{"as_of":"2026-08-23T12:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9e2158fa0cd2823c4ab62d71b4d7944c8a785895fb25e0b71d9e1c2e05766719","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T23:00:51.294463Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.00399/citation-record","integrity":"/paper/2606.00399/integrity","json":"/paper/2606.00399/citation-record.json","paper":"/paper/2606.00399"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T23:00:51.294463Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:faa5c123ff43dcfaf1108120724f9575e1d83786e6b79409ebd849aff5911986","observation_id":"9e7a9c75-91c2-42de-b31d-66cff3a6ecc6","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":"Shao and Y","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:22c029df974701278af276a72407d7bb1c1ac07480f553b74a147f88cc486802","observation_id":"55672898-cd84-4660-9a93-0428108a4360","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":"What makes unlearning hard and what to do about it","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:425082b6b4b27319545124bff51e25ff506eca333480196a5755863ca71e32c7","observation_id":"a919e017-431d-4013-b921-37e797c5c793","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:f030316bef73bed5525315c2ce131d8f86b8aee840072fd2232ea4bb5c425d11","observation_id":"e6922965-6efb-4cf5-86fa-39ef7257d2fd","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":"Cheng, Z","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:1bc9c3007b012f3359afc08daa6cf9431cd222d9cae2e702768f38401a9eaae4","observation_id":"185708da-66ef-4d38-8e31-1ce34927581a","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.acl-long.457","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: 10.18653/v1/2024.acl-long.457","venue":null,"work_id":"66e3159c-8da8-417a-9c9c-d6ccf6957f1c","year":2024},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:310897c31b5c985c23327be2b905fcaf3287f545d0b73ebcd90a4f91973942b2","observation_id":"8b8cf086-0ede-41d7-86e0-4579a4cc1d68","resolution":{"observed_at":"2026-06-28T23:02:45.632741Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-15T03:08:21.617312+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-15T03:08:21.617312+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12320","last_updated":"2023-05-21T02:37:26Z","snapshot_observed_at":"2026-08-20T17:00:06.137456Z","submitted_at":"2023-05-21T02:37:26Z","title":"Random Relabeling for Efficient Machine Unlearning","version":1},"cited_work":{"arxiv_id":"2305.12320","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.12320","snapshot_observed_at":"2026-06-28T23:02:46.152715Z","title":"Random relabeling for effi- cient machine unlearning.arXiv preprint arXiv:2305.12320","venue":null,"work_id":"8cc0167c-739e-4334-8585-5bc2d5a39518","year":2023},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"cited_paper":"/paper/2305.12320","citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:0d8d513283a2ecc65367e5362e38f48bddd551543053d61c90e80a09d90d4563","observation_id":"3832a6c4-3113-4473-876c-9d77509624ac","resolution":{"observed_at":"2026-06-28T23:02:46.154369Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-06-28T23:00:51.294463Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:b8ecc46d2f589e1f4b1c63d5741ead60bb6a3114d3288d0d7bd39580f33da8f1","observation_id":"bcf8805c-c4d0-4e61-a6ed-46c9221e94d2","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":"Unrolling sgd: Understanding factors influencing machine unlearning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:1b2bfb56788c88e4ae3016de0cb21cf3d0697ad4993be0e2789f2bd0268db0a9","observation_id":"54f78d98-e5e9-4b80-ad3c-560184d363d3","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":"Eternal sunshine of the spotless net: Selective forget- ting in deep networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:d410214f805d3fbffd8e95bbf62d58e43309b4fd58455f6cda8d5601bb591e85","observation_id":"8a8515bc-8c9b-4480-8bb0-0c19cf0af63a","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11577","last_updated":"2023-08-07T12:33:20Z","snapshot_observed_at":"2026-08-18T17:58:08.799481Z","submitted_at":"2021-08-26T04:42:24Z","title":"Machine Unlearning of Features and Labels","version":4},"cited_work":{"arxiv_id":"2108.11577","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2108.11577","snapshot_observed_at":"2026-07-01T22:36:16.684381Z","title":"Machine unlearning of features and labels","venue":null,"work_id":"150e2d42-7333-40f1-8264-7ac68291258c","year":2021},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"cited_paper":"/paper/2108.11577","citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:7adcd304a2b844c1743aa6d9a8af567a0c4a0fb8682fbe3ec6149d92bb820580","observation_id":"cc3ad69d-2cde-422f-bdf2-29b48ae1f1eb","resolution":{"observed_at":"2026-06-28T23:02:46.150440Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-06-28T23:00:51.294463Z","title":"Approximate