{"as_of":"2026-08-20T15:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:986b316feabb29af2c38389666100df06eb60df75a1cddb0d98f0194c1d0c530","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T14:22:14.842803Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:00:20.552546Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T02:06:26.932715Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"cited_work":{"arxiv_id":"2411.15469","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.15469","snapshot_observed_at":"2026-07-28T00:21:30.371308Z","title":"Cheng, Y","venue":null,"work_id":"d4f80946-d268-4a7c-9938-aa00b03a9b08","year":2024},"citing_paper":{"arxiv_id":"2505.18604","last_updated":"2026-05-13T05:15:53Z","snapshot_observed_at":"2026-08-18T14:56:11.384860Z","submitted_at":"2025-05-24T08:59:13Z","title":"Exemplar-Free Continual Learning for State Space Models","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-19T13:27:20.403879Z"},"links":{"cited_paper":"/paper/2411.15469","citing_paper":"/paper/2505.18604"},"observation_digest":"sha256:12bf5e0414d7f2cffee600b67294bea9d03b5ce821f3982b8aa9a2d477242e56","observation_id":"5bf0c40e-c91d-45a6-bc56-a508e71aa3e6","resolution":{"observed_at":"2026-07-28T00:21:30.371308Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.15469","snapshot_observed_at":"2026-08-06T18:00:20.552546Z","title":"Mamba-cl: Optimizing selective state space model in null space for continual learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09471","last_updated":"2026-08-19T01:54:00Z","snapshot_observed_at":"2026-08-20T13:23:11.162789Z","submitted_at":"2025-07-13T03:11:35Z","title":"CKAA: Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T18:00:20.552546Z"},"links":{"cited_paper":"/paper/2411.15469","citing_paper":"/paper/2507.09471"},"observation_digest":"sha256:07265ce7ac4e14d0468d52a11dd0c75b58e2cd459cb4dafc565b69f88972d4b8","observation_id":"59f49a11-c205-41f3-9828-56b3021ff082","resolution":{"observed_at":"2026-08-06T18:00:20.552546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.15469","snapshot_observed_at":"2026-08-03T03:16:05.350480Z","title":"Mambacl: Optimizing selective state space model in null space for continual learning.arXiv preprint arXiv:2411.15469,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.09075","last_updated":"2026-06-03T09:18:46Z","snapshot_observed_at":"2026-08-13T11:59:16.577265Z","submitted_at":"2026-02-09T16:09:51Z","title":"Learning to Remember, Learn, and Forget in Attention-Based Models","version":4},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-03T03:16:05.350480Z"},"links":{"cited_paper":"/paper/2411.15469","citing_paper":"/paper/2602.09075"},"observation_digest":"sha256:84084a2e3c95cf3fe716f10bbd38db6737d902ee143dad70264e7b691698d754","observation_id":"37d138a5-9364-4845-827f-e6b2f826c213","resolution":{"observed_at":"2026-08-03T03:16:05.350480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"cited_work":{"arxiv_id":"2411.15469","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.15469","snapshot_observed_at":"2026-07-28T00:21:30.371308Z","title":"Cheng, Y","venue":null,"work_id":"d4f80946-d268-4a7c-9938-aa00b03a9b08","year":2024},"citing_paper":{"arxiv_id":"2606.03539","last_updated":"2026-06-02T11:59:27Z","snapshot_observed_at":"2026-08-17T05:30:16.350652Z","submitted_at":"2026-06-02T11:59:27Z","title":"Knowledge-Preserved Model Tuning in Null-Space for Robust Spatio-Temporal Video Grounding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T11:14:30.556120Z"},"links":{"cited_paper":"/paper/2411.15469","citing_paper":"/paper/2606.03539"},"observation_digest":"sha256:c720f0605b952274e0c36c0927d91e24151db930c069c7da96ed29f7fad70e66","observation_id":"9b9d7254-2805-43ae-bd10-22dab4155469","resolution":{"observed_at":"2026-07-28T00:21:30.371308Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.15469/citation-record","integrity":"/paper/2411.15469/integrity","json":"/paper/2411.15469/citation-record.json","paper":"/paper/2411.15469"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:22:14.529353Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.529353Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:edbd10863b8444e2149034e02b7e77bf1b72a5937b182032a368a5a355aa0732","observation_id":"51570a3d-caa8-42bd-a815-5bdedd1d5203","resolution":{"observed_at":"2026-08-12T14:22:14.529353Z","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":"2026-08-12T14:22:15.967568Z","title":"Gradient based sample selection for online continual