{"as_of":"2026-08-18T13:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bf8d05e6f7fc081c5d00eda4773f4d496146fdf3e64f9d59f1e23a8c6e05eaf8","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:34:01.145392Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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":"2026-07-11T21:45:42.517559Z","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":"2026-05-11T20:26:13.916471Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"cited_work":{"arxiv_id":"2504.15210","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.15210","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Integrating symbolic execution into the fine-tuning of code-generating llms","venue":null,"work_id":"3a6324af-ba53-4aee-acde-29fb12120175","year":2025},"citing_paper":{"arxiv_id":"2605.06184","last_updated":"2026-05-07T13:01:06Z","snapshot_observed_at":"2026-08-16T08:10:37.933368Z","submitted_at":"2026-05-07T13:01:06Z","title":"Teaching LLMs Program Semantics via Symbolic Execution Traces","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-08T08:56:25.022619Z"},"links":{"cited_paper":"/paper/2504.15210","citing_paper":"/paper/2605.06184"},"observation_digest":"sha256:87c157a260a0b2647081ff3472c76daac455b05fbdf1ef272daacfd1e4fcc82e","observation_id":"fb274bd0-e345-4d1a-a6e6-9e8cfca61111","resolution":{"observed_at":"2026-05-11T20:26:13.927352Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15210","snapshot_observed_at":"2026-07-11T21:45:42.517559Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.04092","last_updated":"2026-07-05T03:07:10Z","snapshot_observed_at":"2026-08-14T14:49:05.499440Z","submitted_at":"2026-07-05T03:07:10Z","title":"SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-11T21:45:42.517559Z"},"links":{"cited_paper":"/paper/2504.15210","citing_paper":"/paper/2607.04092"},"observation_digest":"sha256:4b718de857f43f706404f190863887bf4501de1a0af5dc16600a4fbcaf4735b7","observation_id":"8d77a167-7348-45de-83c5-9de290b816ab","resolution":{"observed_at":"2026-07-11T21:45:42.517559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.15210/citation-record","integrity":"/paper/2504.15210/integrity","json":"/paper/2504.15210/citation-record.json","paper":"/paper/2504.15210"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-15T17:40:38.050939Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-08-16T11:34:01.026055Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.026055Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:eb81944bdc23b12b8b6c3b2a31d35b2a9292e800a05e32e4b668d1d9e5c5ec1e","observation_id":"582fecea-f890-4259-bf27-e464f6beaf79","resolution":{"observed_at":"2026-08-16T11:34:01.026055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.031883Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.031883Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:117019a8f8107c911c563c154dd0111f2995ec4b89c17845558a703dd72ab2ea","observation_id":"71857ad0-57e8-432d-bbcc-fcd29403dd5b","resolution":{"observed_at":"2026-08-16T11:34:01.031883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15217","last_updated":"2023-09-11T17:25:24Z","snapshot_observed_at":"2026-08-17T11:20:55.974248Z","submitted_at":"2023-07-27T22:29:25Z","title":"Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15217","snapshot_observed_at":"2026-08-16T11:34:01.037432Z","title":"Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem B y k, Anca Dragan, David Krueger, Dorsa Sadigh, and Dylan Hadfield-Menell","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.037432Z"},"links":{"cited_paper":"/paper/2307.15217","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:2dced5ec0b6e3d0a37bda2a99b2f0b31578da8ca9adfcef5c38c7c9db96758d1","observation_id":"e1dd0b37-99d6-4d2c-a347-7c070201ea3f","resolution":{"observed_at":"2026-08-16T11:34:01.037432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2139/ssrn.4938953","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.254437Z","title":null,"venue":null,"work_id":"0ece5a9c-0f2c-46ec-9a40-7fc415c77e80","year":2024},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.045376Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:4380591a12321dd596b49386cd5981ce3826aa5dcabd29014d5dc11167ea5797","observation_id":"feb76b16-42b5-4b80-9cb7-ec94023f3aee","resolution":{"observed_at":"2026-08-16T11:34:01.260116Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-16T11:34:01.049972Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.049972Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:974bffd904a446364a57f666896ebb2e9ab5d451c4a3c3d694b187fcd3660e3a","observation_id":"a79f49aa-8697-4611-a2b9-a042e06b0b88","resolution":{"observed_at":"2026-08-16T11:34:01.049972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01391","last_updated":"2024-02-05T13:28:23Z","snapshot_observed_at":"2026-08-17T14:37:36.214810Z","submitted_at":"2024-02-02T13:14:31Z","title":"StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01391","snapshot_observed_at":"2026-08-16T11:34:01.054883Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.054883Z"},"links":{"cited_paper":"/paper/2402.01391","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:eefd43226f232429f1bd240f8a7b88afc2ccbbc4f0bc84e026ccecbaa89e258f","observation_id":"ea30a76c-e77e-40e4-b4e9-06aaaf99fb45","resolution":{"observed_at":"2026-08-16T11:34:01.054883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.641941Z","title":null,"venue":null,"work_id":"c64c1f21-327b-42b9-a996-75292e5d140f","year":2023},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.060927Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:346d3f59d0c908fc69f87e39c80ecba9dc7251e9f6577ee9d84f37296d65c6ed","observation_id":"50f17fdd-521e-4ff6-a8d7-633e4f512f08","resolution":{"observed_at":"2026-08-16T11:34:01.647598Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.09938","last_updated":"2021-11-08T21:16:44Z","snapshot_observed_at":"2026-08-14T15:19:05.440904Z","submitted_at":"2021-05-20T17:58:42Z","title":"Measuring