{"as_of":"2026-08-10T06:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c9c6847e22bbbbbf55b6ff71f2b1d23f5bea535ec2067b355346be62315a5bc0","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T14:26:32.233655Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-03T07:34:22.014373Z","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":2,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01806","snapshot_observed_at":"2026-08-03T07:34:22.014373Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.20147","last_updated":"2026-07-17T10:12:37Z","snapshot_observed_at":"2026-08-06T05:52:15.832126Z","submitted_at":"2026-01-28T00:45:28Z","title":"Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-03T07:34:22.014373Z"},"links":{"cited_paper":"/paper/2502.01806","citing_paper":"/paper/2601.20147"},"observation_digest":"sha256:c8828eeba210f3a99d18845f702de8c01d93ba7db0f4a2678a221a02c021c22c","observation_id":"2dbc3f5a-3f46-408b-b4a0-69202c2fdd76","resolution":{"observed_at":"2026-08-03T07:34:22.014373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"cited_work":{"arxiv_id":"2502.01806","doi":"10.48550/arxiv.2502.01806","metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01806","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Palacio, Antonio Mastropaolo, and Denys Poshyvanyk","venue":"ArXiv.org","work_id":"f1b15b83-424b-484b-a5b0-11ee542488bb","year":2025},"citing_paper":{"arxiv_id":"2604.13934","last_updated":"2026-04-15T14:46:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-15T14:46:12Z","title":"Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-10T12:43:14.903173Z"},"links":{"cited_paper":"/paper/2502.01806","citing_paper":"/paper/2604.13934"},"observation_digest":"sha256:9027bbe0b1c690f26ff7cce401e8a43001b91c902829a9de427eb991bedc6ef5","observation_id":"138eb725-05a6-4027-9857-2f63ce48527d","resolution":{"observed_at":"2026-05-10T12:45:23.438249Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.01806/citation-record","integrity":"/paper/2502.01806/integrity","json":"/paper/2502.01806/citation-record.json","paper":"/paper/2502.01806"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.025656Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.025656Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:ed6e8280d2620f76405bcaba173c938329edc1d1ae2c9b9bfdac8dd7fcdb7d40","observation_id":"1732006e-5864-400a-92d4-7854082647d7","resolution":{"observed_at":"2026-08-09T14:26:32.025656Z","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":"2026-08-09T14:26:33.135296Z","title":"Copilot website,","venue":null,"work_id":"30bf87fa-74f4-4cbd-8033-f47afa738371","year":2022},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.031816Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:9a3ab4eb224c2b5c4bf58a26e1b3d51cd037394a4b7248241ee33a0b2a487268","observation_id":"ecef56b3-0994-4fce-baf5-f6cc2cf6c5a2","resolution":{"observed_at":"2026-08-09T14:26:33.139835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:33.119722Z","title":"Chatgpt,","venue":null,"work_id":"6ddbd9bb-a1dd-4e0b-954a-6a9360b62970","year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.036861Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:630c49826bc237fc39da59157445a4c2985944a14fa1b7d1c4840f2213c88c67","observation_id":"3d2d0f74-7ccc-4529-8238-9accc0680ac9","resolution":{"observed_at":"2026-08-09T14:26:33.124675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.07839","last_updated":"2023-03-11T14:43:17Z","snapshot_observed_at":"2026-07-06T15:03:08.992538Z","submitted_at":"2023-03-11T14:43:17Z","title":"ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.07839","snapshot_observed_at":"2026-08-09T14:26:32.041733Z","title":"ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.041733Z"},"links":{"cited_paper":"/paper/2303.07839","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:7282e7759aee330d1c3f8b512d00af75123a6c33b43bf7d6ff0c191dcb90b72b","observation_id":"d9fd823e-7574-46fb-b446-6086a1ac45a6","resolution":{"observed_at":"2026-08-09T14:26:32.041733Z","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":"2026-08-09T14:26:33.102837Z","title":"Retrieval-based prompt selection for