{"as_of":"2026-08-10T04:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2f91d4f588d452bf9f51807908a8f999063a1c157f9ce9bc4f71abf468497a71","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:03:35.851539Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-03T12:25:04.420234Z","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":"2026-05-11T15:36:06.508267Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.00876","snapshot_observed_at":"2026-08-03T12:25:04.420234Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.03190","last_updated":"2026-07-23T19:26:16Z","snapshot_observed_at":"2026-08-09T14:57:40.708124Z","submitted_at":"2026-01-06T17:10:48Z","title":"Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning","version":4},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-03T12:25:04.420234Z"},"links":{"cited_paper":"/paper/2506.00876","citing_paper":"/paper/2601.03190"},"observation_digest":"sha256:1805b46585b15761a1595de0fb94e6038fdf8985749f4d11d456c255c202d293","observation_id":"4289f6ae-dd92-45bb-85f1-24e489d0b85d","resolution":{"observed_at":"2026-08-03T12:25:04.420234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.00876","snapshot_observed_at":"2026-08-03T10:37:03.968826Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.09624","last_updated":"2026-07-27T02:03:28Z","snapshot_observed_at":"2026-08-08T15:45:54.493538Z","submitted_at":"2026-01-14T16:55:58Z","title":"A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-03T10:37:03.968826Z"},"links":{"cited_paper":"/paper/2506.00876","citing_paper":"/paper/2601.09624"},"observation_digest":"sha256:013f01d485979fcd11239608c427038d5cf7ea0464a3096ab044655a0277b799","observation_id":"c75e962f-a083-4e6f-b515-da826d5ca859","resolution":{"observed_at":"2026-08-03T10:37:03.968826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"cited_work":{"arxiv_id":"2506.00876","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.00876","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Not every token needs forgetting: Selective un- learning to limit change in utility in large language model unlearning.arXiv preprint arXiv:2506.00876","venue":null,"work_id":"3f609ef8-be59-46ff-af1e-c70e68a62d71","year":null},"citing_paper":{"arxiv_id":"2605.00364","last_updated":"2026-05-06T12:12:22Z","snapshot_observed_at":"2026-07-06T23:13:47.082550Z","submitted_at":"2026-05-01T02:59:03Z","title":"Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-09T19:39:15.279115Z"},"links":{"cited_paper":"/paper/2506.00876","citing_paper":"/paper/2605.00364"},"observation_digest":"sha256:93d7c8d5e3a7b983eee1def39dffe73328aa3481be9370924472a8670debb0aa","observation_id":"61fb617a-4094-4ae9-bc0a-6e856aa644f3","resolution":{"observed_at":"2026-05-11T15:36:06.514291Z","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":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"cited_work":{"arxiv_id":"2506.00876","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.00876","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Not every token needs forgetting: Selective un- learning to limit change in utility in large language model unlearning.arXiv preprint arXiv:2506.00876","venue":null,"work_id":"3f609ef8-be59-46ff-af1e-c70e68a62d71","year":null},"citing_paper":{"arxiv_id":"2605.01735","last_updated":"2026-05-27T13:05:54Z","snapshot_observed_at":"2026-08-02T07:44:23.046934Z","submitted_at":"2026-05-03T06:20:03Z","title":"Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-10T16:00:56.896665Z"},"links":{"cited_paper":"/paper/2506.00876","citing_paper":"/paper/2605.01735"},"observation_digest":"sha256:8dc7033330753066ff650ec9197c451ef6e607e3a6d0a0be15f38e6c3663b507","observation_id":"8383cf05-cbd9-4bb9-a567-0c77fe1c7064","resolution":{"observed_at":"2026-05-11T09:26:02.744143Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2506.00876/citation-record","integrity":"/paper/2506.00876/integrity","json":"/paper/2506.00876/citation-record.json","paper":"/paper/2506.00876"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:03:32.171502Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:32.171502Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:9f1e40679463dd34b4378720bb903b6afd9d2e6c4e96c4e283f7241af60635aa","observation_id":"57d9e77f-524e-4857-9900-e378640ef209","resolution":{"observed_at":"2026-08-07T12:03:32.171502Z","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":"2026-08-07T12:03:32.320024Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:32.320024Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:56ec2bb16913658d06c944a584f803d3bbca4c0ed4e2fdbc50c6a038c86c4f42","observation_id":"9eda6c2a-785f-46df