{"as_of":"2026-08-21T15:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4ec59f00cfe616a29f4e44ec6aff445d4bf69bed59b3ce546fae833696c7623c","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:50:58.795919Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2506.18747/citation-record","integrity":"/paper/2506.18747/integrity","json":"/paper/2506.18747/citation-record.json","paper":"/paper/2506.18747"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:59.053547Z","title":"safe completion","venue":null,"work_id":"db5a881e-2760-4ace-97ea-2c9c720859e0","year":2023},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.762866Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:bf8d2e36ffd9cad219b0c90b063012de4953c6b316c3f6df331eb7ce01d2b4e8","observation_id":"9d9e5440-af13-4885-b4f4-5659dda161be","resolution":{"observed_at":"2026-08-15T18:50:59.058153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:59.026857Z","title":"Flow Matching Toward Soft Mass-Subtracted Distributions","venue":null,"work_id":"1d0a3075-2495-4447-995b-00e5a3373d93","year":2025},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.770751Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:75e27f523a5d92631ab8c6b422c301a13e13ae946f7c24033641bae0e752a677","observation_id":"2210aac9-a33d-4179-916a-c3e53f533501","resolution":{"observed_at":"2026-08-15T18:50:59.031266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:58.958201Z","title":"Generation from the full distribution (left), and after suppressing all classes except automobile (middle) and airplane (right), which are assigned low energy","venue":null,"work_id":"557ca11c-5532-45cc-b60a-b3621c06ae76","year":2025},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.791902Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:119d2d221978bac34770473a533217e930118693a8784cb7c565aa9889dba9f3","observation_id":"6d90708b-6766-43c1-a8f1-bf04ff0cdc98","resolution":{"observed_at":"2026-08-15T18:50:58.962518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11190","last_updated":"2025-05-28T16:57:30Z","snapshot_observed_at":"2026-08-16T12:57:58.989445Z","submitted_at":"2025-02-16T16:31:00Z","title":"ReLearn: Unlearning via Learning for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11190","snapshot_observed_at":"2026-08-15T18:50:58.754179Z","title":"doi: 10.18653/v1/2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.754179Z"},"links":{"cited_paper":"/paper/2502.11190","citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:f238feb5f1212069957d70d5674eb0c59682537f9e4acf5d9b1426b9122e5982","observation_id":"c8263a1d-8bc7-4d51-a34a-a051e8649b12","resolution":{"observed_at":"2026-08-15T18:50:58.754179Z","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":"2026-08-15T18:50:58.972652Z","title":"0”, andDforget includes digits “1–9","venue":null,"work_id":"b756dee3-f4be-438f-a037-14f19a6ba485","year":2025},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.787908Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:6d0beeb787713de43ac1eb569c9894bdba61c68c3735268bb5bb0f468e0181a3","observation_id":"53ecae73-e7cc-4e9c-ba89-0e4e5738b062","resolution":{"observed_at":"2026-08-15T18:50:58.978083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:59.012634Z","title":null,"venue":null,"work_id":"471abfe4-a789-412a-9dee-aada114028a5","year":2021},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.775739Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:1c7da0352fd6500579f356368f7dcefad7c8842edba0d0d7017fdc009399dfd4","observation_id":"06eb6d7d-1da8-46bd-82d4-9b8c09c6ad02","resolution":{"observed_at":"2026-08-15T18:50:59.017035Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:59.000382Z","title":"(8) OT-CFM Justification","venue":null,"work_id":"93385b0d-78e7-444e-bf9d-995748bf078e","year":2023},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.779943Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:3f3a78c3e6508a15d8d49db8f0f8d4d0fc8a43eb025e56c6ef3508f9d23d66fb","observation_id":"73e866cf-4c84-4bfa-a355-b6bdc91d934c","resolution":{"observed_at":"2026-08-15T18:50:59.004183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:58.943342Z","title":"The right panel illustrates the learned and unlearned trajectories for four 2D benchmarks: Circles, Moons, 6 Gaussians, and