learning","venue":null,"work_id":"f4ed62cb-febc-4c22-b204-53d8d9679f1b","year":2019},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.545790Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:6f67b5658b2e5c24e7aa613e1055031d1586dbb7b2f6c31947367962a7f6b5c5","observation_id":"1b017aae-17a7-489a-8f1f-a381e4ec74a1","resolution":{"observed_at":"2026-08-12T14:22:15.976896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.930041Z","title":"Autoaugment: Learning augmentation strategies from data","venue":null,"work_id":"6b52cfbf-3b95-4197-9246-ff23c6382d50","year":2019},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.551992Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:aaea59cd25fc9f2cf9af02adae2c72ba8eab7195e25b4d82a8031e483bd8ad29","observation_id":"5b8b8e64-21b5-42a6-b047-838de9b8d52e","resolution":{"observed_at":"2026-08-12T14:22:15.940679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.904448Z","title":"On the effectiveness of layernorm tuning for continual learning in vision transformers","venue":null,"work_id":"93fc685c-48ee-41be-a21c-2e578dfd6004","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.557443Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:fc096f52aa5af08e78cd26b6c1add4082b26f04f0979e43323567fdbff7460c8","observation_id":"fa1b67ba-6fb1-48a3-9b9c-8b5b06d0ae6d","resolution":{"observed_at":"2026-08-12T14:22:15.913024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.884678Z","title":"Flattening sharpness for dynamic gradient projection memory benefits continual learning","venue":null,"work_id":"6f326171-abaf-479b-908b-d3e3417eb9b3","year":2021},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.564187Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:5436df9c5c48b891dc1790ee2fb1ea78b71797aa745b6ce0c1f29ae941e27a98","observation_id":"613a9e83-fbc9-4a2a-9207-07f0fb3394f4","resolution":{"observed_at":"2026-08-12T14:22:15.890143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.863749Z","title":"Efficient architecture search for continual learning","venue":null,"work_id":"2c44bbf5-c1ab-4940-a4bf-e62d0a4d020e","year":2022},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.570100Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:82b35a4402bd8fbd4bacaac7c305afd8a35c91497132b737a2a08a6d5c6b1c2a","observation_id":"8c2e6593-ae65-4465-8377-eed12a97ab5b","resolution":{"observed_at":"2026-08-12T14:22:15.869985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.841308Z","title":"A unified continual learning framework with general parameter-efficient tuning","venue":null,"work_id":"98f34db0-8d78-4fd7-8e17-72c52f2d3009","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.575738Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:bdcc9e12100bb55447e9111b362b5749be79720ef2947fe9192c6b64f8b9520b","observation_id":"0ef1cf60-4a1f-4680-8454-00f2c81b342e","resolution":{"observed_at":"2026-08-12T14:22:15.847591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.815459Z","title":"Consistent prompting for rehearsal-free continual learning","venue":null,"work_id":"740d8d4a-b7a5-494b-9b30-73425cd1509e","year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.581219Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:94809a28a64492a35b307a0c2694447df85b2b8a68b63952353699c7bf3e8377","observation_id":"82a9d4c2-a26e-4361-9d27-5c6299f0bf0e","resolution":{"observed_at":"2026-08-12T14:22:15.825669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-12T14:22:14.586981Z","title":"Mamba: Linear-time sequence modeling with selective state spaces","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.586981Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:bb71985a6a87085da9904204a6e930000191d8b34dc5e542fd4ae70e8a2e32b5","observation_id":"0647f9a4-218f-4453-95d9-7c0c526bc988","resolution":{"observed_at":"2026-08-12T14:22:14.586981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-08-14T01:02:41.198730Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-12T14:22:14.593795Z","title":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.593795Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:ac66809a3d071d640816fa05561eeb0331f6d6d3d255175cc814f81ef69879c4","observation_id":"21ba1b2a-1fac-4535-a67c-7d1ea0103b74","resolution":{"observed_at":"2026-08-12T14:22:14.593795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08083","last_updated":"2025-03-25T17:54:37Z","snapshot_observed_at":"2026-08-19T13:18:49.777037Z","submitted_at":"2024-07-10T23:02:45Z","title":"MambaVision: A Hybrid Mamba-Transformer Vision Backbone","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.08083","snapshot_observed_at":"2026-08-12T14:22:14.602148Z","title":"Mambavision: A hybrid mamba-transformer