{"as_of":"2026-08-10T12:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:33b8b18aacd4f67d480800f44bc89f64a8d2e2877dd35377c73787439cdf29bd","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T19:04:56.880181Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2508.13653/citation-record","integrity":"/paper/2508.13653/integrity","json":"/paper/2508.13653/citation-record.json","paper":"/paper/2508.13653"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.07075","last_updated":"2023-03-28T13:51:14Z","snapshot_observed_at":"2026-08-09T20:08:32.231151Z","submitted_at":"2021-07-15T02:12:20Z","title":"Deep Learning on a Data Diet: Finding Important Examples Early in Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07075","snapshot_observed_at":"2026-08-05T19:04:53.951203Z","title":"Deep Learning on a Data Diet: Finding Important Examples Early in Training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:53.951203Z"},"links":{"cited_paper":"/paper/2107.07075","citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:c1074d06ed69e9c420ff821777e143e9221d28c13c17ce079ea7bebc0624524b","observation_id":"91c61dae-04be-4e7b-8bcf-15ce4ceec057","resolution":{"observed_at":"2026-08-05T19:04:53.951203Z","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":"2026-08-05T19:05:03.157927Z","title":"An empirical study of example forgetting during deep neural network learning","venue":null,"work_id":"c5215647-e1c0-4289-9a41-4c36917df148","year":2018},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.037418Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:f857796bbf84009fba3ae0dec4b091c208a60069af927f0aaeb966cc8b11fc15","observation_id":"b8afbe19-a35b-4ef9-a3b5-a30c0df036fe","resolution":{"observed_at":"2026-08-05T19:05:03.233364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:02.950806Z","title":"Selection via proxy: Efficient data selection for deep learning","venue":null,"work_id":"2a947f15-41de-4d13-8c9b-8cc25706874a","year":2020},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.118318Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:185889f9a01ffb8774a603b4ed0ed403af3d9af6cfb008e4151d254475dbda49","observation_id":"fb79dc72-ab6e-413a-8de6-c757ed73f8a4","resolution":{"observed_at":"2026-08-05T19:05:03.021197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:02.841965Z","title":"DRoP: Distributionally Robust Data Pruning","venue":null,"work_id":"6f941e79-785b-4a82-8dab-999419b1bd21","year":2025},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.238143Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:e58013781b783ac61e882a31e47db3216d55c871e44b3c8afb4e6413dec9a827","observation_id":"fa5913ef-f394-4b06-a79b-e130895b8842","resolution":{"observed_at":"2026-08-05T19:05:02.903054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:02.613120Z","title":"GradMatch: Gradient Matching Based Data Subset Selection for Efficient Deep Model Training","venue":null,"work_id":"ba907486-c68a-468c-b478-ab3fff4d9866","year":2021},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.431796Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:007e487c5aa61049ac46c2eebc712b30147cfa4198571cb8f06b0c86fb425245","observation_id":"dbaf04df-bbb2-4789-9bf5-44b0e2b896bc","resolution":{"observed_at":"2026-08-05T19:05:02.725052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:02.335273Z","title":"Glister: Generalization based data subset selection for efficient and robust learning","venue":null,"work_id":"c74ba804-0c08-47dc-ad8e-50dd67fdf01f","year":2021},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.508698Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:3d939a08b9148ddbcf8ddeeed2b5d8b3262370880bdc300a35f745c0fbc5fac5","observation_id":"f7027ed6-ed93-4b37-ab18-43d3c7164bc5","resolution":{"observed_at":"2026-08-05T19:05:02.462879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:02.079201Z","title":"Coresets for Data-efficient Training of Machine Learning Models","venue":null,"work_id":"f891337d-36d6-43b4-bf54-2959e5368a53","year":2020},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.588672Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:3a802caf2f0133749d798aae5c9a563b54f2a6530fcf8db9576abe20f3f7fc7a","observation_id":"88269f93-e9fa-4784-9116-64f27edd7ac2","resolution":{"observed_at":"2026-08-05T19:05:02.178808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:01.810036Z","title":"Deep