{"as_of":"2026-08-04T13:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ca969a1eb810fed0a0e0b7e158d0a0948b6e5bb44d7b3d48479230905854e3fc","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":"2026-07-31T12:45:04.789254Z","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":"2026-07-03T16:48:39.391752Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.16511","last_updated":"2025-04-26T00:13:08Z","snapshot_observed_at":"2026-07-06T21:13:27.382529Z","submitted_at":"2025-04-23T08:36:50Z","title":"QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining","version":2},"cited_work":{"arxiv_id":"2504.16511","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.16511","snapshot_observed_at":"2026-07-03T16:48:39.391752Z","title":"Quadmix: Quality- diversity balanced data selection for efficient llm pretraining","venue":null,"work_id":"281ee593-55a6-4abe-92a7-f73b0b1be16e","year":2025},"citing_paper":{"arxiv_id":"2604.16380","last_updated":"2026-03-25T13:30:40Z","snapshot_observed_at":"2026-07-06T23:03:43.854612Z","submitted_at":"2026-03-25T13:30:40Z","title":"Data Mixing for Large Language Models Pretraining: A Survey and Outlook","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-15T00:56:04.958757Z"},"links":{"cited_paper":"/paper/2504.16511","citing_paper":"/paper/2604.16380"},"observation_digest":"sha256:e85588c5d59451e3078f623b22a942be1b3c526bb15f3c469e234821afda5a4e","observation_id":"3b319f6d-5ca3-4aa1-897a-89221e9f7c32","resolution":{"observed_at":"2026-05-15T00:58:25.666412Z","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":"2504.16511","last_updated":"2025-04-26T00:13:08Z","snapshot_observed_at":"2026-07-06T21:13:27.382529Z","submitted_at":"2025-04-23T08:36:50Z","title":"QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining","version":2},"cited_work":{"arxiv_id":"2504.16511","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.16511","snapshot_observed_at":"2026-07-03T16:48:39.391752Z","title":"Quadmix: Quality- diversity balanced data selection for efficient llm pretraining","venue":null,"work_id":"281ee593-55a6-4abe-92a7-f73b0b1be16e","year":2025},"citing_paper":{"arxiv_id":"2605.02364","last_updated":"2026-05-04T09:07:54Z","snapshot_observed_at":"2026-07-06T23:15:27.816549Z","submitted_at":"2026-05-04T09:07:54Z","title":"InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-08T18:45:52.380042Z"},"links":{"cited_paper":"/paper/2504.16511","citing_paper":"/paper/2605.02364"},"observation_digest":"sha256:232be4f4b56aca18f3031d6c51c63b7a0eea51739f2c2281293e5da8b0f21103","observation_id":"810dd919-b834-4c7b-aa5e-a33660d5f189","resolution":{"observed_at":"2026-05-09T06:15:37.219533Z","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":"2504.16511","last_updated":"2025-04-26T00:13:08Z","snapshot_observed_at":"2026-07-06T21:13:27.382529Z","submitted_at":"2025-04-23T08:36:50Z","title":"QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining","version":2},"cited_work":{"arxiv_id":"2504.16511","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.16511","snapshot_observed_at":"2026-07-03T16:48:39.391752Z","title":"Quadmix: Quality- diversity balanced data selection for efficient llm pretraining","venue":null,"work_id":"281ee593-55a6-4abe-92a7-f73b0b1be16e","year":2025},"citing_paper":{"arxiv_id":"2607.02266","last_updated":"2026-07-02T14:51:42Z","snapshot_observed_at":"2026-08-01T16:10:16.151976Z","submitted_at":"2026-07-02T14:51:42Z","title":"HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-07-03T16:44:41.720388Z"},"links":{"cited_paper":"/paper/2504.16511","citing_paper":"/paper/2607.02266"},"observation_digest":"sha256:afe864a633dc6d4baead82c7767a71acaedcb7e8993d4f2c7c8b4b5e91047b2f","observation_id":"d5e2a428-68bd-4552-ac55-1b37d70192f8","resolution":{"observed_at":"2026-07-03T16:48:39.393155Z","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":"2504.16511","last_updated":"2025-04-26T00:13:08Z","snapshot_observed_at":"2026-07-06T21:13:27.382529Z","submitted_at":"2025-04-23T08:36:50Z","title":"QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16511","snapshot_observed_at":"2026-07-31T12:45:04.789254Z","title":"Quadmix: Quality-diversity balanced data selection for efficient llm pretraining","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24516","last_updated":"2026-07-27T14:55:04Z","snapshot_observed_at":"2026-08-03T00:57:22.408833Z","submitted_at":"2026-07-27T14:55:04Z","title":"DecoupleMix: Decoupled Ratio Search and Convex Allocation for Scalable VLM Data Recipes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-31T12:45:04.789254Z"},"links":{"cited_paper":"/paper/2504.16511","citing_paper":"/paper/2607.24516"},"observation_digest":"sha256:baf93bb5dcc168fae1d69c555f1d6c3e22c400bcd84c1828208076bd959995c4","observation_id":"85ef347f-29e8-45c7-84c1-9e86482d7926","resolution":{"observed_at":"2026-07-31T12:45:04.789254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.16511/citation-record","integrity":"/paper/2504.16511/integrity","json":"/paper/2504.16511/citation-record.json","paper":"/paper/2504.16511"},"outbound":[],"paper":{"arxiv_id":"2504.16511","last_updated":"2025-04-26T00:13:08Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T21:13:27.382529Z","submitted_at":"2025-04-23T08:36:50Z","title":"QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining"},"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:2504.16511."}