vision backbone","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.602148Z"},"links":{"cited_paper":"/paper/2407.08083","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:fee5809278c1c521e776991494cf6aea3da8b7687ff1218b0898f713c4e103b2","observation_id":"e6ba260e-57fb-4a48-b193-56c8290fabe5","resolution":{"observed_at":"2026-08-12T14:22:14.602148Z","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":"2026-08-12T14:22:14.608774Z","title":"The many faces of robustness: A critical analysis of out-of-distribution generalization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.608774Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:c01d066408d2d88afb2f8863ae88b27b11df7197cc14b4ce4dea3f2e99eeb10d","observation_id":"5b2797c2-31a2-4809-9079-b1a1cc304176","resolution":{"observed_at":"2026-08-12T14:22:14.608774Z","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":"2026-08-12T14:22:15.774833Z","title":"Curiosity-driven class-incremental learning via adaptive sample selection","venue":null,"work_id":"8a0660ae-2121-4381-a982-d56d182f9958","year":2022},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.616821Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:224ef3880621e1286d10f79df8728f805a2bdc091a939e41108901db96807fe0","observation_id":"18f31a48-0573-4419-a5e9-ea78fbadb621","resolution":{"observed_at":"2026-08-12T14:22:15.780797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.749716Z","title":"OVOR : Oneprompt with virtual outlier regularization for rehearsal-free class-incremental learning","venue":null,"work_id":"5c99d675-3247-4e16-9ad0-332ffc2d01ff","year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.623579Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:ddcea95b8654f4470501e1c58b654e9a8a22c92e0bcc2d07ae967a7aaeb76b9d","observation_id":"55bd7a6b-3a23-4a6b-acdd-9de03c4d8c86","resolution":{"observed_at":"2026-08-12T14:22:15.757376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:14.628992Z","title":"Compacting, picking and growing for unforgetting continual learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.628992Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:1904ce6be27401b587424ec7153be0dd3206ea489eac9ed4a0c9b4fbaad91a8f","observation_id":"34296b63-035a-4505-af18-8956c22a6ff9","resolution":{"observed_at":"2026-08-12T14:22:14.628992Z","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":"2026-08-12T14:22:15.713851Z","title":"Efficient movie scene detection using state-space transformers","venue":null,"work_id":"231f49e0-9bcb-4383-a0b8-f4ac3d6ecf70","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.634097Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:d17d15a42337d036a5b922193e2b8e78320e8f5f14aab5cb3f13d23bb42445c0","observation_id":"e6d9a42d-dae4-4bf8-9853-9b9bc4f4e102","resolution":{"observed_at":"2026-08-12T14:22:15.720955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.691596Z","title":"Introducing language guidance in prompt-based continual learning","venue":null,"work_id":"75485e19-91ec-4138-a85f-77c629cbbda2","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.638879Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:bd3d78ee50c5744285d5e2c5b6b05e1613fc3587974200de6c3486bd064aaace","observation_id":"9c81bb58-d792-44ca-baa0-0c5e699f61c5","resolution":{"observed_at":"2026-08-12T14:22:15.700198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-12T14:22:14.646330Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.646330Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:8c90de03894f0bce5c2aba891446ca9ba9489a53ab5698dc0fec1321446e9cb9","observation_id":"56aa7699-3284-4980-b445-bf37b013c00d","resolution":{"observed_at":"2026-08-12T14:22:14.646330Z","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":"2026-08-12T14:22:14.651960Z","title":"Overcoming catastrophic forgetting in neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.651960Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:e1373711af6bbe94eeca7e70c190411eb99fdfbc6f2fa25c4fc8238a8bf5f5de","observation_id":"166c09b7-ee9f-4d3f-b9bc-c5ed5e3b8128","resolution":{"observed_at":"2026-08-12T14:22:14.651960Z","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":"2026-08-12T14:22:14.658335Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.658335Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:79f43a5134487eb809d97e82eae4adf87fa4833239e00eaffa24b24b69d68ed3","observation_id":"032faf55-be6f-4aa5-9f2c-5fb3630ceade","resolution":{"observed_at":"2026-08-12T14:22:14.658335Z","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":"2026-08-12T14:22:15.631369Z","title":"Evolving