Checkerboard","venue":null,"work_id":"fb4b7dc9-eb26-4369-bee7-1eb1600012d8","year":2025},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.795919Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:b7bfdb00b5481e89800f84a188029848528abc7b952cf105279f0feec450ca2e","observation_id":"bbbce51d-1bb8-41b2-85ee-2a9b0be627b6","resolution":{"observed_at":"2026-08-15T18:50:58.949327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:50:58.735917Z","title":"F., Choquette-Choo, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.735917Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:a254d83b8a72effe5fe7cce943f68d31e0c57e81b59090d9aab0cb8d9306322c","observation_id":"debf5e72-b871-4c2c-8f25-208c90db96cd","resolution":{"observed_at":"2026-08-15T18:50:58.735917Z","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":"2026-08-15T18:50:59.078224Z","title":"DEPN: Detecting and editing privacy neurons in pretrained language models","venue":null,"work_id":"f381f46f-2da4-45c3-87a1-8c6655608b22","year":2023},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.749644Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:bf8d906d5c98642b4efff40c60cad21e8fb49ff89680e32f48db575212b5fe2b","observation_id":"d3094703-de4e-4eb0-b1e1-ee3eda30069d","resolution":{"observed_at":"2026-08-15T18:50:59.083327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:59.039272Z","title":"However, these methods typically address fixed, one-shot unlearning tasks and rely on access to the data to be removed","venue":null,"work_id":"9c3e0b44-c6dc-471c-b7e7-50fa5c6883b8","year":2025},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.766984Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:501f384fff33cd9263aa22a54a3e04c94b5e043a08c77bdf404fcbb16609ed77","observation_id":"69515aa7-9060-4a45-9c74-bba3a9bf6723","resolution":{"observed_at":"2026-08-15T18:50:59.045109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:50:58.987308Z","title":"Empirically, this yields competitive flows and maintains convergence benefits without incurring the overhead of solving full OT across the dataset","venue":null,"work_id":"c17dc69d-ba8e-435f-afff-e421271c8cbb","year":2025},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.783698Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:c61ac01e32dd1012f56fc560aa9d2620e231605abb3b108370103e21c0ff728e","observation_id":"bc59c30b-7e0c-4507-acf4-27f4164606a9","resolution":{"observed_at":"2026-08-15T18:50:58.992064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.13769","last_updated":"2025-03-21T21:36:49Z","snapshot_observed_at":"2026-08-16T12:49:10.491870Z","submitted_at":"2025-03-17T23:17:16Z","title":"Continual Unlearning for Foundational Text-to-Image Models without Generalization Erosion","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.13769","snapshot_observed_at":"2026-08-15T18:50:58.740399Z","title":"Continual unlearning for foundational text-to-image models without generalization erosion","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.740399Z"},"links":{"cited_paper":"/paper/2503.13769","citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:2ffb7a3d4d8f0e38457d25bb75ef8096862bf8e2a308d53f2064cf8468fc1441","observation_id":"a689d407-a64f-4bbe-9208-b03cd73ff375","resolution":{"observed_at":"2026-08-15T18:50:58.740399Z","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":"2026-08-15T18:50:59.066000Z","title":null,"venue":null,"work_id":"55c18dbb-bc82-4303-a54e-8fef2b6fad38","year":2025},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.758501Z"},"links":{"citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:74081c4b05be66fdf80534a85ceccf4c383411b4245b8e8b2fcd1676513a321f","observation_id":"b5fba5ee-856a-4004-af3a-e3ac1c5571dc","resolution":{"observed_at":"2026-08-15T18:50:59.069979Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.00482","last_updated":"2024-03-11T14:27:48Z","snapshot_observed_at":"2026-08-17T02:46:52.486829Z","submitted_at":"2023-02-01T14:47:17Z","title":"Improving and generalizing flow-based generative models with minibatch optimal transport","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.00482","snapshot_observed_at":"2026-08-15T18:50:58.745084Z","title":"Improving and generalizing flow-based generative models with mini- batch optimal transport","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T18:50:58.745084Z"},"links":{"cited_paper":"/paper/2302.00482","citing_paper":"/paper/2506.18747"},"observation_digest":"sha256:bde230182899720a8f41a6c43c8a7eca5fe644904c8f2afd9e52f05ed6240fd0","observation_id":"289ee234-c5c4-4fec-a9a7-0cff63af70ea","resolution":{"observed_at":"2026-08-15T18:50:58.745084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.18747","last_updated":"2025-06-23T15:20:58Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T18:41:36.197548Z","submitted_at":"2025-06-23T15:20:58Z","title":"ContinualFlow: Learning and Unlearning with Neural Flow Matching"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":15},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.18747."}