{"as_of":"2026-08-04T10:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c396d354ce87ad70b4b78dbebbf040a53e8aa55d4eea32d3f812499d18547507","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T10:16:38.248004Z","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":"2026-07-04T19:50:11.208012Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.12563","last_updated":"2021-04-12T09:10:06Z","snapshot_observed_at":"2026-07-06T10:07:57.849143Z","submitted_at":"2020-10-23T17:47:06Z","title":"Concealed Data Poisoning Attacks on NLP Models","version":2},"cited_work":{"arxiv_id":"2010.12563","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.12563","snapshot_observed_at":"2026-07-04T19:50:11.208012Z","title":"poisoned","venue":null,"work_id":"71dd8979-1488-4b54-ab98-09feef579a99","year":2010},"citing_paper":{"arxiv_id":"2512.04457","last_updated":"2026-05-14T19:49:24Z","snapshot_observed_at":"2026-07-06T22:37:45.947333Z","submitted_at":"2025-12-04T05:00:52Z","title":"RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-21T17:33:06.959574Z"},"links":{"cited_paper":"/paper/2010.12563","citing_paper":"/paper/2512.04457"},"observation_digest":"sha256:3646fca6466005bbe6c45dae291119733832d2671b8b645cb38fa56d821c8e9c","observation_id":"0c8be5a4-0139-462a-95bb-a8e3025f9afb","resolution":{"observed_at":"2026-05-21T17:34:17.377307Z","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":"2010.12563","last_updated":"2021-04-12T09:10:06Z","snapshot_observed_at":"2026-07-06T10:07:57.849143Z","submitted_at":"2020-10-23T17:47:06Z","title":"Concealed Data Poisoning Attacks on NLP Models","version":2},"cited_work":{"arxiv_id":"2010.12563","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.12563","snapshot_observed_at":"2026-07-04T19:50:11.208012Z","title":"poisoned","venue":null,"work_id":"71dd8979-1488-4b54-ab98-09feef579a99","year":2010},"citing_paper":{"arxiv_id":"2606.25476","last_updated":"2026-07-21T11:09:35Z","snapshot_observed_at":"2026-08-02T10:16:34.720247Z","submitted_at":"2026-06-24T07:00:53Z","title":"A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-25T20:58:53.119386Z"},"links":{"cited_paper":"/paper/2010.12563","citing_paper":"/paper/2606.25476"},"observation_digest":"sha256:75dd370d87565c6b4b663ba45d3d11044da8d70c7a05653431b545889ac097f7","observation_id":"8f499758-16ba-4934-ae5f-d694e449d34a","resolution":{"observed_at":"2026-07-04T19:50:11.209459Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2010.12563","last_updated":"2021-04-12T09:10:06Z","snapshot_observed_at":"2026-07-06T10:07:57.849143Z","submitted_at":"2020-10-23T17:47:06Z","title":"Concealed Data Poisoning Attacks on NLP Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.12563","snapshot_observed_at":"2026-08-02T10:16:38.248004Z","title":"arXiv preprint arXiv:2010.12563 (2020)","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.25476","last_updated":"2026-07-21T11:09:35Z","snapshot_observed_at":"2026-08-02T10:16:34.720247Z","submitted_at":"2026-06-24T07:00:53Z","title":"A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T10:16:38.248004Z"},"links":{"cited_paper":"/paper/2010.12563","citing_paper":"/paper/2606.25476"},"observation_digest":"sha256:99394950f2b3388c97078aee4303c5b5e6d6a2fac86d0754b5e50b8e69b58ce7","observation_id":"94e3bf5e-1d55-4c90-9ca9-e505211f1e21","resolution":{"observed_at":"2026-08-02T10:16:38.248004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.12563","last_updated":"2021-04-12T09:10:06Z","snapshot_observed_at":"2026-07-06T10:07:57.849143Z","submitted_at":"2020-10-23T17:47:06Z","title":"Concealed Data Poisoning Attacks on NLP Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.12563","snapshot_observed_at":"2026-08-01T23:44:46.193089Z","title":"Alexander Wettig, Kyle Lo, Sewon Min, Hannaneh Hajishirzi, Danqi Chen, and Luca Soldaini","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.15267","last_updated":"2026-07-16T17:56:05Z","snapshot_observed_at":"2026-08-02T05:51:59.468831Z","submitted_at":"2026-07-16T17:56:05Z","title":"Pretraining Data Can Be Poisoned through Computational Propaganda","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-01T23:44:46.193089Z"},"links":{"cited_paper":"/paper/2010.12563","citing_paper":"/paper/2607.15267"},"observation_digest":"sha256:b00df67facaa0df3cfd47de97ba90d80ad3d6f5f09a245e051b5cb491b82d269","observation_id":"e269dfcf-dbbd-42b0-b2e5-223ba479aa7c","resolution":{"observed_at":"2026-08-01T23:44:46.193089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2010.12563/citation-record","integrity":"/paper/2010.12563/integrity","json":"/paper/2010.12563/citation-record.json","paper":"/paper/2010.12563"},"outbound":[],"paper":{"arxiv_id":"2010.12563","last_updated":"2021-04-12T09:10:06Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T10:07:57.849143Z","submitted_at":"2020-10-23T17:47:06Z","title":"Concealed Data Poisoning Attacks on NLP Models"},"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 4 inbound Pith citation observations for arXiv:2010.12563."}