":{"observed_at":"2026-08-03T04:01:16.479073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:16.615354Z","title":"Kylberg texture dataset v","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:16.615354Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:1d4f1fbba9868b1372256adf67148c33cc8f498bac5f9abaa3440f4475438338","observation_id":"733bd9ff-e4d0-4415-b302-f93ad8150f36","resolution":{"observed_at":"2026-08-03T04:01:16.615354Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:16.772133Z","title":"A S imple and E ffective B aseline for O ut-of-distribution D etection","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:16.772133Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:6f7499d6f27203092dcba698bc9fda52103f6c0c433390d911394dd18c6bf954","observation_id":"487f0890-d01d-45b9-b651-5eddcbfdc208","resolution":{"observed_at":"2026-08-03T04:01:16.772133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:16.949669Z","title":"L earning W ithout F orgetting","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:16.949669Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:292f62881a51cd514cb5cddf7bb9da89ad50b2d5a398414da23aa577105684a9","observation_id":"010d2036-5800-4bc8-b502-d53bd4255e57","resolution":{"observed_at":"2026-08-03T04:01:16.949669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:17.072010Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.072010Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:51d5b138a8ef6d07e408caada91c162a933a20414cc41d1eb02e418d6efc3945","observation_id":"7926cc00-2349-42a0-be7b-1b01589045d0","resolution":{"observed_at":"2026-08-03T04:01:17.072010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:17.191127Z","title":"Owens, and Yixuan Li","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.191127Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:e2e71975f3e829ce7ddf831cb88058b32ccc570db5376073750bcb4a7bb374fc","observation_id":"b54764fe-f2de-4e6b-b1c2-ef11da7ae288","resolution":{"observed_at":"2026-08-03T04:01:17.191127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:17.333191Z","title":"A valanche: A n E nd-to-end L ibrary for C ontinual L earning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.333191Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:20ad9fe9f12304faf419b6f2b19111be83704f5dd0e9c41e4817cdf766c21458","observation_id":"d0e9e53d-7547-4d14-980a-af88f38e9d63","resolution":{"observed_at":"2026-08-03T04:01:17.333191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:17.408723Z","title":"Gradient episodic memory for continual learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.408723Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:832e7a2f7be23acce068df51345502bbf5212826cbeaabc79c8b988ca47851d1","observation_id":"30a76a4d-a905-4899-8f62-957a1d840084","resolution":{"observed_at":"2026-08-03T04:01:17.408723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:17.497452Z","title":"O pen C I L : B enchmarking out-of-distribution detection in class incremental learning","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.497452Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:72db5bc985cf18f7e3e6f55bfaf7cf413e59079ac8030a2f22c04ae627351135","observation_id":"31bd6de6-dff9-47fa-9177-4f0d927ef701","resolution":{"observed_at":"2026-08-03T04:01:17.497452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:17.567077Z","title":"Revisiting the calibration of modern neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.567077Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:9fbbc98d33dc31aa64c7b41c86f63a5888ed39690230131531fd94140a07428f","observation_id":"136a8739-b851-444d-b5a8-e90d103286a7","resolution":{"observed_at":"2026-08-03T04:01:17.567077Z","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":"10.1007/s11263-023-01895-7","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"H ow D oes F ine-tuning I mpact O ut-of-distribution D etection for V ision-language M odels? Int","venue":"International Journal of Computer Vision","work_id":"c439c31e-cb42-4d77-aa20-031a4691855f","year":2024},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.676508Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:a559f82af4d32a388671c8fa98fc0a2a976d887f4c14d07f5aa17439283a9c5c","observation_id":"8dcaeda7-0b08-4a04-8c03-796adea77edf","resolution":{"observed_at":"2026-08-03T04:03:18.832507Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-03T04:01:17.847339Z","title":"R eading D igits in N atural I mages with U nsupervised F eature L