data deletion from machine learningmodels","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:32f5de74c874d9ecc8660529e1830ddd6d0cdf2b1d72d1b949eba4d302e2797c","observation_id":"1b994120-16c0-46db-98a5-caefe4f353f4","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.04934","last_updated":"2024-01-27T17:07:46Z","snapshot_observed_at":"2026-08-16T15:41:26.076971Z","submitted_at":"2023-04-11T02:12:02Z","title":"Model Sparsity Can Simplify Machine Unlearning","version":13},"cited_work":{"arxiv_id":"2304.04934","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2304.04934","snapshot_observed_at":"2026-06-28T23:02:46.154205Z","title":"arXiv preprint arXiv:2304.04934 , year=","venue":null,"work_id":"8f76b774-2267-40ab-a6c6-8118032bad7c","year":2023},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"cited_paper":"/paper/2304.04934","citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:ba4cacefd3a5a5b53d68d5e65515073465a4705b5e708630a77769db07a85f90","observation_id":"fc0c54eb-5a23-4790-9981-cf350d5a99cb","resolution":{"observed_at":"2026-06-28T23:02:46.155654Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12508","last_updated":"2024-04-04T07:45:38Z","snapshot_observed_at":"2026-08-03T17:30:25.153454Z","submitted_at":"2023-10-19T06:17:17Z","title":"SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation","version":5},"cited_work":{"arxiv_id":"2310.12508","doi":"10.48550/arxiv.2310.12508","metadata_source":"pith","pith_arxiv_id":"2310.12508","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation","venue":"cs.LG","work_id":"b38d8886-7b2d-4e04-a02e-e7fda5a55db0","year":2023},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"cited_paper":"/paper/2310.12508","citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:c6cd5682bbbb10b6363926f5792f46b445858ce27ac58e1139b820abf5709dce","observation_id":"e98c6e25-4c80-46cb-8355-37e3bd3578ca","resolution":{"observed_at":"2026-06-28T23:02:46.153028Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-06-28T23:00:51.294463Z","title":"Munba: Machine unlearning via nash bargaining","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:43f9e3e96167e191767d9dfa1d81858e64547ea93a8cfbaefa6ad0443163db54","observation_id":"9475ae9e-59fb-4bfd-8543-7981febb367f","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":"Multiple-gradient descent algorithm (MGDA) for multiobjective optimiza- tion","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:457651e209077d2b734f2bc3a349c76627dceac7290c76a3f88cf4617d234590","observation_id":"c8ed2cd1-1ad1-44c9-8840-c9fc87e2898d","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","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":"2026-06-28T23:00:51.294463Z","title":"Multi-task learning as multi-objective optimization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:80182e5cf7cb0cee29b5fc0e9591b29478cc2a8dd28838c43d218c5cf0021276","observation_id":"53d5e9ef-38ce-4453-8fc2-27cec93849af","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16232","last_updated":"2025-02-03T12:29:44Z","snapshot_observed_at":"2026-08-16T13:40:20.191744Z","submitted_at":"2024-06-23T22:06:25Z","title":"Jacobian Descent for Multi-Objective Optimization","version":3},"cited_work":{"arxiv_id":"2406.16232","doi":"10.48550/arxiv.2406.16232","metadata_source":"arxiv_reference","pith_arxiv_id":"2406.16232","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Jacobian descent for multi-objective optimization.arXiv preprint arXiv:2406.16232","venue":"arXiv (Cornell University)","work_id":"4711764f-d344-4191-acbd-4f2e75a07b53","year":2024},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"cited_paper":"/paper/2406.16232","citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:b67b8ce73281539927ad2775e7297cd488d577576e5e3efb8069dea335593c62","observation_id":"1d1fe785-a672-43b7-aec7-a2655ec542b7","resolution":{"observed_at":"2026-06-28T23:02:46.158955Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-06-28T23:00:51.294463Z","title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T23:00:51.294463Z"},"links":{"citing_paper":"/paper/2606.00399"},"observation_digest":"sha256:a978d9869f709105f484bda8f4acf7dc71847e0f55f634edba1db6c056881571","observation_id":"8e57234d-a07e-46f1-8c71-1532ce4bb274","resolution":{"observed_at":"2026-06-28T23:00:51.294463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.00399","last_updated":"2026-05-29T22:32:40Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-01T19:45:29.183659Z","submitted_at":"2026-05-29T22:32:40Z","title":"Multi-Objective Reference-Aligned Machine Unlearning"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":13,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":19},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2606.00399."}