{"as_of":"2026-08-10T08:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:836ea3924ab20160a1bd99510cd9be2a80b7b78fad6164fd7cbc369a32a3f79f","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T22:24:49.395290Z","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":"2026-05-10T06:41:36.577626Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.07031","last_updated":"2024-01-30T20:50:56Z","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T10:19:26Z","title":"Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.07031","snapshot_observed_at":"2026-08-09T22:24:49.395290Z","title":"Code security vulnerability repair using reinforcement learning with large language models.arXiv:2401.07031, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18820","last_updated":"2025-01-31T00:35:55Z","snapshot_observed_at":"2026-08-09T22:19:06.045099Z","submitted_at":"2025-01-31T00:35:55Z","title":"SoK: Towards Effective Automated Vulnerability Repair","version":1},"reference_index":107,"source":"pdf_text","source_observed_at":"2026-08-09T22:24:49.395290Z"},"links":{"cited_paper":"/paper/2401.07031","citing_paper":"/paper/2501.18820"},"observation_digest":"sha256:655567284702c96bc058af5ba8533f48a4dd0c6ac2ddfec9b650a13198903e04","observation_id":"33761a8d-9d77-46b6-aae1-51b61ea743b7","resolution":{"observed_at":"2026-08-09T22:24:49.395290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07031","last_updated":"2024-01-30T20:50:56Z","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T10:19:26Z","title":"Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.07031","snapshot_observed_at":"2026-08-07T13:04:59.596935Z","title":"Code security vulnerability repair using reinforcement learning with large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22704","last_updated":"2025-05-28T17:57:47Z","snapshot_observed_at":"2026-08-09T22:36:48.949951Z","submitted_at":"2025-05-28T17:57:47Z","title":"Training Language Models to Generate Quality Code with Program Analysis Feedback","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T13:04:59.596935Z"},"links":{"cited_paper":"/paper/2401.07031","citing_paper":"/paper/2505.22704"},"observation_digest":"sha256:9d9ffbbceffb8bed441d53d91017ced8bfd83cb5f290cfc15144b1602cb2c225","observation_id":"3b43a0e2-45b5-4b95-bba7-5573f982563b","resolution":{"observed_at":"2026-08-07T13:04:59.596935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07031","last_updated":"2024-01-30T20:50:56Z","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T10:19:26Z","title":"Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.07031","snapshot_observed_at":"2026-08-06T21:56:15.385870Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23034","last_updated":"2025-06-28T23:24:33Z","snapshot_observed_at":"2026-08-08T22:22:43.604466Z","submitted_at":"2025-06-28T23:24:33Z","title":"Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.385870Z"},"links":{"cited_paper":"/paper/2401.07031","citing_paper":"/paper/2506.23034"},"observation_digest":"sha256:f34eac965102f8954976d870faa4331af5808414588d070eb399d912c7fdbf4a","observation_id":"ed6f5078-9715-4c54-9afb-d4f809813165","resolution":{"observed_at":"2026-08-06T21:56:15.385870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07031","last_updated":"2024-01-30T20:50:56Z","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T10:19:26Z","title":"Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.07031","snapshot_observed_at":"2026-08-06T04:31:01.632200Z","title":"Code security vulnerability repair using reinforcement learning with large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03487","last_updated":"2025-08-05T14:17:30Z","snapshot_observed_at":"2026-08-09T07:40:48.901429Z","submitted_at":"2025-08-05T14:17:30Z","title":"BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T04:31:01.632200Z"},"links":{"cited_paper":"/paper/2401.07031","citing_paper":"/paper/2508.03487"},"observation_digest":"sha256:8f95ec5d6073d3540c3a94ca5fb5cf2d16b0114ab4d12811539ce19247945cc0","observation_id":"a951e560-a699-4025-ae78-b4ab2fb1675b","resolution":{"observed_at":"2026-08-06T04:31:01.632200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07031","last_updated":"2024-01-30T20:50:56Z","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T10:19:26Z","title":"Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models","version":2},"cited_work":{"arxiv_id":"2401.07031","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.07031","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2401.07031 , year=","venue":null,"work_id":"3d7250ec-3253-44dc-b9a8-0f9c75663e0c","year":null},"citing_paper":{"arxiv_id":"2604.17184","last_updated":"2026-07-02T22:48:15Z","snapshot_observed_at":"2026-07-12T19:06:03.469874Z","submitted_at":"2026-04-19T01:01:44Z","title":"SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-10T06:39:29.822371Z"},"links":{"cited_paper":"/paper/2401.07031","citing_paper":"/paper/2604.17184"},"observation_digest":"sha256:aa95fb8fbc4ae01eec9358a2ba7b4f887821c39edf662b51b345399ebd7f79db","observation_id":"7f291ae9-1c17-4eb0-a1c5-7a761fd23ac9","resolution":{"observed_at":"2026-05-10T06:41:36.578875Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07031","last_updated":"2024-01-30T20:50:56Z","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T10:19:26Z","title":"Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.07031","snapshot_observed_at":"2026-08-01T08:38:07.245761Z","title":"Code Security Vulnerability Repair Using Reinforce- ment Learning with Large Language Models, 2024, [arXiv:cs.CR/2401.07031]","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21069","last_updated":"2026-07-23T09:02:46Z","snapshot_observed_at":"2026-08-09T20:26:49.860532Z","submitted_at":"2026-07-23T09:02:46Z","title":"From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T08:38:07.245761Z"},"links":{"cited_paper":"/paper/2401.07031","citing_paper":"/paper/2607.21069"},"observation_digest":"sha256:25c2b34bb9c5bce9a58fd2ce5ee27c4ecbd19f9d9f471b8e46d7276ddd81a221","observation_id":"f50a9149-319a-4980-9893-6ec33a27b6a9","resolution":{"observed_at":"2026-08-01T08:38:07.245761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.07031/citation-record","integrity":"/paper/2401.07031/integrity","json":"/paper/2401.07031/citation-record.json","paper":"/paper/2401.07031"},"outbound":[],"paper":{"arxiv_id":"2401.07031","last_updated":"2024-01-30T20:50:56Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-07-06T17:15:06.300682Z","submitted_at":"2024-01-13T10:19:26Z","title":"Code Security Vulnerability Repair Using Reinforcement Learning with 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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2401.07031."}