{"as_of":"2026-08-17T19:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2739e028a96403a9f68b9e821e75bde0872ec7fd6933e933c8185772f012ff27","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":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:52:18.902876Z","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":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":"2311.02962","doi":"10.48550/arxiv.2311.02962","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Retrieval-augmented code generation for universal information extraction","venue":"arXiv (Cornell University)","work_id":"4f77b614-8d50-4807-8e88-d327c72088a5","year":2023},"citing_paper":{"arxiv_id":"2402.19473","last_updated":"2024-06-21T08:26:36Z","snapshot_observed_at":"2026-08-13T05:27:55.126585Z","submitted_at":"2024-02-29T18:59:01Z","title":"Retrieval-Augmented Generation for AI-Generated Content: A Survey","version":6},"reference_index":258,"source":"pdf_text","source_observed_at":"2026-05-15T13:32:17.177021Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2402.19473"},"observation_digest":"sha256:c4145f658acbb51e9bb951594a99a9ceb3a4283e53f7625c87468b2e27041483","observation_id":"24f7bc6f-0c8e-4832-b98a-4f7bbe8e262b","resolution":{"observed_at":"2026-05-15T13:32:17.554723Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-11T15:05:32.462222Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11344","last_updated":"2024-12-16T00:02:38Z","snapshot_observed_at":"2026-08-15T22:45:21.191800Z","submitted_at":"2024-12-16T00:02:38Z","title":"Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T15:05:32.462222Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2412.11344"},"observation_digest":"sha256:edb6aa33aca7a8550f4e7424a635b2271a75a48b2803cc38bf0e40262833905f","observation_id":"f0ab4640-f7c4-450b-8abd-64a9a3afe849","resolution":{"observed_at":"2026-08-11T15:05:32.462222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-15T21:52:18.902876Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08690","last_updated":"2025-05-13T15:47:54Z","snapshot_observed_at":"2026-08-17T14:32:11.113252Z","submitted_at":"2025-05-13T15:47:54Z","title":"Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T21:52:18.902876Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2505.08690"},"observation_digest":"sha256:59689c23359b6a55558f74a0dc32ee93272ef0cc4bc21068c6b6838e750c58be","observation_id":"2266646e-f5d2-4bd2-a3f1-613a6de1cf46","resolution":{"observed_at":"2026-08-15T21:52:18.902876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-07T15:10:10.272006Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16107","last_updated":"2025-05-22T01:28:23Z","snapshot_observed_at":"2026-08-15T18:30:33.195032Z","submitted_at":"2025-05-22T01:28:23Z","title":"MPL: Multiple Programming Languages with Large Language Models for Information Extraction","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T15:10:10.272006Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2505.16107"},"observation_digest":"sha256:c2dea10c69659f6a65d84f01bd7ff916ed87797f7fe77221f2dda7dca4951562","observation_id":"e5babf06-090f-40c3-96c3-8a697415b7b6","resolution":{"observed_at":"2026-08-07T15:10:10.272006Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-07T11:50:45.602034Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.01276","last_updated":"2025-06-02T03:12:44Z","snapshot_observed_at":"2026-08-13T16:13:05.564750Z","submitted_at":"2025-06-02T03:12:44Z","title":"Schema as Parameterized Tools for Universal Information Extraction","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T11:50:45.602034Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2506.01276"},"observation_digest":"sha256:8601e983b5320efc179e63d379f52636458719c1127aad1d0c6c62c16c701112","observation_id":"b5c16ce1-9fea-4625-8450-e8313d88134b","resolution":{"observed_at":"2026-08-07T11:50:45.602034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-07T05:51:44.509211Z","title":"Harsha Gurulingappa, Abdul Mateen Rajput, Angus Roberts, Juliane Fluck, Martin Hofmann-Apitius, and Luca Toldo","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.06881","last_updated":"2025-06-07T18:01:25Z","snapshot_observed_at":"2026-08-14T20:26:04.679689Z","submitted_at":"2025-06-07T18:01:25Z","title":"KnowCoder-V2: Deep Knowledge