parameterized prompt memory for continual learning","venue":null,"work_id":"a1b92bc7-b8af-4f86-b8f6-0e413a1bb22b","year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.665301Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:b60106441e8ece86287cff4d32e55567f91bb029706c2619bc8691176e33864f","observation_id":"5f999052-e3b4-4514-8dbf-b1b80faee3e2","resolution":{"observed_at":"2026-08-12T14:22:15.637793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.609671Z","title":"Adaptive plasticity improvement for continual learning","venue":null,"work_id":"e1117be1-9358-4c1a-9ce3-3c52b256995b","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.670738Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:39482baddfbf062139d9a3ca6b9ad7d46b75ac7c5129c1c03397d0b37661b46f","observation_id":"0bce4074-47dd-47e7-9951-e7b3706a7d72","resolution":{"observed_at":"2026-08-12T14:22:15.615734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.584991Z","title":"Inflora: Interference-free low-rank adaptation for continual learning","venue":null,"work_id":"f2209ace-bc3e-47e6-a1a2-30420d056080","year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.676300Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:1513c8d385e4459272c1bf551b6accaf09c694131c8c66fdb20cf92519b4011e","observation_id":"cd5b9d74-7a7b-4dda-bfa4-cd203da2e894","resolution":{"observed_at":"2026-08-12T14:22:15.592834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-08-17T14:56:56.233298Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-08-12T14:22:14.682536Z","title":"Vmamba: Visual state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.682536Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:3cb415410e75a8e6c4caee5edaa267bc1c182ed3ca10854a2e0e35f4cbfe04ad","observation_id":"0becc981-e4ad-4267-8a92-c04e77b168e0","resolution":{"observed_at":"2026-08-12T14:22:14.682536Z","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":"2026-08-12T14:22:15.560327Z","title":"Visual prompt tuning in null space for continual learning","venue":null,"work_id":"429b7a1c-f9c7-44c3-b0bb-f733c3f321e8","year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.688189Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:2f787100f4791d4e28859a7d785e20e27a01259d08164b2f66b3840803cb7c19","observation_id":"c6f7c9ac-b588-4528-b88c-9cac9fdc7357","resolution":{"observed_at":"2026-08-12T14:22:15.567468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04722","last_updated":"2024-01-09T18:53:20Z","snapshot_observed_at":"2026-08-17T03:22:34.203372Z","submitted_at":"2024-01-09T18:53:20Z","title":"U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04722","snapshot_observed_at":"2026-08-12T14:22:14.700353Z","title":"U-mamba: Enhancing long-range dependency for biomedical image segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.700353Z"},"links":{"cited_paper":"/paper/2401.04722","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:0c6165829e03c16459fc973bdc75dd32c8afa7a810e88e49f1d9bba837321d42","observation_id":"c6eadfc1-e1bc-46ea-ac48-ba9273d9063f","resolution":{"observed_at":"2026-08-12T14:22:14.700353Z","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":"2026-08-12T14:22:14.707043Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.707043Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:28221903bff90796ab6333bcdbad88ea1e06df33f3770caf3a4feb65123452b2","observation_id":"86ae9972-d07c-4780-a5ed-08e014ae17ad","resolution":{"observed_at":"2026-08-12T14:22:14.707043Z","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":"2026-08-12T14:22:15.513394Z","title":"Moment matching for multi-source domain adaptation","venue":null,"work_id":"8a65b58f-5d47-45a4-9809-2946695bc8c4","year":2019},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.713984Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:1814f8af21704e1e48ed4436e5b6567b11e4d45d5d3402a56cb6a0fda5baa22b","observation_id":"cf8878f5-c36f-498d-838b-d52b08316c57","resolution":{"observed_at":"2026-08-12T14:22:15.521667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:14.719657Z","title":"Prompt gradient projection for continual learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.719657Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:09cb4b60e60820ccd53a5ed2fac0a270fc6bd8f620bb66d142e5dc017f5e7a4c","observation_id":"3f63251d-9ca3-48c3-8788-91b6ef9466e9","resolution":{"observed_at":"2026-08-12T14:22:14.719657Z","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":"2026-08-12T14:22:15.473419Z","title":"Convolutional prompting meets language models for continual