batch active learning by diverse, uncertain gradient lower bounds","venue":null,"work_id":"3fc38101-6666-463e-b91e-ac6c28c7cdf3","year":2020},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.693951Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:2527a93419b6677ea0a59660bdc7d254b2a6c560c04617baded0b22df27d4e11","observation_id":"2f4ec392-be2c-4f3f-bae9-a620529377ef","resolution":{"observed_at":"2026-08-05T19:05:01.955558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:01.526016Z","title":"Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning","venue":null,"work_id":"f451f043-334d-4b50-9cd7-7487adb8a989","year":2023},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.719827Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:115d289fe0132927b48377cae663f7171f64ec4cef71e496ede350206362e254","observation_id":"684c8a30-2688-44a1-b431-b58bfc5d7b37","resolution":{"observed_at":"2026-08-05T19:05:01.638165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10963","last_updated":"2022-09-02T07:23:22Z","snapshot_observed_at":"2026-08-06T08:05:22.548234Z","submitted_at":"2021-12-21T03:38:58Z","title":"DRPN: Making CNN Dynamically Handle Scale Variation","version":2},"cited_work":{"arxiv_id":"2112.10963","doi":null,"metadata_source":"pith","pith_arxiv_id":"2112.10963","snapshot_observed_at":"2026-08-05T19:04:57.210294Z","title":"DRPN: Making CNN Dynamically Handle Scale Variation","venue":"cs.CV","work_id":"b16d3ab2-b820-43b9-ab67-1633043c0191","year":2021},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.794796Z"},"links":{"cited_paper":"/paper/2112.10963","citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:917966a03fe1d41eaa47d0d6e080a115a35d5458cfcc4f9c911993ea3257582c","observation_id":"43a77f81-96c6-4259-b02c-2a0a7375f4de","resolution":{"observed_at":"2026-08-05T19:04:57.283146Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:01.268682Z","title":"SelMatch: Selection-based Dataset Distillation via Trajectory Matching","venue":null,"work_id":"49a0a4f2-0ca3-4ab2-b305-8df43c0f6feb","year":2024},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.904445Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:d7874e9465a4eb97cf0e70843d8d7da0397693c88c7b6e0464f9dddcf3230a37","observation_id":"c8b41643-d0c7-4aa9-9974-116627620aab","resolution":{"observed_at":"2026-08-05T19:05:01.418294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:01.000477Z","title":"Efficient data subset selection to generalize training across models: Transductive and inductive networks","venue":null,"work_id":"e6bb66dc-3d69-4762-8fd5-1f3e97df9d2e","year":2023},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.929020Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:2dc377d7a4c8ccfda2c547d8ca377ed591abbd69f27c782066776e23e2abf0cb","observation_id":"15fc38bf-d399-4fe8-8441-f700c5d05803","resolution":{"observed_at":"2026-08-05T19:05:01.131737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:00.734128Z","title":"Column subset selection and Nyström approximation via continuous optimization","venue":null,"work_id":"33c52fde-f517-471e-9c6e-1fbe0aec22c6","year":2023},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:54.969682Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:500f6c41944fa1db4e6b099d7ec9a2797857508bfc12f741eca916b501ae4b7d","observation_id":"033fb48d-2caa-45f0-ad68-97274f938ac0","resolution":{"observed_at":"2026-08-05T19:05:00.856223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:00.471244Z","title":"Incomplete cross approximation in the mosaic-skeleton method","venue":null,"work_id":"2f392b21-55b4-45a8-b798-b3ffc51304a9","year":2000},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.018529Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:9e7d4b78a07e8c94e37bcf77d81112ab0043b12dfcd2867ca9498881a1d5a44c","observation_id":"20992de4-c0e8-439d-9224-86e3d7bb0f68","resolution":{"observed_at":"2026-08-05T19:05:00.571185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:05:00.209405Z","title":"How to find a good