-a6fd-d88dcd54fe42","resolution":{"observed_at":"2026-08-07T12:03:32.320024Z","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":"2026-08-07T12:03:32.468613Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:32.468613Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:102c85b4c5691b3c610e30abca816701b89c0686aac499caa780a4fbe991bcaf","observation_id":"45f47e06-d0fa-4c4f-809f-1a70a037e1f2","resolution":{"observed_at":"2026-08-07T12:03:32.468613Z","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":"2026-08-07T12:03:32.638710Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:32.638710Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:53997f15ba849930fc2393ecd8c2101903699e79241afe4fb4dfc88dbf4f1b86","observation_id":"9bd112df-c912-4971-bff5-c368a1d73f51","resolution":{"observed_at":"2026-08-07T12:03:32.638710Z","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":"2026-08-07T12:03:38.724903Z","title":"Brown, Vincent J","venue":null,"work_id":"0d922c8e-9e4b-4842-a071-95a6a3dbd6bf","year":1992},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:32.808173Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:56f1093b044fcf7fc6f6e2b35f5477ea70527c2bca913fe03391c555957f0bd6","observation_id":"119107d7-5765-416a-8e3c-21886980e1c5","resolution":{"observed_at":"2026-08-07T12:03:38.857564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2410.22086","last_updated":"2025-05-06T00:58:15Z","snapshot_observed_at":"2026-07-06T19:41:32.754401Z","submitted_at":"2024-10-29T14:41:44Z","title":"Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.22086","snapshot_observed_at":"2026-08-07T12:03:32.972167Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:32.972167Z"},"links":{"cited_paper":"/paper/2410.22086","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:67fcc3e34badd246d8fd92c4fdd5aca299e9a56ff169840ece49e1ab31fadbe7","observation_id":"2e051496-4f74-47e7-b95d-80b90dcd5000","resolution":{"observed_at":"2026-08-07T12:03:32.972167Z","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":"2026-08-07T12:03:33.112544Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:33.112544Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:4a77cb193b6cee38a0d5596f14102dc40ff975a485d1d645ab109fa684accc04","observation_id":"118ec5d4-869c-4633-972e-765a8e5358a0","resolution":{"observed_at":"2026-08-07T12:03:33.112544Z","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":"2026-08-07T12:03:38.436939Z","title":null,"venue":null,"work_id":"6581362f-1086-4f6f-adfd-5bb245539618","year":2023},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:33.287989Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:e61a91aa7f3794d2eb67691eb6adeb673fe6106c043c97585081be16f93c9ade","observation_id":"2b8afc6a-b03c-4dc7-aba1-574fa70df5b3","resolution":{"observed_at":"2026-08-07T12:03:38.521422Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2406.12329","last_updated":"2025-06-06T14:08:20Z","snapshot_observed_at":"2026-07-31T18:03:13.170657Z","submitted_at":"2024-06-18T06:54:05Z","title":"Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12329","snapshot_observed_at":"2026-08-07T12:03:33.449038Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:33.449038Z"},"links":{"cited_paper":"/paper/2406.12329","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:e0c6ef378dbbf104b5bea69d84305f3f5dc74c01bd4cad24edf48e2a58cd175c","observation_id":"e3708281-a51d-418a-ae13-18323c286d61","resolution":{"observed_at":"2026-08-07T12:03:33.449038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.00027","last_updated":"2020-12-31T19:00:10Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-12-31T19:00:10Z","title":"The Pile: An 800GB Dataset of Diverse Text for Language Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00027","snapshot_observed_at":"2026-08-07T12:03:33.632127Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:33.632127Z"},"links":{"cited_paper":"/paper/2101.00027","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:cf12071665f7ee9a2ac33c9068ceed06048b527325abc9d0f3e5c635594da6b7","observation_id":"c17d84ba-5cf3-49dd-87d1-b0dcc1cc218b","resolution":{"observed_at":"2026-08-07T12:03:33.632127Z","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":"2026-08-07T12:03:33.776003Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:33.776003Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:8527af2582715af1182f6f7170a61671e387e14fdff5416074b3df6bf42ed578","observation_id":"fef0d0ee-7731-43d2-8b5f-b8f573d3b3de","resolution":{"observed_at":"2026-08-07T12:03:33.776003Z","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":"2026-08-07T12:03:38.125947Z","title":null,"venue":null,"work_id":"7766ef29-76bd-4755-a50f-a75c56c6975b","year":2023},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:33.930886Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:f65f25a5b3e92c4410646aad89584fb1a82da958b4df0b50985b301078ef312d","observation_id":"cf79885e-da3e-433d-93a1-fe5925d139ac","resolution":{"observed_at":"2026-08-07T12:03:38.228076Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2404.07965","last_updated":"2025-01-08T09:07:54Z","snapshot_observed_at":"2026-08-07T08:00:00.868748Z","submitted_at":"2024-04-11T17:52:01Z","title":"Rho-1: