{"as_of":"2026-08-07T18:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53c3c1848248c6aabb6b8bbc48b4654d1af8073efc7408b8bf06178cf534b9b7","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:52:49.697076Z","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":"2026-05-19T17:22:42.031415Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.00123","last_updated":"2021-06-11T22:08:29Z","snapshot_observed_at":"2026-07-06T10:45:06.227629Z","submitted_at":"2021-02-27T04:09:32Z","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00123","snapshot_observed_at":"2026-08-07T10:52:49.697076Z","title":"Grad-match: A gradient matching based data subset selection for efficient learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.04205","last_updated":"2025-06-04T17:49:10Z","snapshot_observed_at":"2026-08-07T10:43:17.329577Z","submitted_at":"2025-06-04T17:49:10Z","title":"EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T10:52:49.697076Z"},"links":{"cited_paper":"/paper/2103.00123","citing_paper":"/paper/2506.04205"},"observation_digest":"sha256:bdf74b2c6dd6d7779f7c890a375888745c52442869a3fabeccb3ca86e86503f8","observation_id":"b0d60cd4-46fd-45a8-a180-5172c1066f19","resolution":{"observed_at":"2026-08-07T10:52:49.697076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00123","last_updated":"2021-06-11T22:08:29Z","snapshot_observed_at":"2026-07-06T10:45:06.227629Z","submitted_at":"2021-02-27T04:09:32Z","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00123","snapshot_observed_at":"2026-08-06T19:50:41.280850Z","title":"Grad-match: Gradient matching based data subset selection for efficient deep model training,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.04529","last_updated":"2025-07-06T20:46:19Z","snapshot_observed_at":"2026-08-06T19:43:09.233743Z","submitted_at":"2025-07-06T20:46:19Z","title":"A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:50:41.280850Z"},"links":{"cited_paper":"/paper/2103.00123","citing_paper":"/paper/2507.04529"},"observation_digest":"sha256:32c3a714be3fc839009ba71be91102abfb1495f2e6b4bb9904b66039147d47ac","observation_id":"9a518858-a07e-4d6f-b6c0-8a188b7794c5","resolution":{"observed_at":"2026-08-06T19:50:41.280850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00123","last_updated":"2021-06-11T22:08:29Z","snapshot_observed_at":"2026-07-06T10:45:06.227629Z","submitted_at":"2021-02-27T04:09:32Z","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00123","snapshot_observed_at":"2026-08-06T15:55:29.254637Z","title":"Grad-match: Gradient matching based data subset selection for efficient deep model training, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14725","last_updated":"2026-06-09T12:16:28Z","snapshot_observed_at":"2026-08-06T15:47:00.939567Z","submitted_at":"2025-07-19T19:15:03Z","title":"GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T15:55:29.254637Z"},"links":{"cited_paper":"/paper/2103.00123","citing_paper":"/paper/2507.14725"},"observation_digest":"sha256:e6707482c0e09a388838ff45c7a292f9798e52f2129aa0695d17cdb562312110","observation_id":"15fe70e1-9b0a-42c3-b6a3-5c3bd9d7ee43","resolution":{"observed_at":"2026-08-06T15:55:29.254637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00123","last_updated":"2021-06-11T22:08:29Z","snapshot_observed_at":"2026-07-06T10:45:06.227629Z","submitted_at":"2021-02-27T04:09:32Z","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training","version":2},"cited_work":{"arxiv_id":"2103.00123","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.00123","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"13 Alex Krizhevsky","venue":null,"work_id":"03ffa4b8-a9f4-4bdb-90f8-9fbf897a8f0a","year":2021},"citing_paper":{"arxiv_id":"2605.02609","last_updated":"2026-05-15T17:20:55Z","snapshot_observed_at":"2026-08-02T11:38:00.584240Z","submitted_at":"2026-05-04T13:56:09Z","title":"Gradient-Discrepancy Acquisition for Pool-Based Active Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-08T18:45:42.032855Z"},"links":{"cited_paper":"/paper/2103.00123","citing_paper":"/paper/2605.02609"},"observation_digest":"sha256:771bae64bb4ca5ef52aa5de29e4fd4b68ec39cd460751ecade68d37bab2c165c","observation_id":"b17e60a6-114a-441c-a66f-294273ba2b14","resolution":{"observed_at":"2026-05-09T06:15:37.326773Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00123","last_updated":"2021-06-11T22:08:29Z","snapshot_observed_at":"2026-07-06T10:45:06.227629Z","submitted_at":"2021-02-27T04:09:32Z","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training","version":2},"cited_work":{"arxiv_id":"2103.00123","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.00123","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"13 Alex Krizhevsky","venue":null,"work_id":"03ffa4b8-a9f4-4bdb-90f8-9fbf897a8f0a","year":2021},"citing_paper":{"arxiv_id":"2605.02609","last_updated":"2026-05-15T17:20:55Z","snapshot_observed_at":"2026-08-02T11:38:00.584240Z","submitted_at":"2026-05-04T13:56:09Z","title":"Gradient-Discrepancy Acquisition for Pool-Based Active Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-19T17:19:30.778377Z"},"links":{"cited_paper":"/paper/2103.00123","citing_paper":"/paper/2605.02609"},"observation_digest":"sha256:0597774d5246bd81b86ed8f928e8594901d1c6dbbf057bc63f7c1e56dfa730e8","observation_id":"4f1b6644-186d-4566-8c15-8905bf1129c8","resolution":{"observed_at":"2026-05-19T17:22:42.033265Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00123","last_updated":"2021-06-11T22:08:29Z","snapshot_observed_at":"2026-07-06T10:45:06.227629Z","submitted_at":"2021-02-27T04:09:32Z","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training","version":2},"cited_work":{"arxiv_id":"2103.00123","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2103.00123","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"13 Alex Krizhevsky","venue":null,"work_id":"03ffa4b8-a9f4-4bdb-90f8-9fbf897a8f0a","year":2021},"citing_paper":{"arxiv_id":"2605.09404","last_updated":"2026-05-10T08:07:09Z","snapshot_observed_at":"2026-07-06T23:21:30.897592Z","submitted_at":"2026-05-10T08:07:09Z","title":"Let the Target Select for Itself: Data Selection via Target-Aligned Paths","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-12T02:47:55.649231Z"},"links":{"cited_paper":"/paper/2103.00123","citing_paper":"/paper/2605.09404"},"observation_digest":"sha256:bf6ca42cce76db7d59155c75d4d935a30cd726c99f84ca12dd46db7dfb2d1226","observation_id":"8f6daf97-d261-4711-9fc0-f670468deaad","resolution":{"observed_at":"2026-05-12T02:51:18.014028Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2103.00123/citation-record","integrity":"/paper/2103.00123/integrity","json":"/paper/2103.00123/citation-record.json","paper":"/paper/2103.00123"},"outbound":[],"paper":{"arxiv_id":"2103.00123","last_updated":"2021-06-11T22:08:29Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T10:45:06.227629Z","submitted_at":"2021-02-27T04:09:32Z","title":"GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2103.00123."}