{"as_of":"2026-08-09T12:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c855cb6aa1dfb7767f343facd35ccff67d0d734bccc49ddd23c530762bf25307","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:35:11.856700Z","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":"2026-07-02T20:57:23.351502Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.00678","last_updated":"2025-05-20T20:47:57Z","snapshot_observed_at":"2026-08-02T00:28:51.006030Z","submitted_at":"2025-02-02T05:50:39Z","title":"How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00678","snapshot_observed_at":"2026-08-06T22:35:11.856700Z","title":"K., Khanov, M., Wei, H., and Li, Y","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21182","last_updated":"2025-06-26T12:40:48Z","snapshot_observed_at":"2026-08-09T03:56:33.001153Z","submitted_at":"2025-06-26T12:40:48Z","title":"Maintaining MTEB: Towards Long Term Usability and Reproducibility of Embedding Benchmarks","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T22:35:11.856700Z"},"links":{"cited_paper":"/paper/2502.00678","citing_paper":"/paper/2506.21182"},"observation_digest":"sha256:dd0a0f144d346dd616ecd744b15dbd4982d39bceb7cec260bf08e940bc7547d7","observation_id":"5730a9c4-46ad-4ad3-b5e8-92b0d06bd75d","resolution":{"observed_at":"2026-08-06T22:35:11.856700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00678","last_updated":"2025-05-20T20:47:57Z","snapshot_observed_at":"2026-08-02T00:28:51.006030Z","submitted_at":"2025-02-02T05:50:39Z","title":"How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00678","snapshot_observed_at":"2026-08-04T07:40:11.897460Z","title":"How contami- nated is your benchmark? quantifying and mitigating data leakage in llmevaluation,2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.24990","last_updated":"2026-08-02T21:17:46Z","snapshot_observed_at":"2026-08-06T23:23:59.021045Z","submitted_at":"2025-10-28T21:37:35Z","title":"The Economics of AI Training Data: A Research Agenda","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T07:40:11.897460Z"},"links":{"cited_paper":"/paper/2502.00678","citing_paper":"/paper/2510.24990"},"observation_digest":"sha256:dc3a6a3ae6c0d385449609c7a6dc2d1de28f27a28c745a182b03870aba59d12e","observation_id":"de39f1bc-94b6-4e53-b903-ba3bd58af580","resolution":{"observed_at":"2026-08-04T07:40:11.897460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00678","last_updated":"2025-05-20T20:47:57Z","snapshot_observed_at":"2026-08-02T00:28:51.006030Z","submitted_at":"2025-02-02T05:50:39Z","title":"How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence","version":2},"cited_work":{"arxiv_id":"2502.00678","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.00678","snapshot_observed_at":"2026-07-02T20:57:23.351502Z","title":"InACL (Findings), pages 15094–15119","venue":null,"work_id":"a3aebd09-6e90-4039-a6aa-54f2ccbf83a5","year":2025},"citing_paper":{"arxiv_id":"2604.21255","last_updated":"2026-04-23T03:48:56Z","snapshot_observed_at":"2026-07-06T23:07:51.979267Z","submitted_at":"2026-04-23T03:48:56Z","title":"When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-09T22:07:58.614654Z"},"links":{"cited_paper":"/paper/2502.00678","citing_paper":"/paper/2604.21255"},"observation_digest":"sha256:373d37c4ed89b6350fe135375d56b6c5f0ae4cf826fb26dc993d068dcd5421b5","observation_id":"8d26c43a-aedc-4344-8d11-af3667d41ce5","resolution":{"observed_at":"2026-05-11T14:16:18.222490Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00678","last_updated":"2025-05-20T20:47:57Z","snapshot_observed_at":"2026-08-02T00:28:51.006030Z","submitted_at":"2025-02-02T05:50:39Z","title":"How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence","version":2},"cited_work":{"arxiv_id":"2502.00678","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.00678","snapshot_observed_at":"2026-07-02T20:57:23.351502Z","title":"InACL (Findings), pages 15094–15119","venue":null,"work_id":"a3aebd09-6e90-4039-a6aa-54f2ccbf83a5","year":2025},"citing_paper":{"arxiv_id":"2606.07805","last_updated":"2026-06-05T19:33:58Z","snapshot_observed_at":"2026-08-03T01:44:22.181409Z","submitted_at":"2026-06-05T19:33:58Z","title":"Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-27T21:53:37.616447Z"},"links":{"cited_paper":"/paper/2502.00678","citing_paper":"/paper/2606.07805"},"observation_digest":"sha256:24083e263b88142d7becc8c26626c7479284878efb4ca8e5d964229b404299b8","observation_id":"a86d04d6-a9a5-46c5-93c1-0cab8b5a3bee","resolution":{"observed_at":"2026-07-02T17:47:17.785130Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00678","last_updated":"2025-05-20T20:47:57Z","snapshot_observed_at":"2026-08-02T00:28:51.006030Z","submitted_at":"2025-02-02T05:50:39Z","title":"How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence","version":2},"cited_work":{"arxiv_id":"2502.00678","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.00678","snapshot_observed_at":"2026-07-02T20:57:23.351502Z","title":"InACL (Findings), pages 15094–15119","venue":null,"work_id":"a3aebd09-6e90-4039-a6aa-54f2ccbf83a5","year":2025},"citing_paper":{"arxiv_id":"2606.07996","last_updated":"2026-06-06T06:27:54Z","snapshot_observed_at":"2026-07-31T22:22:12.944552Z","submitted_at":"2026-06-06T06:27:54Z","title":"MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T20:02:50.169589Z"},"links":{"cited_paper":"/paper/2502.00678","citing_paper":"/paper/2606.07996"},"observation_digest":"sha256:e3774ebf01f4f6812b81d7b169e8b64314d0fd0969045a835d30429e31899632","observation_id":"115f0cf7-4813-46ac-9c84-79ba0c1a5787","resolution":{"observed_at":"2026-07-02T20:57:23.353100Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.00678/citation-record","integrity":"/paper/2502.00678/integrity","json":"/paper/2502.00678/citation-record.json","paper":"/paper/2502.00678"},"outbound":[],"paper":{"arxiv_id":"2502.00678","last_updated":"2025-05-20T20:47:57Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T00:28:51.006030Z","submitted_at":"2025-02-02T05:50:39Z","title":"How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2502.00678."}