Not All Tokens Are What You Need","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07965","snapshot_observed_at":"2026-08-07T12:03:34.041761Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:34.041761Z"},"links":{"cited_paper":"/paper/2404.07965","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:a23aca66ffdb6ddc86abd3aae89fee52aa8fbc8e5b156cfc8e4a112f07d1a564","observation_id":"877563c6-5bb8-43ae-b7ed-0d986c0eaca0","resolution":{"observed_at":"2026-08-07T12:03:34.041761Z","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":"2026-08-07T12:03:37.808684Z","title":null,"venue":null,"work_id":"fd32cc74-ef80-4587-b1b6-90d44a2a2711","year":2022},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:34.215345Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:d225a4421a1fb0243f209f78598c3eb017613fefa2784bb66f26fdd191e603e5","observation_id":"407b1c31-94f4-425d-b8f0-80a3cd8102f9","resolution":{"observed_at":"2026-08-07T12:03:37.920009Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2406.15796","last_updated":"2025-05-19T13:54:52Z","snapshot_observed_at":"2026-08-03T23:19:30.629876Z","submitted_at":"2024-06-22T09:40:07Z","title":"Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis","version":6},"cited_work":{"arxiv_id":"2406.15796","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.15796","snapshot_observed_at":"2026-08-07T12:03:37.186138Z","title":"Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis","venue":"cs.CL","work_id":"60cefda3-fea7-4d9c-81c2-665d48dde302","year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:34.377202Z"},"links":{"cited_paper":"/paper/2406.15796","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:3702c9ad715b563556184f6e9c453cdafa689166e7705d23e863e0e9f024877b","observation_id":"390c6e2c-f5e1-44dc-be95-2b5d555c7417","resolution":{"observed_at":"2026-08-07T12:03:37.297649Z","resolver_source":"local_arxiv","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":"2401.06121","last_updated":"2024-01-11T18:57:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-01-11T18:57:12Z","title":"TOFU: A Task of Fictitious Unlearning for LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.06121","snapshot_observed_at":"2026-08-07T12:03:34.514225Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:34.514225Z"},"links":{"cited_paper":"/paper/2401.06121","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:2db9054eeafa3f2f672c3c97a27c56d61ff8302a974cde4952111b5b9ca7b083","observation_id":"416bf7ae-c338-4bf2-a155-a64fee593d5a","resolution":{"observed_at":"2026-08-07T12:03:34.514225Z","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":"3477.74163","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:03:36.738304Z","title":null,"venue":null,"work_id":"15713613-e651-4721-8f01-9d9d1e306ff5","year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:34.674395Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:2ddfe0b20fec01497ab2572f3c65abee172aff57af172fe3ed5521c502221e02","observation_id":"12434431-29d5-480a-9cab-99e075bac78a","resolution":{"observed_at":"2026-08-07T12:03:36.884611Z","resolver_source":"raw_fallback","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:03:34.895925Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:34.895925Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:2b87f2706e9d9a2f834f33b2beb6bab7611ae6db30791b5bf1d537d26b4ef16a","observation_id":"0b03b9f9-c15b-4943-9a5b-0a77508f22bc","resolution":{"observed_at":"2026-08-07T12:03:34.895925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.15097","last_updated":"2025-02-27T04:41:34Z","snapshot_observed_at":"2026-08-07T18:00:25.196118Z","submitted_at":"2025-02-20T23:30:45Z","title":"LUME: LLM Unlearning with Multitask