code-related few- shot learning,","venue":null,"work_id":"5c7ba6b1-7b54-402b-b5c2-40e6643101d5","year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.047119Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:3d64e9cebac5cf15a71199df5a35a624fddee2f456be59124940380fe2f705a7","observation_id":"a2da1550-3463-43d3-88b2-d6f4f6194720","resolution":{"observed_at":"2026-08-09T14:26:33.108839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:33.086918Z","title":"On the use of chatgpt for code review: Do devel- opers like reviews by chatgpt?","venue":null,"work_id":"d62b2a4b-15cd-43ab-8d48-5cf19ba25f37","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.052181Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:89f59ba4a7addb7bad44e4c67bd0bb1b8b3811146f8bb52b9c56e6bec7a7c6d1","observation_id":"0ae5cfbe-55df-44eb-96b8-9b5822e7950a","resolution":{"observed_at":"2026-08-09T14:26:33.091726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:33.071782Z","title":"Beyond code generation: An observational study of chatgpt usage in software engineering practice,","venue":null,"work_id":"a941fe93-1837-4bd5-b784-256b6ce814e8","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.057549Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:4f3d7c7da1ea1ac8dcd404385d234131bd7ae2b8fd2bb882e483f8f5e1491edf","observation_id":"e453acae-3705-4bcd-99b5-2576b2fdcf30","resolution":{"observed_at":"2026-08-09T14:26:33.076704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T14:26:32.061859Z","title":"Github copilot ai pair programmer: Asset or liability?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.061859Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:c2b1103401d6cd514e1ff292c40a4b511a098e9d0783ad7ae243461c0de2c00e","observation_id":"85492995-5f4b-44ea-abee-82967fa37399","resolution":{"observed_at":"2026-08-09T14:26:32.061859Z","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":"2026-08-09T14:26:33.046936Z","title":"Large language models for software engineering: A sys- tematic literature review,","venue":null,"work_id":"2393f08b-5ddc-4747-9b15-52ff87a90b67","year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.066240Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:f8b39d6247cedbce4280ecef690ae838ac2d5712277a16d53a507f3932046852","observation_id":"e6e4c928-bf85-4018-8ffa-694b24d54477","resolution":{"observed_at":"2026-08-09T14:26:33.051623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:33.032503Z","title":"Towards greener llms: Bringing energy-efficiency to the forefront of llm inference,","venue":null,"work_id":"6eba139e-6506-4a84-8e68-1e7bae22fc9e","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.070740Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:e9b4230649a16bc0ebe777a5ef902860f40f224eedc3bfdf4e4a00f470d12e2d","observation_id":"7fd04987-ae95-4c85-8e44-276a20310e21","resolution":{"observed_at":"2026-08-09T14:26:33.037197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.04325","last_updated":"2024-06-04T22:09:46Z","snapshot_observed_at":"2026-08-09T20:41:07.756820Z","submitted_at":"2022-10-26T00:28:40Z","title":"Will we run out of data? Limits of LLM scaling based on human-generated data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.04325","snapshot_observed_at":"2026-08-09T14:26:32.074862Z","title":"Will we run out of data? Limits of LLM scaling based on human-generated data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.074862Z"},"links":{"cited_paper":"/paper/2211.04325","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:82b0fe4097f52534d6325558a4e6286b0878d3639111910fa8b98dbd25d891cf","observation_id":"73549ae8-b156-4366-b838-528b5ee46227","resolution":{"observed_at":"2026-08-09T14:26:32.074862Z","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":"2026-08-09T14:26:33.018215Z","title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks,","venue":null,"work_id":"f4633627-02dd-4bf0-9b9a-e46694ad1ebc","year":2019},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.079652Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:0f6e437164dc7930b0a71fabe4e821fe2db69b6498e01f235b88108b384169c0","observation_id":"ba1ccdd6-8fd2-4636-bccb-74ada5e68cb3","resolution":{"observed_at":"2026-08-09T14:26:33.022925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:33.004096Z","title":"A