{"as_of":"2026-08-07T18:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4dc2a51ea2d822172abfbcbe0962ebcffbe7c3e7b1e76483ffacd44d7214af07","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:52:33.521093Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","version":2},"cited_work":{"arxiv_id":"2409.17115","doi":"10.48550/arxiv.2409.17115","metadata_source":"arxiv_reference","pith_arxiv_id":"2409.17115","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2409.17115 , year=","venue":"arXiv (Cornell University)","work_id":"cd76eda1-2587-46e9-91dd-ececce05ceb2","year":2024},"citing_paper":{"arxiv_id":"2502.05171","last_updated":"2025-02-17T17:14:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-07T18:55:02Z","title":"Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach","version":2},"reference_index":186,"source":"arxiv_source","source_observed_at":"2026-05-12T15:39:40.845703Z"},"links":{"cited_paper":"/paper/2409.17115","citing_paper":"/paper/2502.05171"},"observation_digest":"sha256:d3fe9621cf7b2e1c76214a84403549955aae694b8aa90599cd2400ed3517bdd5","observation_id":"4dd430f5-bf08-4785-aab2-d2f4f7e97b5c","resolution":{"observed_at":"2026-05-12T15:39:40.994006Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.17115","snapshot_observed_at":"2026-08-06T22:52:33.521093Z","title":"Programming every example: Lifting pre-training data quality like experts at scale","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20639","last_updated":"2025-06-26T15:46:40Z","snapshot_observed_at":"2026-08-06T22:41:34.301841Z","submitted_at":"2025-06-25T17:35:47Z","title":"DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation","version":2},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-06T22:52:33.521093Z"},"links":{"cited_paper":"/paper/2409.17115","citing_paper":"/paper/2506.20639"},"observation_digest":"sha256:5d61e5cdce6473e86d1b71cdae2cf14b51f9064d9518b9af675f33ea3087768e","observation_id":"ca73abac-acf3-492a-bf6b-0a3c7bce0c6f","resolution":{"observed_at":"2026-08-06T22:52:33.521093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.17115","snapshot_observed_at":"2026-08-06T20:21:57.043953Z","title":"Programming every example: Lifting pre-training data quality like experts at scale","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.03253","last_updated":"2025-07-08T18:15:09Z","snapshot_observed_at":"2026-08-07T10:43:54.316543Z","submitted_at":"2025-07-04T02:19:58Z","title":"RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs","version":2},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-06T20:21:57.043953Z"},"links":{"cited_paper":"/paper/2409.17115","citing_paper":"/paper/2507.03253"},"observation_digest":"sha256:347b20ec40d4a8d7b4ef7b1b215d419ad9c52c76681c29d3d286472d798cb784","observation_id":"28521949-bc86-410a-9636-6117c73de4e8","resolution":{"observed_at":"2026-08-06T20:21:57.043953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","version":2},"cited_work":{"arxiv_id":"2409.17115","doi":"10.48550/arxiv.2409.17115","metadata_source":"arxiv_reference","pith_arxiv_id":"2409.17115","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2409.17115 , year=","venue":"arXiv (Cornell University)","work_id":"cd76eda1-2587-46e9-91dd-ececce05ceb2","year":2024},"citing_paper":{"arxiv_id":"2510.06499","last_updated":"2026-04-10T04:08:43Z","snapshot_observed_at":"2026-07-06T22:31:58.848080Z","submitted_at":"2025-10-07T22:30:59Z","title":"Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-18T08:39:54.747656Z"},"links":{"cited_paper":"/paper/2409.17115","citing_paper":"/paper/2510.06499"},"observation_digest":"sha256:b6cfe0944969db13f4e9f6dbe63140426fa6ca962604501d90c95f2d573cd172","observation_id":"b14b2432-90c1-4405-9633-41bacbf3e64a","resolution":{"observed_at":"2026-05-18T08:41:08.306680Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","version":2},"cited_work":{"arxiv_id":"2409.17115","doi":"10.48550/arxiv.2409.17115","metadata_source":"arxiv_reference","pith_arxiv_id":"2409.17115","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2409.17115 , year=","venue":"arXiv (Cornell University)","work_id":"cd76eda1-2587-46e9-91dd-ececce05ceb2","year":2024},"citing_paper":{"arxiv_id":"2603.18297","last_updated":"2026-04-03T22:50:01Z","snapshot_observed_at":"2026-07-06T22:49:37.944352Z","submitted_at":"2026-03-18T21:35:53Z","title":"Path-Constrained Mixture-of-Experts","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T09:16:06.226566Z"},"links":{"cited_paper":"/paper/2409.17115","citing_paper":"/paper/2603.18297"},"observation_digest":"sha256:a97ab584776c02c4a4ebe47e321fe6fa30974f66eeaeef4bdb92f819b9054687","observation_id":"1e151c1e-5169-4ff2-bd1e-af069973ecf8","resolution":{"observed_at":"2026-05-15T09:19:54.257903Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","version":2},"cited_work":{"arxiv_id":"2409.17115","doi":"10.48550/arxiv.2409.17115","metadata_source":"arxiv_reference","pith_arxiv_id":"2409.17115","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2409.17115 , year=","venue":"arXiv (Cornell University)","work_id":"cd76eda1-2587-46e9-91dd-ececce05ceb2","year":2024},"citing_paper":{"arxiv_id":"2605.19762","last_updated":"2026-05-19T12:37:01Z","snapshot_observed_at":"2026-08-02T21:57:08.320932Z","submitted_at":"2026-05-19T12:37:01Z","title":"What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-20T05:06:46.360174Z"},"links":{"cited_paper":"/paper/2409.17115","citing_paper":"/paper/2605.19762"},"observation_digest":"sha256:f6503566863402920f41b13c3a498d21224b5107e7fda45906be2d1c2fe972f6","observation_id":"a14645a0-31e3-4424-96bc-b941d05c8b9c","resolution":{"observed_at":"2026-05-20T05:08:05.116809Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.17115","snapshot_observed_at":"2026-07-31T07:01:44.734751Z","title":"arXiv preprint arXiv:2409.17115 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.24717","last_updated":"2026-07-27T17:54:12Z","snapshot_observed_at":"2026-07-31T07:01:40.912045Z","submitted_at":"2026-07-27T17:54:12Z","title":"DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-07-31T07:01:44.734751Z"},"links":{"cited_paper":"/paper/2409.17115","citing_paper":"/paper/2607.24717"},"observation_digest":"sha256:2c967e67771e85c6b814072efa31ebaef755d9a4552763353d854d686ae5c619","observation_id":"ca7f1222-afe8-45c4-9b9a-074c1bc0d12b","resolution":{"observed_at":"2026-07-31T07:01:44.734751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2409.17115/citation-record","integrity":"/paper/2409.17115/integrity","json":"/paper/2409.17115/citation-record.json","paper":"/paper/2409.17115"},"outbound":[],"paper":{"arxiv_id":"2409.17115","last_updated":"2025-02-14T16:44:08Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T19:22:14.655041Z","submitted_at":"2024-09-25T17:28:13Z","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale"},"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 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2409.17115."}