{"as_of":"2026-08-08T01:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:14ab7049e50bf707ae653957301c6df6c1e938bae59124358110edcb17f88dd7","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:33:16.948188Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T14:51:54.327926Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.01333","last_updated":"2024-06-03T13:58:04Z","snapshot_observed_at":"2026-07-06T18:24:26.225262Z","submitted_at":"2024-06-03T13:58:04Z","title":"Probing Language Models for Pre-training Data Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01333","snapshot_observed_at":"2026-08-07T04:33:16.948188Z","title":"Probing language mod- els for pre-training data detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10424","last_updated":"2025-06-12T07:23:56Z","snapshot_observed_at":"2026-08-07T04:24:32.582994Z","submitted_at":"2025-06-12T07:23:56Z","title":"SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T04:33:16.948188Z"},"links":{"cited_paper":"/paper/2406.01333","citing_paper":"/paper/2506.10424"},"observation_digest":"sha256:8be3ab9f4806fe635fad44a7358efc133867e997cfa7ebaf74bc563e06999eb7","observation_id":"8feff51e-2c9e-4bb6-825b-0fed7cab320f","resolution":{"observed_at":"2026-08-07T04:33:16.948188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01333","last_updated":"2024-06-03T13:58:04Z","snapshot_observed_at":"2026-07-06T18:24:26.225262Z","submitted_at":"2024-06-03T13:58:04Z","title":"Probing Language Models for Pre-training Data Detection","version":1},"cited_work":{"arxiv_id":"2406.01333","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.01333","snapshot_observed_at":"2026-08-06T14:51:54.327926Z","title":"Probing Language Models for Pre-training Data Detection","venue":"cs.CL","work_id":"5ae899a0-cb03-4f03-b752-9a0e8856ab64","year":2024},"citing_paper":{"arxiv_id":"2507.17389","last_updated":"2025-07-23T10:34:22Z","snapshot_observed_at":"2026-08-07T11:21:36.520513Z","submitted_at":"2025-07-23T10:34:22Z","title":"Investigating Training Data Detection in AI Coders","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T14:51:53.791821Z"},"links":{"cited_paper":"/paper/2406.01333","citing_paper":"/paper/2507.17389"},"observation_digest":"sha256:8e4ee065d2f85945c5bfb7499938a91d8fadde4a3a66d68b437192f311e69694","observation_id":"95147091-839a-4949-8508-5ce9011bf053","resolution":{"observed_at":"2026-08-06T14:51:54.331327Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/2406.01333/citation-record","integrity":"/paper/2406.01333/integrity","json":"/paper/2406.01333/citation-record.json","paper":"/paper/2406.01333"},"outbound":[],"paper":{"arxiv_id":"2406.01333","last_updated":"2024-06-03T13:58:04Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T18:24:26.225262Z","submitted_at":"2024-06-03T13:58:04Z","title":"Probing Language Models for Pre-training Data Detection"},"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 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.01333."}