unified approach to interpreting model predictions,","venue":null,"work_id":"5ff02a83-8d90-4dac-8b80-c460d06d59ff","year":2017},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.084378Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:5687e6c4364e0b081d7e6e55465a929b83696ea7db22925949b9aaa1ca1babbb","observation_id":"07aa61c6-3fc0-489d-b6d7-f32e608fdf82","resolution":{"observed_at":"2026-08-09T14:26:33.008860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.988851Z","title":"A value for n-person games,","venue":null,"work_id":"fabc1943-aea0-4d6f-9947-8afb0475b897","year":1953},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.088638Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:a2c404545df5c3f582035fb2e1909dc20a802f76368f6e1576369f333753ff69","observation_id":"daec273a-2530-4251-b6ca-e1b1dfe6bb0e","resolution":{"observed_at":"2026-08-09T14:26:32.993805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.974333Z","title":"Problems with shapley-value-based explanations as feature importance measures,","venue":null,"work_id":"c10b2ac4-c606-4c13-9e0c-ce0f07442db3","year":null},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.092956Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:ae7f1cc4b71a0f824f1d05f15846bed84aa0fa9236d1a12ba51673ae77e6425f","observation_id":"5234e0c3-c916-44a7-a180-a27125e5b5d3","resolution":{"observed_at":"2026-08-09T14:26:32.979358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.959691Z","title":"WWW: A unified framework for explaining what, where and why of neural networks by interpretation of neuron con- cepts,","venue":null,"work_id":"f103fdad-4c39-46aa-b852-2813719f4d7e","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.097605Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:1a6a14666e24472795d5263431245251a6002b267da2a6562ab158131da80e04","observation_id":"9b771afc-d0fb-4211-bb71-8c776b6c4a67","resolution":{"observed_at":"2026-08-09T14:26:32.964579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.944666Z","title":"The many shapley values for model explana- tion,","venue":null,"work_id":"6ee6bd43-bcce-44dd-86d3-46f4bd271cef","year":null},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.101743Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:c6f50dbdc34c76631c4c5d882995b32fafc96e9405f64344d8ab837809d5d160","observation_id":"0a62e6a3-c088-4761-ae74-1152a49d6fa1","resolution":{"observed_at":"2026-08-09T14:26:32.949529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.929664Z","title":"Interpretability of machine learning-based prediction models in healthcare,","venue":null,"work_id":"ec6882d2-a89a-4547-9b88-932581105bb2","year":2020},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.106159Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:53428ecde87a876ee5de8ebfd05b9df98ece78c07d2941efa9ba31e786dfea92","observation_id":"4e4b3e60-30ae-4fb9-ac36-f4e8f021980c","resolution":{"observed_at":"2026-08-09T14:26:32.934659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.914987Z","title":"Navigating the Complexities of AI: The Critical Role of Interpretability and Explainability in Ensuring Transparency and Trust,","venue":null,"work_id":"6b92ab78-be48-4a7b-811e-4826daecf634","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.110419Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:b5d680becfd0b0649a4d4284092ebd0a2b545c00d49947abf64359ca059c9f1a","observation_id":"6156ee57-04d5-4677-bc9e-773c5a89e6df","resolution":{"observed_at":"2026-08-09T14:26:32.920008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.899527Z","title":"Designing and Interpreting Probes with Control Tasks,","venue":null,"work_id":"3c3de2d7-4025-4e3b-bc9c-b3b09ef64063","year":2019},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.114624Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:924e93fae47e4dadce016d118244a162c15d905af90720160a707e4513de01e8","observation_id":"3a5be5b5-5ef3-407c-b68d-a9f9d72d8dfc","resolution":{"observed_at":"2026-08-09T14:26:32.904144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.884863Z","title":"Ast-probe: