{"as_of":"2026-08-20T19:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:faa121622bce363f6fef89090ea23c9bbc217b8ed872a9fd4860751f70ec47ff","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:34:01.107321Z","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":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"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":"2026-08-09T05:32:24.190024Z","title":"Python symbolic execution with llm-powered code generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.18474","last_updated":"2025-02-05T14:27:17Z","snapshot_observed_at":"2026-08-13T04:40:04.565039Z","submitted_at":"2025-02-05T14:27:17Z","title":"A Contemporary Survey of Large Language Model Assisted Program Analysis","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-09T05:32:24.190024Z"},"links":{"cited_paper":"/paper/2409.09271","citing_paper":"/paper/2502.18474"},"observation_digest":"sha256:61045d144c489d20c15f12b072e80705f4efe976530894a85986fb81f0ef7c6b","observation_id":"cdc3fd9c-bde4-4a69-9dff-47532bd72e59","resolution":{"observed_at":"2026-08-09T05:32:24.190024Z","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":"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":"2026-08-06T23:10:14.656505Z","title":"Python symbolic execution with llm-powered code generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19897","last_updated":"2025-06-24T13:02:35Z","snapshot_observed_at":"2026-08-08T11:24:18.587635Z","submitted_at":"2025-06-24T13:02:35Z","title":"Can LLMs Replace Humans During Code Chunking?","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:14.656505Z"},"links":{"cited_paper":"/paper/2409.09271","citing_paper":"/paper/2506.19897"},"observation_digest":"sha256:469b326f35be5f0c55bf236a3bfb3a5b8f264f962f47d691857cec22a95bc156","observation_id":"ac872aab-64d6-4223-bb9b-5bdc16925ea5","resolution":{"observed_at":"2026-08-06T23:10:14.656505Z","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":"2026-08-03T20:51:08.346652Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.18288","last_updated":"2026-06-18T06:58:18Z","snapshot_observed_at":"2026-08-16T19:35:33.198834Z","submitted_at":"2025-11-23T04:54:48Z","title":"Can Large Language Models Reason About Complex Execution Paths? An Empirical Study on Python","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T20:51:08.346652Z"},"links":{"cited_paper":"/paper/2409.09271","citing_paper":"/paper/2511.18288"},"observation_digest":"sha256:a34f4323b2b133658ab808a16cfa492726bcfada8a2d059975886086df33336e","observation_id":"8032930b-5cd9-43ca-8ac9-6124b50f5a7e","resolution":{"observed_at":"2026-08-03T20:51:08.346652Z","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":"2409.09271","doi":"10.48550/arxiv.2409.09271","metadata_source":"arxiv_reference","pith_arxiv_id":"2409.09271","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Python Symbolic Execution with LLM-powered Code Generation, September 2024","venue":"arXiv (Cornell University)","work_id":"239b7f32-4891-458c-95ae-433b49dc0b57","year":2024},"citing_paper":{"arxiv_id":"2512.12109","last_updated":"2026-05-06T16:33:04Z","snapshot_observed_at":"2026-07-06T22:38:54.914862Z","submitted_at":"2025-12-13T00:53:26Z","title":"A Neuro-Symbolic Framework for Accountability in Public-Sector AI","version":4},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-05-16T23:24:56.685420Z"},"links":{"cited_paper":"/paper/2409.09271","citing_paper":"/paper/2512.12109"},"observation_digest":"sha256:e78e891d7eb971f030c51679970504c596ccb9daf111be7c2ac710ea58ee8086","observation_id":"f4d909c0-b32d-459e-813f-b8f494cca4fe","resolution":{"observed_at":"2026-05-16T23:28:40.993296Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2409.09271","doi":"10.48550/arxiv.2409.09271","metadata_source":"arxiv_reference","pith_arxiv_id":"2409.09271","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Python Symbolic Execution with LLM-powered Code Generation, September 2024","venue":"arXiv (Cornell University)","work_id":"239b7f32-4891-458c-95ae-433b49dc0b57","year":2024},"citing_paper":{"arxiv_id":"2606.26545","last_updated":"2026-06-25T02:43:48Z","snapshot_observed_at":"2026-08-16T17:39:06.452384Z","submitted_at":"2026-06-25T02:43:48Z","title":"ConcoLixir: Reactive LLM Discovery Oracles for Python Concolic Testing","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T04:30:51.720496Z"},"links":{"cited_paper":"/paper/2409.09271","citing_paper":"/paper/2606.26545"},"observation_digest":"sha256:f05254d7ee8ed71f49b5e8295038fcbc881d0b3ebf5f25326dd8fa37dddeba4b","observation_id":"064d76ea-ca15-4fc0-9137-0fbdb91452b0","resolution":{"observed_at":"2026-06-26T05:09:00.761389Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2409.09271","doi":"10.48550/arxiv.2409.09271","metadata_source":"arxiv_reference","pith_arxiv_id":"2409.09271","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Python Symbolic Execution with LLM-powered Code Generation, September 2024","venue":"arXiv (Cornell University)","work_id":"239b7f32-4891-458c-95ae-433b49dc0b57","year":2024},"citing_paper":{"arxiv_id":"2607.02333","last_updated":"2026-07-02T15:37:54Z","snapshot_observed_at":"2026-08-15T04:44:36.947884Z","submitted_at":"2026-07-02T15:37:54Z","title":"Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-03T08:42:04.804042Z"},"links":{"cited_paper":"/paper/2409.09271","citing_paper":"/paper/2607.02333"},"observation_digest":"sha256:8cb136bb99fee570fcec6b2c5f9b2f0acafd08c3954b459caebfc29a37ec685c","observation_id":"6422d228-54ab-4031-bddf-6200b2dd5d2c","resolution":{"observed_at":"2026-07-03T08:47:49.669884Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2409.09271/citation-record","integrity":"/paper/2409.09271/integrity","json":"/paper/2409.09271/citation-record.json","paper":"/paper/2409.09271"},"outbound":[],"paper":{"arxiv_id":"2409.09271","last_updated":"2024-09-14T02:43:20Z","latest_version":1,"primary_category":"cs.SE","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"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2409.09271."}