{"as_of":"2026-08-10T02:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d5a191b5b35240455b2d8cb5caf6858da29990a9d9979fcf2f2b993b1ac2938e","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":28,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T22:11:15.762865Z","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":"2026-07-04T06:19:38.018427Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","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":"2026-08-07T14:33:13.790579Z","title":"Gradient projection memory for continual learning.arXiv preprint arXiv:2103.09762,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.18568","last_updated":"2025-05-24T07:16:55Z","snapshot_observed_at":"2026-08-07T22:28:28.687793Z","submitted_at":"2025-05-24T07:16:55Z","title":"Learning without Isolation: Pathway Protection for Continual Learning","version":1},"reference_index":2000,"source":"pdf_text","source_observed_at":"2026-08-07T14:33:13.790579Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2505.18568"},"observation_digest":"sha256:24af9c5fd26870114a286c6b33fd7f5fe91e250a00e0a1799622020e5ea9fe43","observation_id":"5e7a85ab-d2a9-42cf-9053-f49bb04728c5","resolution":{"observed_at":"2026-08-07T14:33:13.790579Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-07T13:59:36.608853Z","title":"Gradient projection memory for continual learn- ing","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20485","last_updated":"2025-06-30T19:20:31Z","snapshot_observed_at":"2026-08-07T22:25:20.793479Z","submitted_at":"2025-05-26T19:43:11Z","title":"Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:59:36.608853Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2505.20485"},"observation_digest":"sha256:778d7a8d9a69405909b1b8892c3d9523a5b39fc1a0159bf1fd689a9ef5be3162","observation_id":"d7fb2d79-5ee2-499f-9e3a-27fb651f01a5","resolution":{"observed_at":"2026-08-07T13:59:36.608853Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-07T13:26:44.995114Z","title":"Gradient Projection Memory for Continual Learning , March 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21942","last_updated":"2025-05-28T03:52:34Z","snapshot_observed_at":"2026-08-09T06:07:36.077148Z","submitted_at":"2025-05-28T03:52:34Z","title":"Continual Learning Beyond Experience Rehearsal and Full Model Surrogates","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-07T13:26:44.995114Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2505.21942"},"observation_digest":"sha256:25981a08497ee22016e81873dc5a2582b7e9702b9a45782b28d11185aef6aeb4","observation_id":"c356b2fc-de1b-4f2a-8785-c3f1b68cf541","resolution":{"observed_at":"2026-08-07T13:26:44.995114Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-07T13:17:01.022589Z","title":"Gradient projection memory for continual learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22408","last_updated":"2025-05-28T14:37:57Z","snapshot_observed_at":"2026-08-09T07:43:48.686524Z","submitted_at":"2025-05-28T14:37:57Z","title":"Frugal Incremental Generative Modeling using Variational Autoencoders","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:17:01.022589Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2505.22408"},"observation_digest":"sha256:cd71afa4ba32776f4ef69da10143a8e6a74859154dcb51477cf489f0889ee750","observation_id":"11b795f3-88ef-4062-8ad0-d0079bf847e0","resolution":{"observed_at":"2026-08-07T13:17:01.022589Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-07T05:22:42.177849Z","title":"Gradient projection memory for continual learning.arXiv preprint arXiv:2103.09762, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08255","last_updated":"2026-05-29T14:58:51Z","snapshot_observed_at":"2026-08-07T22:28:25.880949Z","submitted_at":"2025-06-09T21:43:56Z","title":"SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense","version":4},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T05:22:42.177849Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2506.08255"},"observation_digest":"sha256:393ebde74612a60f534a9d547bb030722032811deda61c594c2473ab25ca6279","observation_id":"8e11c5a4-d857-4c2b-8787-ebddfe0cc49b","resolution":{"observed_at":"2026-08-07T05:22:42.177849Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-08T22:11:15.762865Z","title":"Gradient projection memory for continual learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.09999","last_updated":"2025-02-07T02:22:11Z","snapshot_observed_at":"2026-08-08T22:04:54.631065Z","submitted_at":"2025-02-07T02:22:11Z","title":"Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive Fusion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T22:11:15.762865Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2506.09999"},"observation_digest":"sha256:2f52bd7cf65adc6380ca2961be382666c1b1ea3d6bc0e6dd80c1b83bc0c1d7d7","observation_id":"163bcb0d-15a7-49ae-b0a5-29ca213f37e7","resolution":{"observed_at":"2026-08-08T22:11:15.762865Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-06T12:32:06.541738Z","title":"Gradient