earning","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:17.847339Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:15254d8affa5a7540219a59d46dc26c0d04708a107b79ecb0decdacd9f182bce","observation_id":"513515f5-95a5-46ac-94de-cd01081e9327","resolution":{"observed_at":"2026-08-03T04:01:17.847339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:18.065707Z","title":"Nearest neighbor guidance for out-of-distribution detection","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:18.065707Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:02ce9e81131ccda40499d0de61ee50a94afc009cf3a265de2e1445b4684befdb","observation_id":"9913ca75-1226-4e98-abc3-85a8ccc9f007","resolution":{"observed_at":"2026-08-03T04:01:18.065707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:18.240447Z","title":"Anatomy of catastrophic forgetting: Hidden representations and task semantics","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:18.240447Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:f79272fd18df34cbce3aff65ca94f962e4cf0de4ce435ddae197a33b77f7f586","observation_id":"35295eec-d9e9-4729-83c0-6271fa4c4a01","resolution":{"observed_at":"2026-08-03T04:01:18.240447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:18.382906Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:18.382906Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:dbae950e58d10fa622b413a527bcfa0381a5532a6e36647786401ba85e9a6f9d","observation_id":"0e5333f9-6b1b-40b5-8f74-aa48ed64abd5","resolution":{"observed_at":"2026-08-03T04:01:18.382906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:18.564321Z","title":"A da S C A L E : A daptive S caling for OOD D etection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:18.564321Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:77faf4c1b69ed8307e6a610668dc0ee91d44c164a1420e2c4eaa910d991c1b8d","observation_id":"05b3c983-4680-44b0-a6d8-1d79480c699e","resolution":{"observed_at":"2026-08-03T04:01:18.564321Z","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":"2026-08-09T14:14:13.613085Z","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":"2026-08-03T04:01:18.723619Z","title":"Progressive neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:18.723619Z"},"links":{"cited_paper":"/paper/1606.04671","citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:d6e37bfb163a983ca5e8679906ff2810b27876f660f4ec24e0589c4443f637ef","observation_id":"2dec14f1-6318-42b0-82b8-03a69d83f280","resolution":{"observed_at":"2026-08-03T04:01:18.723619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:18.874930Z","title":"Overcoming catastrophic forgetting with hard attention to the task","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:18.874930Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:179f7f241d10462fd39512a3fdaaa5458d92d721c29e572cd74dbf03766e3c63","observation_id":"0340a542-548c-4516-bf9e-5423f2944086","resolution":{"observed_at":"2026-08-03T04:01:18.874930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:19.067561Z","title":"DICE: L everaging S parsification for O ut-of-distribution D etection","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:19.067561Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:e27e2a04c4c99f9f471d8025ea6fb51aa3de4de4ead156bb443f88875be491f9","observation_id":"4fad4eaf-967a-4d49-a83b-643435261a1c","resolution":{"observed_at":"2026-08-03T04:01:19.067561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:19.253438Z","title":"React: Out-of-distribution detection with rectified activations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:19.253438Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:669494f0b3ef5448c8a9aa14657c1e242ed6252c5797c3b603734a83cfc94b35","observation_id":"35785885-2c51-4377-a14a-bb9dc4bcdf02","resolution":{"observed_at":"2026-08-03T04:01:19.253438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:19.506852Z","title":"Out-of-distribution detection with deep nearest neighbors","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:19.506852Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:6d0ea95e13c6bbdc94d7f280e8721bb94ecf3b3f180014a9b3fd95657bbbee0e","observation_id":"89ec8fe2-be63-4fc1-9472-4ac0b50d5f8a","resolution":{"observed_at":"2026-08-03T04:01:19.506852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.07734","last_updated":"2019-04-15T12:22:36Z","snapshot_observed_at":"2026-07-06T07:46:29.188511Z","submitted_at":"2019-04-15T12:22:36Z","title":"Three scenarios for continual learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.07734","snapshot_observed_at":"2026-08-03T04:01:19.707043Z","title":"Three scenarios for continual