submatrix","venue":null,"work_id":"c1de7d13-398f-46d6-a70a-0b6a4386ec40","year":2010},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.044056Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:0e54c583ea76dbf4a1ed6eb863d64fa922854790e14d81bf94631e7911ab1ae1","observation_id":"594c6a59-9999-478d-8a68-9e01029ad52a","resolution":{"observed_at":"2026-08-05T19:05:00.316821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:59.894427Z","title":"Optimization for machine learning","venue":null,"work_id":"ddc5250d-fc73-4411-8f80-0bccef726ca6","year":2018},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.091708Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:38cc338fb5a65e00cf8edd66bb1caaacb971b9d0bec5e800b516cbf3b09acf61","observation_id":"4f2e686e-3fa7-4c74-b765-0fb82506c359","resolution":{"observed_at":"2026-08-05T19:05:00.017992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:59.634751Z","title":"Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions","venue":null,"work_id":"ac88d8a3-95a9-441f-a91a-18db2246c3a1","year":2011},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.208549Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:66eb5040663be1026a55e85489e9248f6bb15b25b970b2b0b5c7e811abd370d9","observation_id":"b89e93be-f9aa-4bdf-acff-b599d62737cd","resolution":{"observed_at":"2026-08-05T19:04:59.769883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:59.328423Z","title":"Matrix Computations","venue":null,"work_id":"d0592aa8-4db8-4869-bdbe-b471d933e59c","year":2013},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.349479Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:b1ab6df6bf65ba9e07e312d033455d273d1f7797009779c0b416507c0a4c1d2b","observation_id":"e02076dd-e625-4cd1-a024-d2e4e217956a","resolution":{"observed_at":"2026-08-05T19:04:59.435703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:59.108978Z","title":"Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai","venue":null,"work_id":"5bd2f932-aaea-4d0c-93c1-f6c520474d9f","year":2022},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.470178Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:6bdf5bbe5f848766e324aa35102ff16fa428279d0134800cd97c793cdb47e3f6","observation_id":"2a736fa4-17f4-4d7e-a3f3-b6b3df39abf6","resolution":{"observed_at":"2026-08-05T19:04:59.216184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:58.929538Z","title":"CIFAR-10 and CIFAR-100 Datasets","venue":null,"work_id":"dd098f05-ad51-4988-a6a8-abdb0b971f46","year":2024},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.535163Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:a4c5722ef57c1d7bf1611f3141f8181e2991ea95b3b2d4f1813893b5c0694a22","observation_id":"e5bea3a7-41ce-4acb-a0b2-186c77c0d0e9","resolution":{"observed_at":"2026-08-05T19:04:59.016811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:58.778055Z","title":"Tiny ImageNet Visual Recognition Challenge","venue":null,"work_id":"89c74a6c-10ca-4b5d-a35c-d1071cc19ded","year":2025},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.641571Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:035723c30f501418add0a96cc6a2e5d2f3895506a0e8b6ab6a81b9c83dc5deae","observation_id":"4f47729b-f796-43d9-8921-739917c704bb","resolution":{"observed_at":"2026-08-05T19:04:58.852293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:58.594893Z","title":"Caltech-256 Object Category Dataset","venue":null,"work_id":"89eca63d-94b1-41a3-ab0a-274af3defdc6","year":2025},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.780385Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:eaa1fa3188562e1837814c833affe147602244278ea6729b3bf7a638b791da1b","observation_id":"cb857d33-a4a6-4034-9a48-f0ac461c39f1","resolution":{"observed_at":"2026-08-05T19:04:58.673429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:58.357671Z","title":"Teneva: A Fast and Efficient Tensor Decomposition Library","venue":null,"work_id":"5c8a799b-c505-4617-8c34-501c2f77864f","year":2025},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:55.903778Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:3b2d66b4be01c6943bbe593ac84301fbf9ee10bd10ba3446ce0db83dbae45828","observation_id":"ec10852a-e7ae-4ef9-985f-37a7a8470a4e","resolution":{"observed_at":"2026-08-05T19:04:58.487510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:56.123704Z","title":"The