learning","venue":null,"work_id":"cc70c8e3-f473-496c-9fb3-ab214a673692","year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.729040Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:703b6ed753131f3b24086882e0246d971228b8b4e9c9c8ff2681e910a89e0bab","observation_id":"eec952c4-bc36-4b8c-a91d-da3d3cfab075","resolution":{"observed_at":"2026-08-12T14:22:15.480983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:14.735687Z","title":"Imagenet large scale visual recognition challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.735687Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:04ac29b1fef95ce90176762663ca355d1a8882fbba2dd83ca2b0a1815ebc22cd","observation_id":"a7f52563-517c-4073-9516-3544a55fcd7b","resolution":{"observed_at":"2026-08-12T14:22:14.735687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-20T00:16:12.774764Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-08-12T14:22:14.751970Z","title":"Gradient projection memory for continual learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.751970Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:cd7388daa4f1b86684fe96dba37d880594835634da59e872138f2d39436a601f","observation_id":"bd583c75-4a8b-4cb9-b824-621941bb5dcb","resolution":{"observed_at":"2026-08-12T14:22:14.751970Z","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":"2026-08-12T14:22:15.426495Z","title":"Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning","venue":null,"work_id":"03c8fc55-5387-434f-a76b-e62a44cb2893","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.760030Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:4153d976fdc68135fb18a70203e966a718f0f93974c6a58bb262d484fc89c2d6","observation_id":"03b44af6-bfdd-4674-bc4a-657c284d3bdb","resolution":{"observed_at":"2026-08-12T14:22:15.437727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2208.04933","last_updated":"2023-03-03T18:35:28Z","snapshot_observed_at":"2026-08-13T08:14:01.416880Z","submitted_at":"2022-08-09T17:57:43Z","title":"Simplified State Space Layers for Sequence Modeling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.04933","snapshot_observed_at":"2026-08-12T14:22:14.766046Z","title":"Simplified state space layers for sequence modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.766046Z"},"links":{"cited_paper":"/paper/2208.04933","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:2633124ee6eb6747ba3f5caa7546b8130b589fe2a0aa31d463a0fddd50ba6837","observation_id":"9feba25e-b2d3-41c0-9d7a-d0004b9ee639","resolution":{"observed_at":"2026-08-12T14:22:14.766046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04342","last_updated":"2024-06-06T17:59:56Z","snapshot_observed_at":"2026-08-20T04:01:35.943319Z","submitted_at":"2024-06-06T17:59:56Z","title":"Learning 1D Causal Visual Representation with De-focus Attention Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04342","snapshot_observed_at":"2026-08-12T14:22:14.772398Z","title":"Learning 1d causal visual representation with de-focus attention networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.772398Z"},"links":{"cited_paper":"/paper/2406.04342","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:7ad79c28a7d71c93a81c88fa7c42ae7cf23de1eb7f7525219aaa8366e120a900","observation_id":"cbf5f2d0-0890-4d63-815a-f1499f3040ee","resolution":{"observed_at":"2026-08-12T14:22:14.772398Z","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":"2026-08-12T14:22:15.399722Z","title":"Selective structured state-spaces for long-form video understanding","venue":null,"work_id":"1508b651-f8ef-4fe3-9e1a-bbdf5ee83dac","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.779602Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:16f32dc63c14a62906a43f49a4675a62617c6f5b7807a7f02127c9824d48a840","observation_id":"45f79fc8-221a-4ffa-8eb0-bf819bd0e065","resolution":{"observed_at":"2026-08-12T14:22:15.409493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.363750Z","title":"Training networks in null space of feature covariance for continual learning","venue":null,"work_id":"1f0b16f2-aae4-4784-972e-b487cf6292a4","year":2021},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.787910Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:55a3e956f202c2eae744b6dc9e6b53ea73801d2093b9835fa6f84c80dc0f28e2","observation_id":"3d5ac587-b4e0-48be-8e6f-a810c74b614f","resolution":{"observed_at":"2026-08-12T14:22:15.369421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.344222Z","title":"Isolation and impartial aggregation: A paradigm of incremental learning without