Recovering abstract syntax trees from hidden representations of pre-trained language models,","venue":null,"work_id":"bc47e0b6-6ea3-461e-8292-1753addae96b","year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.119045Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:90c2d8056be4722cec659d5d149dc040826dbdcdef96ae720903885209e317a3","observation_id":"d66a9432-32db-48b8-97c7-118fcb932b36","resolution":{"observed_at":"2026-08-09T14:26:32.889696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.870582Z","title":"Probing pretrained models of source codes,","venue":null,"work_id":"fa8ad483-1d94-4e13-aba7-b6e0f8c0986b","year":2022},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.123520Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:ba805dc4aea7e8dd48af6aa4f8710253faf83c88751174807fa7d17107b0c932","observation_id":"59e98c08-8725-45e1-b247-58fcac5569da","resolution":{"observed_at":"2026-08-09T14:26:32.875267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.854688Z","title":"Probing classifiers: Promises, shortcomings, and ad- vances,","venue":null,"work_id":"4c47dba8-80d4-4e04-94dc-cbc95ae73661","year":2022},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.127975Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:3ad69ba639341fb4f9ddc60eb0a8b26aa53ddb88a466b7bcfdc1cfb4cbab2d3e","observation_id":"05a6b4bf-e1b5-43f4-9d44-c2c7f45fd5cf","resolution":{"observed_at":"2026-08-09T14:26:32.860601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.840884Z","title":"Which syntactic capabilities are statistically learned by masked language models for code?","venue":null,"work_id":"968a3886-f72a-43a7-81d3-d5f04183af1c","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.132262Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:99c60bf8af052b8f12183d6d82f9431f7b555f4bc1441f5d1b765b3d71b6ca07","observation_id":"c5b56676-4cbf-4126-852b-3822955484a6","resolution":{"observed_at":"2026-08-09T14:26:32.845421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T14:26:32.136789Z","title":"Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.136789Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:bffe40841f97f32a960e6b6705bd039e6c174841b094b6b43da33c51c5fe698f","observation_id":"d0c9584e-ee5a-426a-9274-ecd5d60b8b07","resolution":{"observed_at":"2026-08-09T14:26:32.136789Z","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":"2026-08-09T14:26:32.825980Z","title":"Toward a Theory of Causation for Interpreting Neural Code Models ,","venue":null,"work_id":"de4047a8-6922-42b3-976e-b385918a8c89","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.141321Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:e07f58639edcd6d5552fd821b8672fc35578bb8f07424fefc6734a6d7944277c","observation_id":"97e792ae-c1ee-4600-ab54-439bfe338e0e","resolution":{"observed_at":"2026-08-09T14:26:32.831237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08155","last_updated":"2020-09-18T15:38:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-19T13:09:07Z","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08155","snapshot_observed_at":"2026-08-09T14:26:32.145669Z","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.145669Z"},"links":{"cited_paper":"/paper/2002.08155","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:04b5b382ad1e756f99ef54cff1b6cc2198537aad3001976573be39c2d3e20854","observation_id":"efb072cc-def6-45c2-a1ed-0b43484b4c00","resolution":{"observed_at":"2026-08-09T14:26:32.145669Z","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":"2026-08-09T14:26:32.810273Z","title":"Bert-based github issue report classification,","venue":null,"work_id":"51ed7c2b-84e1-4131-b7fb-7ca4b3dce69d","year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.150623Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:20a112940797c413dddc07775f08d1e5f4ef8bb32e98b050babe727e3cc3e31a","observation_id":"5cb7da1f-3fbb-4a3c-9b94-470af0d81b7f","resolution":{"observed_at":"2026-08-09T14:26:32.815028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.794847Z","title":"Bert for sentiment classification in software