Analysis","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:51:44.509211Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2506.06881"},"observation_digest":"sha256:7de9f8cdb2b80b0713e55123dbba8a49b0ebc2bf0f6b7f8768423c213c5044d2","observation_id":"c532402f-7083-4cc6-bc65-4c0079cc31dc","resolution":{"observed_at":"2026-08-07T05:51:44.509211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-06T23:02:57.375769Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.20081","last_updated":"2025-06-26T04:06:50Z","snapshot_observed_at":"2026-08-10T13:33:29.822475Z","submitted_at":"2025-06-25T01:44:28Z","title":"SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T23:02:57.375769Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2506.20081"},"observation_digest":"sha256:e6fe9b9c25a53ed1c29cb068128ffb87036c0b2fac854c816858a18e7ce047c9","observation_id":"ba662dbb-808f-49c1-bf8e-465ca5fbec41","resolution":{"observed_at":"2026-08-06T23:02:57.375769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-15T18:13:22.777857Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18812","last_updated":"2025-07-24T21:23:44Z","snapshot_observed_at":"2026-08-16T14:20:04.527551Z","submitted_at":"2025-07-24T21:23:44Z","title":"MemoCoder: Automated Function Synthesis using LLM-Supported Agents","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T18:13:22.777857Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2507.18812"},"observation_digest":"sha256:3d5936ffb437d2064c0f5a97a46783d30ff3d4c57c71d68cc22e1d44cc54bd53","observation_id":"33e82b85-6b94-4224-a1da-03964cc0b3f2","resolution":{"observed_at":"2026-08-15T18:13:22.777857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-05T12:54:05.422943Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01158","last_updated":"2025-09-09T07:14:16Z","snapshot_observed_at":"2026-08-12T01:44:42.010259Z","submitted_at":"2025-09-01T06:28:33Z","title":"Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-05T12:54:05.422943Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2509.01158"},"observation_digest":"sha256:10959d27c550a18964a3b04af24e27ea58c8950cc8657bfe2348eddebecff58b","observation_id":"9d2bd02b-5d17-42ff-861c-b3de244b59a3","resolution":{"observed_at":"2026-08-05T12:54:05.422943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":"2311.02962","doi":"10.48550/arxiv.2311.02962","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Retrieval-augmented code generation for universal information extraction","venue":"arXiv (Cornell University)","work_id":"4f77b614-8d50-4807-8e88-d327c72088a5","year":2023},"citing_paper":{"arxiv_id":"2606.29407","last_updated":"2026-06-28T14:01:52Z","snapshot_observed_at":"2026-08-15T23:59:45.134273Z","submitted_at":"2026-06-28T14:01:52Z","title":"LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-30T07:33:12.712241Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2606.29407"},"observation_digest":"sha256:b6f9db9fbe2ca0e430db23c70938f723f800e183f22c62ebc425a3e0bb8d35b2","observation_id":"4c4cf28e-de65-49d7-b432-4f37bfe33edd","resolution":{"observed_at":"2026-06-30T07:34:20.787392Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":"2311.02962","doi":"10.48550/arxiv.2311.02962","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Retrieval-augmented code generation for universal information extraction","venue":"arXiv (Cornell University)","work_id":"4f77b614-8d50-4807-8e88-d327c72088a5","year":2023},"citing_paper":{"arxiv_id":"2606.30914","last_updated":"2026-06-29T21:03:32Z","snapshot_observed_at":"2026-08-15T05:09:41.109171Z","submitted_at":"2026-06-29T21:03:32Z","title":"Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-07-01T01:46:18.576571Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2606.30914"},"observation_digest":"sha256:a1163ccc4ede6f6895c97121040effb791fa470ccc259a92a692e8d0aaf4d6b0","observation_id":"4bf0870c-ded6-41cb-a199-e5814cf194d0","resolution":{"observed_at":"2026-07-01T06:05:29.546824Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02962","snapshot_observed_at":"2026-07-30T22:49:43.135996Z","title":"CoRR , volume =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23420","last_updated":"2026-07-26T02:35:47Z","snapshot_observed_at":"2026-08-14T07:07:48.520338Z","submitted_at":"2026-07-26T02:35:47Z","title":"LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-07-30T22:49:43.135996Z"},"links":{"cited_paper":"/paper/2311.02962","citing_paper":"/paper/2607.23420"},"observation_digest":"sha256:03f8c00d1716db28007e829d73e211a15e534260b8e4691b8eb532e1476d94b1","observation_id":"56294a99-9ea6-4d02-868a-f9ae9d2d7aa0","resolution":{"observed_at":"2026-07-30T22:49:43.135996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2311.02962/citation-record","integrity":"/paper/2311.02962/integrity","json":"/paper/2311.02962/citation-record.json","paper":"/paper/2311.02962"},"outbound":[],"paper":{"arxiv_id":"2311.02962","last_updated":"2023-11-06T09:03:21Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T14:45:44.553972Z","submitted_at":"2023-11-06T09:03:21Z","title":"Retrieval-Augmented Code Generation for Universal Information Extraction"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2311.02962."}