{"as_of":"2026-08-11T13:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d98d5fffed309bcc0fe410dddb6b3fbced9043a480e4f1ddcd39c731f24b1f9c","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":"2026-08-11T06:34:44.6726+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":"2026-06-27T01:13:11.483599Z","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":"2026-07-03T20:38:56.065163Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.10305","last_updated":"2024-12-17T02:53:54Z","snapshot_observed_at":"2026-08-10T00:40:36.170796Z","submitted_at":"2024-06-14T03:39:01Z","title":"Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.10305","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10305","snapshot_observed_at":"2026-07-03T20:38:56.065163Z","title":"arXiv preprint arXiv:2406.10305 , year=","venue":null,"work_id":"4054a9ef-844e-44bf-b5bb-347d8c608b1e","year":2024},"citing_paper":{"arxiv_id":"2604.16804","last_updated":"2026-05-06T17:41:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-18T03:24:54Z","title":"AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-10T07:02:02.992871Z"},"links":{"cited_paper":"/paper/2406.10305","citing_paper":"/paper/2604.16804"},"observation_digest":"sha256:1a42568ada3ae67c44cf0b8ebab82df3c6a48626de312f657f30e7058c0d511d","observation_id":"c25c2c08-16a5-4711-81b7-9ff75a054061","resolution":{"observed_at":"2026-05-10T07:11:53.641585Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10305","last_updated":"2024-12-17T02:53:54Z","snapshot_observed_at":"2026-08-10T00:40:36.170796Z","submitted_at":"2024-06-14T03:39:01Z","title":"Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models","version":2},"cited_work":{"arxiv_id":"2406.10305","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10305","snapshot_observed_at":"2026-07-03T20:38:56.065163Z","title":"arXiv preprint arXiv:2406.10305 , year=","venue":null,"work_id":"4054a9ef-844e-44bf-b5bb-347d8c608b1e","year":2024},"citing_paper":{"arxiv_id":"2606.18089","last_updated":"2026-07-05T17:40:26Z","snapshot_observed_at":"2026-07-12T13:34:39.011240Z","submitted_at":"2026-06-16T15:55:28Z","title":"From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning","version":1},"reference_index":142,"source":"arxiv_source","source_observed_at":"2026-06-27T01:13:11.483599Z"},"links":{"cited_paper":"/paper/2406.10305","citing_paper":"/paper/2606.18089"},"observation_digest":"sha256:5a584fb09b5a36f2bccb3fb8154b58eb915c312fdc39c236542564504663c85f","observation_id":"a1f3be3a-d229-4aca-bc40-8d18874dd6a3","resolution":{"observed_at":"2026-07-03T20:38:56.066602Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.10305/citation-record","integrity":"/paper/2406.10305/integrity","json":"/paper/2406.10305/citation-record.json","paper":"/paper/2406.10305"},"outbound":[],"paper":{"arxiv_id":"2406.10305","last_updated":"2024-12-17T02:53:54Z","latest_version":2,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-10T00:40:36.170796Z","submitted_at":"2024-06-14T03:39:01Z","title":"Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language 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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.10305."}