engineering,","venue":null,"work_id":"740c9555-efad-4483-aa0e-6a56380330fc","year":2021},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.155195Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:3e7621dfaa9b63ef7683be2f9a8e4c25ebb6aa6fc362f97ac11ad865ec2ca7ba","observation_id":"ec601492-995d-4d09-9cd8-ca7b231b879c","resolution":{"observed_at":"2026-08-09T14:26:32.799779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.779388Z","title":"Using bert to predict bug-fixing time,","venue":null,"work_id":"b2cd42bf-d202-446c-8e38-b6f332862915","year":2020},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.159499Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:b1bc675a4aeaab8037fd30d729b542b858ee75286b755b88016013ea623b1f20","observation_id":"cb75ffe6-2092-4ca8-9e8b-816dfe49efeb","resolution":{"observed_at":"2026-08-09T14:26:32.784679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.764781Z","title":"Using a nearest-neighbour, bert-based approach for scalable clone detection,","venue":null,"work_id":"ed7f00bb-047d-4eef-bfe0-d7d5bfbbbae8","year":2022},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.163724Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:8fe413f4632b70fe20b7159d160b23dc11f8d8c5caf7e43d5d223861f077ab55","observation_id":"0f9a7144-8ece-40b8-8c9a-a6baca9bd419","resolution":{"observed_at":"2026-08-09T14:26:32.769468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.03496","last_updated":"2019-09-08T16:14:31Z","snapshot_observed_at":"2026-08-09T15:14:45.070830Z","submitted_at":"2019-09-08T16:14:31Z","title":"Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.03496","snapshot_observed_at":"2026-08-09T14:26:32.168028Z","title":"Devign: Effective Vulnerability Identification by Learn- ing Comprehensive Program Semantics via Graph Neural Networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.168028Z"},"links":{"cited_paper":"/paper/1909.03496","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:5904bfd45a466b857f62556d2f7f5956f4480ee1b0c91f95667591fa28d36573","observation_id":"b172cd1f-b723-4234-82f6-b52d55f617fa","resolution":{"observed_at":"2026-08-09T14:26:32.168028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04664","last_updated":"2021-03-16T08:28:37Z","snapshot_observed_at":"2026-07-06T10:39:42.676631Z","submitted_at":"2021-02-09T06:16:25Z","title":"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04664","snapshot_observed_at":"2026-08-09T14:26:32.172657Z","title":"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.172657Z"},"links":{"cited_paper":"/paper/2102.04664","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:f33fac0a5b6afd5df927ba0f1043380629c5193d523605e7a20fd32f6c12ca68","observation_id":"d5d77221-eca3-4e2f-a9ed-2447426d808c","resolution":{"observed_at":"2026-08-09T14:26:32.172657Z","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":"2026-08-09T14:26:32.750444Z","title":"CAT-probing: A metric-based approach to interpret how pre-trained models for programming language attend code structure,","venue":null,"work_id":"561678a0-de33-4492-bd80-4a8bef0a6325","year":2022},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.177373Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:3cf338586f3c9f016cd7205d92fad2cc3a6f001467b464884508b8013a2527b1","observation_id":"8267fb21-c293-4415-aa71-93e5c3b59f01","resolution":{"observed_at":"2026-08-09T14:26:32.755247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.734434Z","title":"A Critical Study of What Code-LLMs (Do Not) Learn,","venue":null,"work_id":"9ffc612c-faf9-48e3-b1ad-f1b49f056aee","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.181648Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:fb466fdf6e2a68d714272516da49a9206e673760ca96df0a5ccda6558e2af7a8","observation_id":"6beade1a-13b9-435b-ad2d-a104ae8bf967","resolution":{"observed_at":"2026-08-09T14:26:32.740107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.716875Z","title":"AutoFocus: Interpreting attention-based neural networks by code