{"as_of":"2026-08-04T21:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cd6d4d2b7b504b5bdac34f1330aefd9338975bf4c793d2b003e7345da66662d6","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T18:28:32.507165Z","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":"2026-07-04T06:19:38.875861Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":"2002.08484","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-07-04T06:19:38.875861Z","title":"Estimating training data influence by tracing gradient descent","venue":null,"work_id":"beaa9ade-a6e8-4faf-8499-42886e01db56","year":2020},"citing_paper":{"arxiv_id":"2502.00270","last_updated":"2026-05-13T20:43:28Z","snapshot_observed_at":"2026-08-03T15:35:37.964180Z","submitted_at":"2025-02-01T01:52:32Z","title":"DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-23T03:58:48.967122Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2502.00270"},"observation_digest":"sha256:441f4eb3d0d6cc97583e3cae170ca635fba71b42efa0232ad186b94142d406de","observation_id":"7650d613-c445-4905-aa1f-af0ea6e173af","resolution":{"observed_at":"2026-05-23T04:02:30.439479Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-08-03T18:28:32.507165Z","title":"P Rajpurkar","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-03T18:28:30.048107Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.507165Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:023790686024a6b83ad82df5ad85c4fd3d29afd55da44038408accc5ef42dd1e","observation_id":"446f04a7-9d6d-434d-93ff-edcac189c2ed","resolution":{"observed_at":"2026-08-03T18:28:32.507165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-08-03T06:35:43.109362Z","title":"Estimat- ing training data influence by tracing gradient descent","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2601.22651","last_updated":"2026-06-01T06:29:25Z","snapshot_observed_at":"2026-08-03T06:35:40.543977Z","submitted_at":"2026-01-30T07:10:59Z","title":"GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T06:35:43.109362Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2601.22651"},"observation_digest":"sha256:d609cfa515e6d7dbd02df656e3776b2e6051e90a3274026045620b2eb9107d9f","observation_id":"67c159ce-1e0d-43bb-9a02-e9d026560db4","resolution":{"observed_at":"2026-08-03T06:35:43.109362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":"2002.08484","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-07-04T06:19:38.875861Z","title":"Estimating training data influence by tracing gradient descent","venue":null,"work_id":"beaa9ade-a6e8-4faf-8499-42886e01db56","year":2020},"citing_paper":{"arxiv_id":"2604.16197","last_updated":"2026-07-19T20:58:40Z","snapshot_observed_at":"2026-08-02T16:12:05.608942Z","submitted_at":"2026-04-17T16:07:11Z","title":"Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T08:47:36.122054Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2604.16197"},"observation_digest":"sha256:5c0e6a70afe81e5612f6d025ff7304042ef5aaf5271883898ddedeea9c1c6954","observation_id":"f635a768-3758-424c-a018-f1e72d98b768","resolution":{"observed_at":"2026-05-10T08:48:01.020637Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-08-02T16:12:11.881072Z","title":"Estimating training data influence by tracing gradient descent, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2604.16197","last_updated":"2026-07-19T20:58:40Z","snapshot_observed_at":"2026-08-02T16:12:05.608942Z","submitted_at":"2026-04-17T16:07:11Z","title":"Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T16:12:11.881072Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2604.16197"},"observation_digest":"sha256:b3e85e19081aa063caf702067b6792242fe3456e15539d4aff3f75da35cf6afe","observation_id":"3352af87-c686-4285-9da9-d981d2c28f60","resolution":{"observed_at":"2026-08-02T16:12:11.881072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":"2002.08484","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-07-04T06:19:38.875861Z","title":"Estimating training data influence by tracing gradient descent","venue":null,"work_id":"beaa9ade-a6e8-4faf-8499-42886e01db56","year":2020},"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":35,"source":"pdf_text","source_observed_at":"2026-05-12T02:47:55.649231Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2605.09404"},"observation_digest":"sha256:5ba3459ac1a3c0191ef3b3f0d1a5eccf5c573d211eb1f3a53bbec592bc7a2a3f","observation_id":"7b219f61-860f-454c-9ac9-fd13d30be752","resolution":{"observed_at":"2026-05-12T02:51:18.005919Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":"2002.08484","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-07-04T06:19:38.875861Z","title":"Estimating training data influence by tracing gradient descent","venue":null,"work_id":"beaa9ade-a6e8-4faf-8499-42886e01db56","year":2020},"citing_paper":{"arxiv_id":"2606.05800","last_updated":"2026-06-04T07:29:43Z","snapshot_observed_at":"2026-07-06T23:45:47.161248Z","submitted_at":"2026-06-04T07:29:43Z","title":"SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T02:53:22.159132Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2606.05800"},"observation_digest":"sha256:80d7658c819413039c3fdf6bf9dcb3df91a8141fa579cc0668ff0c03a5dacf85","observation_id":"8800ab39-58a8-4af0-87ae-710e28aadeee","resolution":{"observed_at":"2026-07-02T11:46:56.166054Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":"2002.08484","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-07-04T06:19:38.875861Z","title":"Estimating training data influence by tracing gradient descent","venue":null,"work_id":"beaa9ade-a6e8-4faf-8499-42886e01db56","year":2020},"citing_paper":{"arxiv_id":"2606.21306","last_updated":"2026-06-19T10:38:00Z","snapshot_observed_at":"2026-08-01T23:32:53.213705Z","submitted_at":"2026-06-19T10:38:00Z","title":"Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-26T14:31:46.594002Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2606.21306"},"observation_digest":"sha256:bb4424c50b040a2fce2fd0d12fba46f3e96d02aabaca33048b97a5f6c102c2f7","observation_id":"bf36f196-3744-4671-a195-093c825fdbd9","resolution":{"observed_at":"2026-07-04T06:19:38.877819Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-08-01T19:49:25.386620Z","title":"Estimating training data influence by tracing gradient descent.arXiv preprint arXiv:2002.08484, 2020","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2607.16859","last_updated":"2026-07-18T15:47:03Z","snapshot_observed_at":"2026-08-01T19:49:19.386440Z","submitted_at":"2026-07-18T15:47:03Z","title":"Dataset Distillation by Influence Matching","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T19:49:25.386620Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2607.16859"},"observation_digest":"sha256:8e3bb20b59f431b8227415273dc608733b9519df4e9324efc9c29eac1844c499","observation_id":"80ed8efe-3b24-45f2-807e-4eba225cedd1","resolution":{"observed_at":"2026-08-01T19:49:25.386620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2002.08484/citation-record","integrity":"/paper/2002.08484/integrity","json":"/paper/2002.08484/citation-record.json","paper":"/paper/2002.08484"},"outbound":[],"paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent"},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2002.08484."}