Coding Challenge Competence With APPS","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.09938","snapshot_observed_at":"2026-08-16T11:34:01.066056Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.066056Z"},"links":{"cited_paper":"/paper/2105.09938","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:6ac28f951f0663a6f126025a1327272d1b63fbcf193cf89278887cf06d8c60c1","observation_id":"dbe27fdc-08f4-4550-bc19-0718b17def1a","resolution":{"observed_at":"2026-08-16T11:34:01.066056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5772/intechopen.68392","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.236078Z","title":null,"venue":null,"work_id":"9e75a30f-4c78-4af0-b945-24c6263b5c3f","year":2017},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.070956Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:6d1d5ead8b78b116fa9bbe15d1c89a80d4e986d50b307a67b29655699450a2dd","observation_id":"6ff6c071-45ac-4644-8e94-25f0cb24ab92","resolution":{"observed_at":"2026-08-16T11:34:01.243171Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.076092Z","title":null,"venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.076092Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:682761d35417f4d38cbf1b9c559cc9b937820f3be4260102a880d2b18e5b0917","observation_id":"6620d634-c413-4214-bae2-1b1b37153a36","resolution":{"observed_at":"2026-08-16T11:34:01.076092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01780","last_updated":"2022-11-03T08:32:59Z","snapshot_observed_at":"2026-08-16T16:48:21.526954Z","submitted_at":"2022-07-05T02:42:15Z","title":"CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.01780","snapshot_observed_at":"2026-08-16T11:34:01.080935Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.080935Z"},"links":{"cited_paper":"/paper/2207.01780","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:19820524097b02c995ac76fafa30a8ed050adaa7d6fc511dbabcabe3c02b7713","observation_id":"f32b00ae-b77e-4282-b19b-6ff9c64cc8cd","resolution":{"observed_at":"2026-08-16T11:34:01.080935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.04349","last_updated":"2023-11-13T03:49:27Z","snapshot_observed_at":"2026-08-16T15:17:34.306721Z","submitted_at":"2023-07-10T05:18:18Z","title":"RLTF: Reinforcement Learning from Unit Test Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.04349","snapshot_observed_at":"2026-08-16T11:34:01.086282Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.086282Z"},"links":{"cited_paper":"/paper/2307.04349","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:5c980b2279077427b40bd40cd6c72a269c4ce3eafd6a3163030d39e076cf5597","observation_id":"41d0aa29-416c-468f-a611-61a732325f4d","resolution":{"observed_at":"2026-08-16T11:34:01.086282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.02155","last_updated":"2022-03-04T07:04:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-03-04T07:04:42Z","title":"Training language models to follow instructions with human feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.02155","snapshot_observed_at":"2026-08-16T11:34:01.091896Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.091896Z"},"links":{"cited_paper":"/paper/2203.02155","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:157bca7cbcf19a38751bc786ad49dd707326af0448a67f6e144fac6455ffce82","observation_id":"f38df14e-36e9-4e95-902a-b39c7ad183c2","resolution":{"observed_at":"2026-08-16T11:34:01.091896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18290","last_updated":"2024-07-29T22:26:36Z","snapshot_observed_at":"2026-08-01T16:34:38.795326Z","submitted_at":"2023-05-29T17:57:46Z","title":"Direct Preference Optimization: Your Language Model is Secretly a Reward Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.18290","snapshot_observed_at":"2026-08-16T11:34:01.096990Z","title":"Manning, and Chelsea Finn","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.096990Z"},"links":{"cited_paper":"/paper/2305.18290","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:29a7f82c8d9967491d1674573fb9da4ad01be0d78ea34c52e1e0ec2a3a109934","observation_id":"e8605360-fe7f-4e46-a534-502d8bdc6a7a","resolution":{"observed_at":"2026-08-16T11:34:01.096990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13816","last_updated":"2023-07-19T19:55:31Z","snapshot_observed_at":"2026-08-17T14:37:55.930583Z","submitted_at":"2023-01-31T18:02:26Z","title":"Execution-based Code Generation using