perturbation,","venue":null,"work_id":"db73a13d-183c-4e10-a926-f70538c0369e","year":2019},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.185836Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:8dd2633617dbc9ce6754f620e6353fb1c2b4fffc239df087b56eb273a46052f8","observation_id":"cc1ab3f7-699e-4bea-99ac-0d2e5c6edc20","resolution":{"observed_at":"2026-08-09T14:26:32.722332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04868","last_updated":"2024-07-05T21:13:41Z","snapshot_observed_at":"2026-08-09T10:19:30.826021Z","submitted_at":"2024-07-05T21:13:41Z","title":"Looking into Black Box Code Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04868","snapshot_observed_at":"2026-08-09T14:26:32.190212Z","title":"Looking into Black Box Code Language Models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.190212Z"},"links":{"cited_paper":"/paper/2407.04868","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:3527cb00cf6c10d9190f1d15c0241f1a4b4e262fb6267c411df46df735940da5","observation_id":"21584b34-a13e-4903-9407-959086ec5683","resolution":{"observed_at":"2026-08-09T14:26:32.190212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08890","last_updated":"2024-07-11T23:16:44Z","snapshot_observed_at":"2026-07-06T18:45:05.905995Z","submitted_at":"2024-07-11T23:16:44Z","title":"DeepCodeProbe: Towards Understanding What Models Trained on Code Learn","version":1},"cited_work":{"arxiv_id":"2407.08890","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.08890","snapshot_observed_at":"2026-08-09T14:26:32.363070Z","title":"DeepCodeProbe: Towards Understanding What Models Trained on Code Learn","venue":"cs.SE","work_id":"07d18998-1e84-45a5-ad42-7396f1681f87","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.194899Z"},"links":{"cited_paper":"/paper/2407.08890","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:de82272714f484767a50bf2707ca16dfa6ecd427addcca3276fb63dee89e31b5","observation_id":"32074974-e635-435f-b351-9ade75f2ff46","resolution":{"observed_at":"2026-08-09T14:26:32.368034Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13888","last_updated":"2024-08-25T16:37:26Z","snapshot_observed_at":"2026-07-06T19:05:44.820697Z","submitted_at":"2024-08-25T16:37:26Z","title":"Enhancing SQL Query Generation with Neurosymbolic Reasoning","version":1},"cited_work":{"arxiv_id":"2408.13888","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.13888","snapshot_observed_at":"2026-08-09T14:26:32.342304Z","title":"Enhancing SQL Query Generation with Neurosymbolic Reasoning","venue":"cs.DB","work_id":"3ba9a0c0-9b3e-4720-ba46-17a42f9cfceb","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.199501Z"},"links":{"cited_paper":"/paper/2408.13888","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:1dc34d2a5c468f9fd2646dae54dfcb40635c4e6f3528b8064c49d381d14101dd","observation_id":"ca8f49db-ff5a-441f-97bc-28513a6f44ad","resolution":{"observed_at":"2026-08-09T14:26:32.347855Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.700389Z","title":"Ns3: neuro-symbolic semantic code search,","venue":null,"work_id":"3caee5bb-8a1c-4cc0-94b9-0951ac7de9a0","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.203657Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:b25f5f1f83da4761e35386e577bb5c2370289453555f6be3b12a44986f45f6b8","observation_id":"91c9357b-96f9-4776-961e-6462d324701b","resolution":{"observed_at":"2026-08-09T14:26:32.705543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.06755","last_updated":"2024-10-30T17:22:41Z","snapshot_observed_at":"2026-07-06T15:41:21.038156Z","submitted_at":"2023-06-11T19:47:52Z","title":"CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution","version":4},"cited_work":{"arxiv_id":"2306.06755","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.06755","snapshot_observed_at":"2026-08-09T14:26:32.317854Z","title":"CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution","venue":"cs.PL","work_id":"bf04f328-4c1a-44de-9968-5ab3f9cad068","year":2023},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.207664Z"},"links":{"cited_paper":"/paper/2306.06755","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:84b23b0296c7337c1fa43e6a41e36ac4edd383f257609373a5435b0c00f42bcc","observation_id":"06ec8b85-189d-4096-b5fd-0b5c52e59b08","resolution":{"observed_at":"2026-08-09T14:26:32.324727Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01855","last_updated":"2016-11-06T22:23:56Z","snapshot_observed_at":"2026-07-06T05:17:35.666507Z","submitted_at":"2016-11-06T22:23:56Z","title":"Neuro-Symbolic