Evaluations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.15097","snapshot_observed_at":"2026-08-07T12:03:35.075615Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:35.075615Z"},"links":{"cited_paper":"/paper/2502.15097","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:b0f0506d6a353fdd62d5b0fff6d99293a95397a78aff14cb9df27713d1384387","observation_id":"11e1a793-6c1a-425b-a962-f257c446a40d","resolution":{"observed_at":"2026-08-07T12:03:35.075615Z","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":"2026-08-07T12:03:35.190341Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:35.190341Z"},"links":{"citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:660d767190f372140b22e7075c6f0d80f1f5bd4ab766b617beba85ee46c5b81a","observation_id":"568e061d-0e35-49cf-8798-a8f7a13b43c5","resolution":{"observed_at":"2026-08-07T12:03:35.190341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06460","last_updated":"2024-07-14T20:14:02Z","snapshot_observed_at":"2026-07-06T18:43:22.614883Z","submitted_at":"2024-07-08T23:47:29Z","title":"MUSE: Machine Unlearning Six-Way Evaluation for Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06460","snapshot_observed_at":"2026-08-07T12:03:35.340707Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:35.340707Z"},"links":{"cited_paper":"/paper/2407.06460","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:1fdacfc2ed33bc0d63973138fd1e50f4a3238afb5a08658d2f56fb856c20469b","observation_id":"a61dfad5-5159-4e1a-b8c3-72049051dcfa","resolution":{"observed_at":"2026-08-07T12:03:35.340707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01983","last_updated":"2024-06-04T05:51:43Z","snapshot_observed_at":"2026-08-02T23:42:41.924782Z","submitted_at":"2024-06-04T05:51:43Z","title":"RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01983","snapshot_observed_at":"2026-08-07T12:03:35.509909Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:35.509909Z"},"links":{"cited_paper":"/paper/2406.01983","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:88b5102fc9c388de87aad55b9def4ea43b67d512ec96e14c43f6514035936e14","observation_id":"4298e1f2-c6bb-4262-b3e7-944768ba0f0c","resolution":{"observed_at":"2026-08-07T12:03:35.509909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05813","last_updated":"2024-12-16T12:44:07Z","snapshot_observed_at":"2026-08-10T00:59:19.370107Z","submitted_at":"2024-02-08T16:50:01Z","title":"Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models","version":2},"cited_work":{"arxiv_id":"2402.05813","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.05813","snapshot_observed_at":"2026-08-07T12:03:36.144827Z","title":"Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models","venue":"cs.CL","work_id":"3fa751ff-9051-4062-99a9-d03ff137cf54","year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:35.571000Z"},"links":{"cited_paper":"/paper/2402.05813","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:cc74c90fb54dc03d3885b683eee3c991085a0bb0f3872d793fee178c33b8abf0","observation_id":"f649b722-3f5e-4160-bf65-76d2b5ab2be7","resolution":{"observed_at":"2026-08-07T12:03:36.261774Z","resolver_source":"local_arxiv","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":"2310.10683","last_updated":"2024-02-16T19:47:36Z","snapshot_observed_at":"2026-08-04T08:36:17.664739Z","submitted_at":"2023-10-14T00:32:55Z","title":"Large Language Model Unlearning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10683","snapshot_observed_at":"2026-08-07T12:03:35.694158Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:35.694158Z"},"links":{"cited_paper":"/paper/2310.10683","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:77f70c2094631c0ee5078ed207f516a4da5b817941945f20b2e9ab6adc1ba7ee","observation_id":"ee9df450-9d8c-4548-9f14-aa4eba851817","resolution":{"observed_at":"2026-08-07T12:03:35.694158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05868","last_updated":"2024-10-10T22:00:41Z","snapshot_observed_at":"2026-07-06T17:57:27.510162Z","submitted_at":"2024-04-08T21:05:42Z","title":"Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05868","snapshot_observed_at":"2026-08-07T12:03:35.851539Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T12:03:35.851539Z"},"links":{"cited_paper":"/paper/2404.05868","citing_paper":"/paper/2506.00876"},"observation_digest":"sha256:4ddca1d3867c0df2457e4a08a9b68e95503b3f664439ee9d50da5a594e860c1e","observation_id":"41b80684-ee97-44c7-94d3-3bea6540e079","resolution":{"observed_at":"2026-08-07T12:03:35.851539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.00876","last_updated":"2025-06-01T07:36:45Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T21:31:55.238749Z","submitted_at":"2025-06-01T07:36:45Z","title":"Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":21,"verified_exact":2,"verified_fuzzy":1},"total_outbound_references":25},"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 25 of 25 outbound references and 4 inbound Pith citation observations for arXiv:2506.00876."}