projection memory for continual learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21738","last_updated":"2025-07-29T12:16:55Z","snapshot_observed_at":"2026-08-07T22:28:21.437798Z","submitted_at":"2025-07-29T12:16:55Z","title":"Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T12:32:06.541738Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2507.21738"},"observation_digest":"sha256:9114a2dbcf3a88a878a54c8c4d47f69f2584df44a07b875ce440f08a575e23ed","observation_id":"8e063407-9301-44b0-8d8f-844bdb9dcbe3","resolution":{"observed_at":"2026-08-06T12:32:06.541738Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-06T11:37:31.785543Z","title":"Gradi- ent projection memory for continual learning.arXiv preprint arXiv:2103.09762, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.22553","last_updated":"2025-07-30T10:25:28Z","snapshot_observed_at":"2026-08-08T06:39:51.022473Z","submitted_at":"2025-07-30T10:25:28Z","title":"RainbowPrompt: Diversity-Enhanced Prompt-Evolving for Continual Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:37:31.785543Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2507.22553"},"observation_digest":"sha256:ff60a5582c85cc3bf7be0e1fe93ccf67a818dd5acdabc835a9f81eefd61d0d53","observation_id":"a49b85d3-da68-4815-9d6e-7ad8e4d30546","resolution":{"observed_at":"2026-08-06T11:37:31.785543Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-05T17:19:17.827340Z","title":"Gradient projection memory for continual learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.16512","last_updated":"2025-08-22T16:35:19Z","snapshot_observed_at":"2026-08-07T08:47:26.164430Z","submitted_at":"2025-08-22T16:35:19Z","title":"Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T17:19:17.827340Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2508.16512"},"observation_digest":"sha256:85f39fd01a63f356a27a7efa93036e449a823d8155a768f3c90b1af87fd3fc42","observation_id":"a68991f9-5a41-423e-8705-ee2685a82cd9","resolution":{"observed_at":"2026-08-05T17:19:17.827340Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-05T10:34:45.744361Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.03934","last_updated":"2025-09-04T06:50:47Z","snapshot_observed_at":"2026-08-05T10:34:39.922254Z","submitted_at":"2025-09-04T06:50:47Z","title":"SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-05T10:34:45.744361Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2509.03934"},"observation_digest":"sha256:e196204243f4d1220174cbea49754f3296990467caf44ccd5f9633ade06fbc1b","observation_id":"497ec6b9-bcc2-4576-9cd4-13bc542bc52d","resolution":{"observed_at":"2026-08-05T10:34:45.744361Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-03T04:54:48.086335Z","title":"Gradient projection memory for continual learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.03846","last_updated":"2026-06-15T22:17:43Z","snapshot_observed_at":"2026-08-09T04:46:21.995253Z","submitted_at":"2026-02-03T18:59:42Z","title":"PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T04:54:48.086335Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2602.03846"},"observation_digest":"sha256:a6e958aa59e011308be242bedf83ae8b3f631e360a10c12b1ed31bfe72de77e6","observation_id":"04cf26e8-5938-4646-9575-bee7b2f220a3","resolution":{"observed_at":"2026-08-03T04:54:48.086335Z","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":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2603.20410","last_updated":"2026-04-16T22:24:09Z","snapshot_observed_at":"2026-08-02T16:56:48.494461Z","submitted_at":"2026-03-20T18:30:38Z","title":"SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-15T08:07:24.246646Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2603.20410"},"observation_digest":"sha256:0c1294a60a69a815a87fe06761b812ffa89f62564a7a03a45a8902ed824043f5","observation_id":"f1c5e361-0104-4f61-aacc-1dbb9e0ec82b","resolution":{"observed_at":"2026-05-15T08:09:51.327963Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2604.14176","last_updated":"2026-03-25T14:58:42Z","snapshot_observed_at":"2026-08-08T17:09:38.548755Z","submitted_at":"2026-03-25T14:58:42Z","title":"The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-15T00:30:02.907230Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2604.14176"},"observation_digest":"sha256:6e388e12e3dce96c7ac6ab5f921a93cd2b228043b2a77e9d34f00beff8ece480","observation_id":"4cf9385a-3e26-40f7-80ce-737dc0043139","resolution":{"observed_at":"2026-05-15T00:33:23.454847Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2605.08949","last_updated":"2026-05-14T22:50:46Z","snapshot_observed_at":"2026-07-06T23:21:06.773761Z","submitted_at":"2026-05-09T13:42:08Z","title":"Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-12T02:12:25.713592Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2605.08949"},"observation_digest":"sha256:5df5e203726452d40c6ee322712b0987a822f15fbd3c85c474fc148912b16cf8","observation_id":"094ef791-65a5-4336-996e-31512d0ac3d9","resolution":{"observed_at":"2026-05