interference","venue":null,"work_id":"dbf693aa-ebb0-417c-9b24-3e1321fcda06","year":2023},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.794844Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:97360bcda5a7bfe6f18a2ea86404f709f2b080b007af6242402994a05f22ca66","observation_id":"0df5652c-4d6f-4600-afc6-413c3b33a42c","resolution":{"observed_at":"2026-08-12T14:22:15.350111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.324768Z","title":"Dualprompt: Complementary prompting for rehearsal-free continual learning","venue":null,"work_id":"27755f53-404f-48e0-aae3-0817cf461495","year":2022},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.800797Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:0dba99e5a62973abf544bceba4dab32ac235c4d5ea6803c1ef20163f4111b634","observation_id":"7cb0c95c-477b-44f1-b2b4-87414a998052","resolution":{"observed_at":"2026-08-12T14:22:15.331513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.297291Z","title":"Learning to prompt for continual learning","venue":null,"work_id":"3305f0e2-ad6d-481e-b261-e2f81c976c87","year":2022},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.806325Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:9591a5c2b3170572bdbdecd6dd1518c2f1b2b79c83bb763ea799081f3eb65a00","observation_id":"1a8a1bb3-f8d3-4883-a19d-97e83438957b","resolution":{"observed_at":"2026-08-12T14:22:15.306510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:14.811893Z","title":"Pytorch image models","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.811893Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:e2d1b2fa82e87dbee11f883e9e5723d000b923a1504de8b732dc0ede61e0faaa","observation_id":"8b666179-1bb9-425e-8e55-9c1ea4bcb002","resolution":{"observed_at":"2026-08-12T14:22:14.811893Z","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":"2026-08-12T14:22:15.249503Z","title":"Scalable and order-robust continual learning with additive parameter decomposition","venue":null,"work_id":"7bfef995-0468-4707-9b39-ea8aa7dbce14","year":2020},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.817107Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:a53dbbcbbcb05b7fe9919107c9d67e8d4082019c8e0d86ef7c892224a47ad53b","observation_id":"ee1f75ad-2582-458f-96b0-8bd9b9546c24","resolution":{"observed_at":"2026-08-12T14:22:15.259505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:14.823754Z","title":"Continual learning of context-dependent processing in neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.823754Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:bc0727d2e921c7e2b0363eb9fe5be4d9309d88761f02c414105097b5c4767d99","observation_id":"4ffc503c-04d4-423c-9a68-b88610b76f40","resolution":{"observed_at":"2026-08-12T14:22:14.823754Z","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":"2026-08-12T14:22:15.199669Z","title":"Memory-efficient class-incremental learning for image classification","venue":null,"work_id":"5fab86b0-4fd9-41cc-865f-3f2931084a0a","year":2021},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.829299Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:9c38d23e755739c78efa0063f81e1920feeffe664d9bc4ae80c41605acdef022","observation_id":"438a5a02-1db1-4ae5-bd5d-cdf63f50051d","resolution":{"observed_at":"2026-08-12T14:22:15.206525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T14:22:15.163856Z","title":"Expandable subspace ensemble for pre-trained model-based class-incremental learning","venue":null,"work_id":"8b84950c-2cfa-46e3-a82a-2ba303385d90","year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.836716Z"},"links":{"citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:185c49ea1c17f069221df6505b2b7791f0225007ef2c87c5a4c998849c420563","observation_id":"89f5d539-6b41-45fc-bb01-94c9aa8d19f3","resolution":{"observed_at":"2026-08-12T14:22:15.174998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-08-14T11:12:31.002605Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-08-12T14:22:14.842803Z","title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-12T14:22:14.842803Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2411.15469"},"observation_digest":"sha256:2642498ded004ba8ac0e5f4d0bfeae4298ea7e19a6ee0a81799045573c923b28","observation_id":"70ea5305-67c0-4228-beeb-b711f21a8f69","resolution":{"observed_at":"2026-08-12T14:22:14.842803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.15469","last_updated":"2026-07-25T02:34:20Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T02:48:06.385464Z","submitted_at":"2024-11-23T06:36:16Z","title":"Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":26},"total_outbound_references":46},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 4 inbound Pith citation observations for arXiv:2411.15469."}