{"as_of":"2026-08-14T18:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:124d251c52b9bb7138ae5ba99e1cf3ef45ce5ab58d2746ec30d0b7eb413a9a8e","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:09:17.518402Z","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":"2026-05-12T05:51:24.292614Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.07700","last_updated":"2023-07-15T04:03:17Z","snapshot_observed_at":"2026-08-13T20:50:46.663720Z","submitted_at":"2023-07-15T04:03:17Z","title":"NeurASP: Embracing Neural Networks into Answer Set Programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07700","snapshot_observed_at":"2026-08-12T11:09:17.518402Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.18564","last_updated":"2024-12-12T16:03:30Z","snapshot_observed_at":"2026-08-13T07:15:00.264918Z","submitted_at":"2024-11-27T18:04:05Z","title":"Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-12T11:09:17.518402Z"},"links":{"cited_paper":"/paper/2307.07700","citing_paper":"/paper/2411.18564"},"observation_digest":"sha256:ed748a44cfec62c90380a8a170369c40b8e1d7de36d043a747c5ebfaf048c49b","observation_id":"604c28dd-b66f-4946-a48a-11348f9b4c07","resolution":{"observed_at":"2026-08-12T11:09:17.518402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07700","last_updated":"2023-07-15T04:03:17Z","snapshot_observed_at":"2026-08-13T20:50:46.663720Z","submitted_at":"2023-07-15T04:03:17Z","title":"NeurASP: Embracing Neural Networks into Answer Set Programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07700","snapshot_observed_at":"2026-08-10T16:30:41.953603Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.16368","last_updated":"2025-02-04T21:21:49Z","snapshot_observed_at":"2026-08-13T22:52:26.103958Z","submitted_at":"2025-01-22T18:52:41Z","title":"Foundation Models for CPS-IoT: Opportunities and Challenges","version":2},"reference_index":137,"source":"pdf_text","source_observed_at":"2026-08-10T16:30:41.953603Z"},"links":{"cited_paper":"/paper/2307.07700","citing_paper":"/paper/2501.16368"},"observation_digest":"sha256:11e74898627a07943352872f141a54eefc1dde7d2918ec1a364c7ec42f87121b","observation_id":"3de020dd-6761-4242-a0c0-98dc371e8dcd","resolution":{"observed_at":"2026-08-10T16:30:41.953603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07700","last_updated":"2023-07-15T04:03:17Z","snapshot_observed_at":"2026-08-13T20:50:46.663720Z","submitted_at":"2023-07-15T04:03:17Z","title":"NeurASP: Embracing Neural Networks into Answer Set Programming","version":1},"cited_work":{"arxiv_id":"2307.07700","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.07700","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2307.07700 , year=","venue":null,"work_id":"d6af6d5a-1868-4f83-959d-97546d4b2991","year":null},"citing_paper":{"arxiv_id":"2605.10279","last_updated":"2026-05-11T09:39:59Z","snapshot_observed_at":"2026-07-06T23:22:13.774652Z","submitted_at":"2026-05-11T09:39:59Z","title":"DeepLog: A Software Framework for Modular Neurosymbolic AI","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-12T04:54:15.203914Z"},"links":{"cited_paper":"/paper/2307.07700","citing_paper":"/paper/2605.10279"},"observation_digest":"sha256:91a3c2ce569e692655db339f1fb05f513e97a25f0775d905c9b4eba7b935d633","observation_id":"34bb2909-693a-4995-a6eb-41d223978ee7","resolution":{"observed_at":"2026-05-12T05:51:24.299689Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07700","last_updated":"2023-07-15T04:03:17Z","snapshot_observed_at":"2026-08-13T20:50:46.663720Z","submitted_at":"2023-07-15T04:03:17Z","title":"NeurASP: Embracing Neural Networks into Answer Set Programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07700","snapshot_observed_at":"2026-08-01T09:59:08.626059Z","title":"2020 , pages =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20402","last_updated":"2026-08-07T22:04:09Z","snapshot_observed_at":"2026-08-14T15:12:28.323464Z","submitted_at":"2026-07-22T17:38:39Z","title":"SoftReason: A Fully Differentiable Neuro-Soft-Symbolic Deductive Reasoning Architecture over High-Dimensional Perceptual Data","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T09:59:08.626059Z"},"links":{"cited_paper":"/paper/2307.07700","citing_paper":"/paper/2607.20402"},"observation_digest":"sha256:b50d3e9be57574c1550210ad69d236c76418afde056003e98edd93fa3fcdf24d","observation_id":"0b77608e-0c8b-492b-995c-f078053339e1","resolution":{"observed_at":"2026-08-01T09:59:08.626059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07700","last_updated":"2023-07-15T04:03:17Z","snapshot_observed_at":"2026-08-13T20:50:46.663720Z","submitted_at":"2023-07-15T04:03:17Z","title":"NeurASP: Embracing Neural Networks into Answer Set Programming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07700","snapshot_observed_at":"2026-07-31T06:00:23.116955Z","title":"Neurasp: Embracing neural networks into answer set programming.arXiv preprint arXiv:2307.07700, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28481","last_updated":"2026-07-30T16:33:26Z","snapshot_observed_at":"2026-08-09T20:13:25.593762Z","submitted_at":"2026-07-30T16:33:26Z","title":"A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-31T06:00:23.116955Z"},"links":{"cited_paper":"/paper/2307.07700","citing_paper":"/paper/2607.28481"},"observation_digest":"sha256:9b36b13b607bad52675f79d167fb56f8fd618cf44589ecccd65cd791d2cfb0d8","observation_id":"a5465f8e-efa0-4409-a1df-91c965fcd778","resolution":{"observed_at":"2026-07-31T06:00:23.116955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2307.07700/citation-record","integrity":"/paper/2307.07700/integrity","json":"/paper/2307.07700/citation-record.json","paper":"/paper/2307.07700"},"outbound":[],"paper":{"arxiv_id":"2307.07700","last_updated":"2023-07-15T04:03:17Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-13T20:50:46.663720Z","submitted_at":"2023-07-15T04:03:17Z","title":"NeurASP: Embracing Neural Networks into Answer Set Programming"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.07700."}