Use of Multiple Measurements in Taxonomic Problems","venue":null,"work_id":null,"year":1936},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.123704Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:ae87a3431b3cb36d5f44dc924097b62f18b717c5af1e81461f3748057d79b811","observation_id":"889ba289-9e07-48f6-9cab-c9e568b62d7b","resolution":{"observed_at":"2026-08-05T19:04:56.123704Z","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":"2026-08-05T19:04:58.191796Z","title":"Aggregated residual transformations for deep neural networks","venue":null,"work_id":"b8e2ea85-7348-4ac9-848e-1d22bdbc8d58","year":2017},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.201255Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:1536401d1e200f66641047c307c6e2a6e2dd2451f1687b1cf916493c506fb4e4","observation_id":"ea966a0a-d53e-4082-a610-e75e6bed996d","resolution":{"observed_at":"2026-08-05T19:04:58.263912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:57.995718Z","title":"Krizhevsky, G","venue":null,"work_id":"0bf0a292-ef37-46ac-a407-7699097de883","year":2009},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.339118Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:58ce4462ecad76a79a3c6c4f1772fa13c917e9be04de939e718c78509c5c653f","observation_id":"456d9d76-c4fb-44fd-a9c3-6ee8d722da75","resolution":{"observed_at":"2026-08-05T19:04:58.080801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:57.850412Z","title":"Visualizing the loss landscape of neural nets","venue":null,"work_id":"4743e1c6-f625-4e01-adcc-e0860f612f3e","year":2018},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.479184Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:3330adc6fb91c512cb86352539d8222218c65f66f68b21c0db0098a2fd637f2f","observation_id":"4af23ff4-00bc-4232-8f55-24291c3ce7e3","resolution":{"observed_at":"2026-08-05T19:04:57.920970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:57.719640Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks","venue":null,"work_id":"e5d1bd65-bbf9-4809-9326-2846d896e295","year":2018},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.572645Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:e4032cc40cebcf69672f162b66af1d4511f9fa0305e30b9923411b9dabbb9027","observation_id":"d3af463a-d208-49fa-b551-2ef5785537dc","resolution":{"observed_at":"2026-08-05T19:04:57.789367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:56.638800Z","title":"Chapter 5 - Text Mining Methodology","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.638800Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:236514bb80390d5a0ef9e0f81f856513b55a580d88a621ad524fed64ee928e72","observation_id":"dc7d475f-c892-47f5-a480-0e852ddca022","resolution":{"observed_at":"2026-08-05T19:04:56.638800Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-05T19:04:57.548137Z","title":"A review of algorithms for SAW sensors e-nose based volatile compound identification","venue":null,"work_id":"64196b59-a155-4b1a-9173-46e298d8dd47","year":2018},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.772858Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:344ce16988baf36f1d02a8a35aafed62f7a2115fedefdb431f1d9dc18c505e7b","observation_id":"c517e98d-2964-42fa-8b99-ec3b2f7a1278","resolution":{"observed_at":"2026-08-05T19:04:57.655527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T19:04:57.401941Z","title":"Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics","venue":null,"work_id":"fcbb2cec-ff25-46ac-bde0-b80e5188ffcb","year":2011},"citing_paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T19:04:56.880181Z"},"links":{"citing_paper":"/paper/2508.13653"},"observation_digest":"sha256:7fee819a0a81b987933b36ed4fffdd1946a5ed6d58037abbb3e7e60c7f518f27","observation_id":"549a8fdb-a70c-4a97-b32f-f300bb45485f","resolution":{"observed_at":"2026-08-05T19:04:57.456076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.13653","last_updated":"2025-08-22T14:54:58Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T03:55:42.252996Z","submitted_at":"2025-08-19T09:03:39Z","title":"GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":31},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2508.13653."}