-12T02:16:16.357064Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2605.08949","last_updated":"2026-05-14T22:50:46Z","snapshot_observed_at":"2026-07-06T23:21:06.773761Z","submitted_at":"2026-05-09T13:42:08Z","title":"Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-19T15:11:43.827012Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2605.08949"},"observation_digest":"sha256:dc89795da07f47e6bbe49a99845a89601d4713605feaa2174d9bde402f9315ce","observation_id":"cca22aa0-6057-42d0-a604-da45827a8d37","resolution":{"observed_at":"2026-05-19T15:12:37.480755Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2605.15775","last_updated":"2026-05-15T09:31:10Z","snapshot_observed_at":"2026-07-06T23:27:01.213106Z","submitted_at":"2026-05-15T09:31:10Z","title":"Continual Learning of Domain-Invariant Representations","version":1},"reference_index":105,"source":"arxiv_source","source_observed_at":"2026-05-20T21:16:08.255679Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2605.15775"},"observation_digest":"sha256:9a1e472098b61f5bda0c9928a4cab1070cc59fdaccb310bae449b9f37eec7cad","observation_id":"b6c83ce9-b7ad-4b65-b095-9e7a11d5f3ab","resolution":{"observed_at":"2026-05-20T21:19:03.153566Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2605.19042","last_updated":"2026-05-18T19:05:40Z","snapshot_observed_at":"2026-07-06T23:29:47.081347Z","submitted_at":"2026-05-18T19:05:40Z","title":"Interference-Aware Multi-Task Unlearning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-20T10:25:57.025905Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2605.19042"},"observation_digest":"sha256:b8238b4b5564deca7ee431903e8362df43e10b0e4ef4580af90563b39554616b","observation_id":"69d651bc-7a07-427d-ba74-e2c471434020","resolution":{"observed_at":"2026-05-20T10:28:12.063585Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2606.11391","last_updated":"2026-06-09T19:16:12Z","snapshot_observed_at":"2026-08-03T14:24:49.540209Z","submitted_at":"2026-06-09T19:16:12Z","title":"Recursive Binding on a Budget: Subspace Carving in Order-p Tensor Memories","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-06-27T13:56:36.654708Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2606.11391"},"observation_digest":"sha256:d22fe4da34acc8a8cc910f6920cfaa83d56ee808ab42864aa440a9c29cbb2bd2","observation_id":"83ef4a51-5438-447b-9d7d-1ee9b448468b","resolution":{"observed_at":"2026-07-03T04:27:36.508725Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2606.18627","last_updated":"2026-06-23T03:37:22Z","snapshot_observed_at":"2026-08-03T04:42:05.437110Z","submitted_at":"2026-06-17T02:48:35Z","title":"PACT: Preserving Anchored Cores in Task-vectors for Model Merging","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T21:57:07.546038Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2606.18627"},"observation_digest":"sha256:3509a5d68be5d564910a745ecbe99e573dd15f529972af07c299a282274e1920","observation_id":"854fec10-1083-4e83-9092-8f094ebd5153","resolution":{"observed_at":"2026-07-03T23:39:03.932909Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2606.21307","last_updated":"2026-06-19T10:41:30Z","snapshot_observed_at":"2026-08-08T12:50:56.546243Z","submitted_at":"2026-06-19T10:41:30Z","title":"Task-Differentiated Atomic Skill Expansion and Routing for Continual Learning Across Highly Heterogeneous Tasks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T14:37:20.383589Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2606.21307"},"observation_digest":"sha256:53cb99aaf711ec939cdd72aa5a5e5e09abff958e6df232441c9a026248b587b9","observation_id":"189e17d1-3348-4678-9fe6-7ce736d17db5","resolution":{"observed_at":"2026-07-04T06:19:38.019995Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2606.24901","last_updated":"2026-06-12T13:44:48Z","snapshot_observed_at":"2026-08-03T03:21:51.293576Z","submitted_at":"2026-06-12T13:44:48Z","title":"LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-06-27T05:02:18.347642Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2606.24901"},"observation_digest":"sha256:170181505bba98386e0961c9bd3051238f8292b91e81924ea817ed194c50e6c9","observation_id":"2b12f03a-8277-4a04-bd1e-ee9be079ed45","resolution":{"observed_at":"2026-07-03T16:48:39.917185Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2606.28745","last_updated":"2026-06-27T05:51:23Z","snapshot_observed_at":"2026-08-04T21:49:43.374107Z","submitted_at":"2026-06-27T05:51:23Z","title":"FreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-30T09:55:08.186739Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2606.28745"},"observation_digest":"sha256:bcc6a23ba89eb106b3551e5c3c02a5cfbdc6dbeb3436f7c0a1be93e090780d11","observation_id":"69114548-ce4d-4f3f-938e-de5ae802fad4","resolution":{"observed_at":"2026-06-30T12:54:40.500829Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning","version":1},"cited_work":{"arxiv_id":"2103.09762","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.09762","snapshot_observed_at":"2026-07-04T06:19:38.018427Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":"c153a61d-0cc6-4da4-b9a8-ebb88319bd68","year":2021},"citing_paper":{"arxiv_id":"2606.29164","last_updated":"2026-06-28T02:59:08Z","snapshot_observed_at":"2026-08-07T22:27:12.733103Z","submitted_at":"2026-06-28T02:59:08Z","title":"Invariant