learning","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:19.707043Z"},"links":{"cited_paper":"/paper/1904.07734","citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:36a1b510cefd8a6120e61caf33d80e3495fa3fdde2ea35367dd3cebdad3deb3a","observation_id":"f1093d29-2963-45d8-a706-2fef324423b9","resolution":{"observed_at":"2026-08-03T04:01:19.707043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.10807","last_updated":"2022-03-21T08:56:55Z","snapshot_observed_at":"2026-07-06T12:50:08.081200Z","submitted_at":"2022-03-21T08:56:55Z","title":"ViM: Out-Of-Distribution with Virtual-logit Matching","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.10807","snapshot_observed_at":"2026-08-03T04:01:19.890323Z","title":"V i M : O ut- O f-distribution with V irtual-logit M atching","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:19.890323Z"},"links":{"cited_paper":"/paper/2203.10807","citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:03ba071847c571a7d69b34473a52d273a10206cdeb9b5ce0ec31de5e882c7bcc","observation_id":"29fee81c-5c26-469f-ac54-f29f74fa2ca7","resolution":{"observed_at":"2026-08-03T04:01:19.890323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:20.073482Z","title":"Large scale incremental learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:20.073482Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:e43a4839d9be4d76dded64099f0eb04779b587ecf6131e244487926a63cb5309","observation_id":"12afef34-67ff-47dc-bcdf-c07ac0924291","resolution":{"observed_at":"2026-08-03T04:01:20.073482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.11334","last_updated":"2024-01-23T07:36:33Z","snapshot_observed_at":"2026-08-09T19:23:41.731633Z","submitted_at":"2021-10-21T17:59:41Z","title":"Generalized Out-of-Distribution Detection: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.11334","snapshot_observed_at":"2026-08-03T04:01:20.250096Z","title":"G eneralized out-of-distribution detection: A survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:20.250096Z"},"links":{"cited_paper":"/paper/2110.11334","citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:d573d31d888555ad84cbf8318dfa542766ce563cd91eaa35746bffdf03c5bd2e","observation_id":"a4f3330c-aab0-428b-b7c6-28e73e517fc5","resolution":{"observed_at":"2026-08-03T04:01:20.250096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:20.340405Z","title":"M N I S T handwritten digit database","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:20.340405Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:fc693d81eafafec4ddcc505e9ba1015d53778e2c7025545dc74d081b7a3016ea","observation_id":"7ca7221d-2ccb-4dc3-93b5-9a38041f13ce","resolution":{"observed_at":"2026-08-03T04:01:20.340405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:20.489649Z","title":"Continual learning through synaptic intelligence","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:20.489649Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:6273da0475d2e93739130fc0eaba6fd81390f24b48ae4a4436c82a6076969703","observation_id":"34ffbeee-1ae8-4122-b64a-50250b63f3b5","resolution":{"observed_at":"2026-08-03T04:01:20.489649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:20.587513Z","title":"O pen O O D v1.5: E nhanced B enchmark for O ut-of-distribution D etection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:20.587513Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:f27a28406e7f52f7381d928fa3dc64892b3c5bc89a35e8f4b96c323699028bdf","observation_id":"d3772b09-c346-41a7-9b70-739f638fa2e6","resolution":{"observed_at":"2026-08-03T04:01:20.587513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:20.695593Z","title":"M aintaining discrimination and fairness in class incremental learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:20.695593Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:c247bad59ec9575550ca9e9d7a1b789e4085a7ea406932fbbc50507e8040ee91","observation_id":"81c910c0-9009-4ad4-bb98-35fc2f74da5a","resolution":{"observed_at":"2026-08-03T04:01:20.695593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T04:01:20.831727Z","title":"P laces: A 10 M illion I mage D atabase for S cene R ecognition","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-03T04:01:20.831727Z"},"links":{"citing_paper":"/paper/2607.29592"},"observation_digest":"sha256:c37c084b09d32209ad20b866ba80da62e936fb389979a469e134a7059946f4aa","observation_id":"a0ec0a8e-0b15-4bb0-b1fa-99ef780edcf5","resolution":{"observed_at":"2026-08-03T04:01:20.831727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.29592","last_updated":"2026-07-31T16:18:30Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T21:02:12.570298Z","submitted_at":"2026-07-31T16:18:30Z","title":"TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":49,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":51},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2607.29592."}