Deep Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.13816","snapshot_observed_at":"2026-08-16T11:34:01.102338Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.102338Z"},"links":{"cited_paper":"/paper/2301.13816","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:e00502c0e0d5a6c5d7ae813b2c99ecf9cf984e4fc98e7199b5ac810ec99d7255","observation_id":"c24997c3-8e7e-4179-b065-4e5bbedb2a5b","resolution":{"observed_at":"2026-08-16T11:34:01.102338Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09271","last_updated":"2024-09-14T02:43:20Z","snapshot_observed_at":"2026-08-16T13:18:38.678090Z","submitted_at":"2024-09-14T02:43:20Z","title":"Python Symbolic Execution with LLM-powered Code Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.09271","snapshot_observed_at":"2026-08-16T11:34:01.107321Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.107321Z"},"links":{"cited_paper":"/paper/2409.09271","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:6f3acc9e84376dae61cba7c8a3811fb9a7028accf9f8c4bfb2ac46c1ec870493","observation_id":"10b63110-9f54-4891-857f-1c037293eae6","resolution":{"observed_at":"2026-08-16T11:34:01.107321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05132","last_updated":"2022-03-10T03:15:17Z","snapshot_observed_at":"2026-08-16T17:15:42.200323Z","submitted_at":"2022-03-10T03:15:17Z","title":"Compilable Neural Code Generation with Compiler Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05132","snapshot_observed_at":"2026-08-16T11:34:01.113204Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.113204Z"},"links":{"cited_paper":"/paper/2203.05132","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:10c879efd819327d39f048ffd69b31f0225d88f49196223f657edf47cd652fed","observation_id":"fa4551ca-25ad-40d3-8fe5-9d3882ba111e","resolution":{"observed_at":"2026-08-16T11:34:01.113204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00859","last_updated":"2021-09-02T12:21:06Z","snapshot_observed_at":"2026-08-15T23:44:55.691307Z","submitted_at":"2021-09-02T12:21:06Z","title":"CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00859","snapshot_observed_at":"2026-08-16T11:34:01.119213Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.119213Z"},"links":{"cited_paper":"/paper/2109.00859","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:58c7a1529a47b0bffab60f1d204024af4aba13aaaba5724662cbe346f93ffb22","observation_id":"398516e2-79c2-415d-9b20-23125f1c97bd","resolution":{"observed_at":"2026-08-16T11:34:01.119213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10719","last_updated":"2024-10-10T08:30:17Z","snapshot_observed_at":"2026-08-18T11:11:58.049967Z","submitted_at":"2024-04-16T16:51:53Z","title":"Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10719","snapshot_observed_at":"2026-08-16T11:34:01.124724Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.124724Z"},"links":{"cited_paper":"/paper/2404.10719","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:f28cd74c6845043372259d9e117cebd7b95bba75052a558b33fbe9d3c2d112d6","observation_id":"fa1daec4-b8fe-45d6-8380-4c4c15727c86","resolution":{"observed_at":"2026-08-16T11:34:01.124724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03173","last_updated":"2024-03-18T21:44:47Z","snapshot_observed_at":"2026-08-16T14:54:58.743590Z","submitted_at":"2023-10-04T21:40:36Z","title":"$\\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03173","snapshot_observed_at":"2026-08-16T11:34:01.131319Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.131319Z"},"links":{"cited_paper":"/paper/2310.03173","citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:0df3f21bfb25584a028b19c0f2f7ae2026eee3900a69f3dc9cd0c822e61e3482","observation_id":"faf30df7-b47f-4e34-b371-72887dd112ce","resolution":{"observed_at":"2026-08-16T11:34:01.131319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.625319Z","title":null,"venue":null,"work_id":"da77410c-562e-49be-aefe-18e5364ce22d","year":2024},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.136574Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:afb93da537241e99867366179c90ab0205c98941620db3ffca0163f47e1f2d82","observation_id":"fa58853a-e9b3-4738-b5e9-83afa419a6a9","resolution":{"observed_at":"2026-08-16T11:34:01.631352Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.140491Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.140491Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:b1c7dd48402c6233b29dd56accf16556384d8db03be064937017de015eae0887","observation_id":"f840f13b-3232-443b-b9d7-27e37de85a87","resolution":{"observed_at":"2026-08-16T11:34:01.140491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:34:01.145392Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-16T11:34:01.145392Z"},"links":{"citing_paper":"/paper/2504.15210"},"observation_digest":"sha256:2ec1700d7e550f44a54e4df0425e94255240cdadf4d8d046146cb8775ac25836","observation_id":"818e70fc-5439-4c19-a5be-974b11f2761a","resolution":{"observed_at":"2026-08-16T11:34:01.145392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.15210","last_updated":"2025-05-05T06:56:16Z","latest_version":2,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-18T06:50:37.132646Z","submitted_at":"2025-04-21T16:29:07Z","title":"Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":23},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 2 inbound Pith citation observations for arXiv:2504.15210."}