Reasoning Directions in Latent Trajectories of Language Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T08:06:54.835970Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2606.29164"},"observation_digest":"sha256:112da92a36e32fbacf6f7382107516a69f31d295364fba983bf7436c5955c323","observation_id":"a245ac28-71b3-4d5f-abd9-807cd3152fa7","resolution":{"observed_at":"2026-06-30T08:14:25.761932Z","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":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","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":"2026-08-01T18:23:29.806079Z","title":"arXiv preprint arXiv:2103.09762 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.17340","last_updated":"2026-07-19T17:02:15Z","snapshot_observed_at":"2026-08-08T07:43:39.816068Z","submitted_at":"2026-07-19T17:02:15Z","title":"Orthogonal Knowledge Refreshing for Domain-Incremental Object Detection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T18:23:29.806079Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2607.17340"},"observation_digest":"sha256:0b7180b2317a50feaf108b08efd2126363fcb7d170f452456630329782ada8a4","observation_id":"61483427-04dd-4a79-96df-d55dde8e339a","resolution":{"observed_at":"2026-08-01T18:23:29.806079Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-02T09:17:59.237440Z","title":"arXiv preprint arXiv:2103.09762 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.19384","last_updated":"2026-07-01T11:46:30Z","snapshot_observed_at":"2026-08-07T22:27:47.693900Z","submitted_at":"2026-07-01T11:46:30Z","title":"SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T09:17:59.237440Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2607.19384"},"observation_digest":"sha256:285391acd2b57b2e1052a7b55746929a0d976b9e83b30a398c171a8cedf68a28","observation_id":"6b6bcca8-480c-4bfd-a73f-e0864814361f","resolution":{"observed_at":"2026-08-02T09:17:59.237440Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-07-30T10:55:15.339975Z","title":"arXiv preprint arXiv:2103.09762 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23837","last_updated":"2026-07-26T20:42:56Z","snapshot_observed_at":"2026-08-03T05:54:37.554482Z","submitted_at":"2026-07-26T20:42:56Z","title":"Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-07-30T10:55:15.339975Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2607.23837"},"observation_digest":"sha256:ee48a2f511a2913db9b5a4660686599724ef522bb77997acb9c347673b307c36","observation_id":"e85993e5-bc11-49b0-8d21-aea13b51c57d","resolution":{"observed_at":"2026-07-30T10:55:15.339975Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-07-31T22:47:13.092981Z","title":"(2021).Gradient Projection Memory for Continual Learning.International Conference on Learning Represen- tations (ICLR 2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.27940","last_updated":"2026-07-31T04:21:25Z","snapshot_observed_at":"2026-08-09T12:34:14.004122Z","submitted_at":"2026-07-30T09:49:13Z","title":"TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-31T22:47:13.092981Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2607.27940"},"observation_digest":"sha256:ed83d46af8cce0d0f129cf82273171392787d97077f41c55ea3fc976a4dc1a90","observation_id":"d20a7dd5-11b1-4cfc-9dd0-2abeed20db33","resolution":{"observed_at":"2026-07-31T22:47:13.092981Z","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":"2026-08-07T22:26:17.816043Z","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":"2026-08-03T01:42:09.481728Z","title":"(2021).Gradient Projection Memory for Continual Learning.International Conference on Learning Represen- tations (ICLR 2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.27940","last_updated":"2026-07-31T04:21:25Z","snapshot_observed_at":"2026-08-09T12:34:14.004122Z","submitted_at":"2026-07-30T09:49:13Z","title":"TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T01:42:09.481728Z"},"links":{"cited_paper":"/paper/2103.09762","citing_paper":"/paper/2607.27940"},"observation_digest":"sha256:a5410b196e63900da10348d061ae7e943b63870d4ecfa369178646b3fcadba1e","observation_id":"b0d8e1c4-3a8e-4e45-9e7b-c37ae6b4d827","resolution":{"observed_at":"2026-08-03T01:42:09.481728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2103.09762/citation-record","integrity":"/paper/2103.09762/integrity","json":"/paper/2103.09762/citation-record.json","paper":"/paper/2103.09762"},"outbound":[],"paper":{"arxiv_id":"2103.09762","last_updated":"2021-03-17T16:31:29Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T22:26:17.816043Z","submitted_at":"2021-03-17T16:31:29Z","title":"Gradient Projection Memory for Continual Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2103.09762."}