Program Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01855","snapshot_observed_at":"2026-08-09T14:26:32.212033Z","title":"Neuro-Symbolic Program Synthesis,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.212033Z"},"links":{"cited_paper":"/paper/1611.01855","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:7d4c57c532dbc5acabc4b0fa040a678f20a69d5a6478f8665275e528fabad1bd","observation_id":"2a338df7-0a8f-4c2c-b934-ebd72fc9dca3","resolution":{"observed_at":"2026-08-09T14:26:32.212033Z","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":"2026-08-09T14:26:32.684957Z","title":"Programming with a differentiable forth interpreter,","venue":null,"work_id":"49442ae5-e820-4226-a72d-5c9f8d595331","year":2017},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.216590Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:2a6d897d720c7b36cf7770da505335b12db604cbf8afa62880d0e2301cf9aa88","observation_id":"8b3b2453-8227-41de-906a-4299baa68078","resolution":{"observed_at":"2026-08-09T14:26:32.689801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.669873Z","title":"Learning continuous semantic representations of symbolic expressions,","venue":null,"work_id":"0560450a-fd88-4b98-9a8d-d08015b5977d","year":2017},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.220684Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:2681123b30adbc1ea3af4ac83b6e197af4cdb408f0bc7bf81cc6fd9add320781","observation_id":"e8b323ec-8557-49f9-826e-03942eedf557","resolution":{"observed_at":"2026-08-09T14:26:32.674872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T14:26:32.654843Z","title":"An interpretable error correction method for enhancing code-to-code translation,","venue":null,"work_id":"7d1cdec0-b2e7-4612-b685-5e716e386624","year":2024},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.224944Z"},"links":{"citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:bb038b4529604b8bd9602bf4021c074ecb2c487a47a1cbec3de10efc549f74de","observation_id":"c469bff0-2689-42b6-bf51-18bd2146a201","resolution":{"observed_at":"2026-08-09T14:26:32.659677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.11054","last_updated":"2017-10-30T16:32:45Z","snapshot_observed_at":"2026-07-06T06:06:48.906444Z","submitted_at":"2017-10-30T16:32:45Z","title":"Semantic Code Repair using Neuro-Symbolic Transformation Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.11054","snapshot_observed_at":"2026-08-09T14:26:32.229169Z","title":"Semantic Code Repair using Neuro-Symbolic Trans- formation Networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.229169Z"},"links":{"cited_paper":"/paper/1710.11054","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:a88207df96a842caf3f0e78ce99f87aef3093b119168ba59dc6c5151bdbf568a","observation_id":"dcb59237-1345-48a3-8845-9324387de73e","resolution":{"observed_at":"2026-08-09T14:26:32.229169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06643","last_updated":"2022-04-13T21:39:01Z","snapshot_observed_at":"2026-08-05T22:45:07.974550Z","submitted_at":"2022-04-13T21:39:01Z","title":"Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06643","snapshot_observed_at":"2026-08-09T14:26:32.233655Z","title":"Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T14:26:32.233655Z"},"links":{"cited_paper":"/paper/2204.06643","citing_paper":"/paper/2502.01806"},"observation_digest":"sha256:bd8bdfd73bc4e66dac06531cc45313b522ff594a13b2eface4e96aaa2ca5e9a3","observation_id":"b32dd3b2-33f0-423d-9182-4c206b50fa10","resolution":{"observed_at":"2026-08-09T14:26:32.233655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.01806","last_updated":"2025-02-03T20:38:58Z","latest_version":1,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-09T14:20:54.490048Z","submitted_at":"2025-02-03T20:38:58Z","title":"Toward Neurosymbolic Program Comprehension"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":3,"verified_fuzzy":32},"total_outbound_references":47},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2502.01806."}