{"as_of":"2026-08-07T11:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bef8b608899fab5b69b538912681dee4886b1d4918620aff3948cbb65c06197e","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:45:35.444743Z","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":7,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-06T14:45:35.444743Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.17979","last_updated":"2025-07-23T23:00:24Z","snapshot_observed_at":"2026-08-06T14:36:49.239136Z","submitted_at":"2025-07-23T23:00:24Z","title":"SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T14:45:35.444743Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2507.17979"},"observation_digest":"sha256:bfdf1eebb4f6925e275650a641282d31c4e2ef9ebcea799b9bfc72dd0ea71b3c","observation_id":"45326256-50c5-470f-a46e-7afd779d0f83","resolution":{"observed_at":"2026-08-06T14:45:35.444743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-06T14:00:10.007792Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.19863","last_updated":"2025-07-26T08:37:11Z","snapshot_observed_at":"2026-08-06T14:00:09.392366Z","submitted_at":"2025-07-26T08:37:11Z","title":"Anchoring Trends: Mitigating Social Media Popularity Prediction Drift via Feature Clustering and Expansion","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T14:00:10.007792Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2507.19863"},"observation_digest":"sha256:165d8594c681331bf0a30c73d0a441c7708abe81784e82aa6c4a507e7d3b5bd3","observation_id":"7452ab75-a7b2-4021-98b5-0ecba84b1018","resolution":{"observed_at":"2026-08-06T14:00:10.007792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":"2305.03403","doi":"10.48550/arxiv.2305.03403","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Context-aware automated feature engineering (caafe)","venue":"arXiv (Cornell University)","work_id":"fb5f1307-2f3e-4f51-b033-e547418c78b7","year":2023},"citing_paper":{"arxiv_id":"2508.13657","last_updated":"2026-04-08T13:11:36Z","snapshot_observed_at":"2026-08-02T19:16:01.732561Z","submitted_at":"2025-08-19T09:05:16Z","title":"In-Context Decision Making for Optimizing Complex AutoML Pipelines","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-18T22:37:35.864671Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2508.13657"},"observation_digest":"sha256:5638135604d267c7daa64f735365170d1634392f0056b6acd9a00aa1e70f4986","observation_id":"c5bec6d2-300a-41d6-baf1-471704fef336","resolution":{"observed_at":"2026-05-18T22:41:53.728672Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-05T18:59:38.298737Z","title":"Hollmann, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13662","last_updated":"2025-08-19T09:08:59Z","snapshot_observed_at":"2026-08-05T18:59:34.961499Z","submitted_at":"2025-08-19T09:08:59Z","title":"Fabrication of nano-diamonds with a single NV center: Towards matter-wave interferometry with massive objects","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T18:59:38.298737Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2508.13662"},"observation_digest":"sha256:cddc281da74ac8b61a1d46c2942e6b6b7d509ad3e81209e282b42bdef4d56e2d","observation_id":"1cd95322-5aa3-447a-9944-6de69c4c312b","resolution":{"observed_at":"2026-08-05T18:59:38.298737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":"2305.03403","doi":"10.48550/arxiv.2305.03403","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Context-aware automated feature engineering (caafe)","venue":"arXiv (Cornell University)","work_id":"fb5f1307-2f3e-4f51-b033-e547418c78b7","year":2023},"citing_paper":{"arxiv_id":"2509.21465","last_updated":"2026-05-15T11:35:18Z","snapshot_observed_at":"2026-07-06T22:30:46.129271Z","submitted_at":"2025-09-25T19:30:39Z","title":"Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-21T22:05:11.146329Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2509.21465"},"observation_digest":"sha256:8e4890e5b6339133bc33ca3c49b46cb3a9d41ec0c07f66b2bd15177204d4fffe","observation_id":"9fc1590b-98ac-4879-890a-00c36691a332","resolution":{"observed_at":"2026-05-21T22:05:41.546543Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":"2305.03403","doi":"10.48550/arxiv.2305.03403","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Context-aware automated feature engineering (caafe)","venue":"arXiv (Cornell University)","work_id":"fb5f1307-2f3e-4f51-b033-e547418c78b7","year":2023},"citing_paper":{"arxiv_id":"2604.14655","last_updated":"2026-05-11T06:17:04Z","snapshot_observed_at":"2026-08-03T18:25:43.429312Z","submitted_at":"2026-04-16T06:03:45Z","title":"AgentGA: Evolving Code Solutions in Agent-Seed Space","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-10T11:59:05.907000Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2604.14655"},"observation_digest":"sha256:7083afc7aae610c2adc7a78e063e448c39ab4173ee6255acac8ff2aaade3e64f","observation_id":"efe9d4e3-0373-42a3-a2bd-bfb099f780f8","resolution":{"observed_at":"2026-05-10T12:00:20.987367Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":"2305.03403","doi":"10.48550/arxiv.2305.03403","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Context-aware automated feature engineering (caafe)","venue":"arXiv (Cornell University)","work_id":"fb5f1307-2f3e-4f51-b033-e547418c78b7","year":2023},"citing_paper":{"arxiv_id":"2604.14655","last_updated":"2026-05-11T06:17:04Z","snapshot_observed_at":"2026-08-03T18:25:43.429312Z","submitted_at":"2026-04-16T06:03:45Z","title":"AgentGA: Evolving Code Solutions in Agent-Seed Space","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-12T04:13:02.212804Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2604.14655"},"observation_digest":"sha256:bcb0707813230e1f2c5ed65b877486c5b1f5cf692455d71864b97fe9c3dc2ec3","observation_id":"ccdf1653-4d9d-4038-bbdf-006cd751a3cc","resolution":{"observed_at":"2026-05-12T04:16:20.132714Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering","version":5},"cited_work":{"arxiv_id":"2305.03403","doi":"10.48550/arxiv.2305.03403","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.03403","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Context-aware automated feature engineering (caafe)","venue":"arXiv (Cornell University)","work_id":"fb5f1307-2f3e-4f51-b033-e547418c78b7","year":2023},"citing_paper":{"arxiv_id":"2606.17915","last_updated":"2026-06-16T13:34:27Z","snapshot_observed_at":"2026-07-06T23:53:25.839293Z","submitted_at":"2026-06-16T13:34:27Z","title":"Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T22:07:45.909694Z"},"links":{"cited_paper":"/paper/2305.03403","citing_paper":"/paper/2606.17915"},"observation_digest":"sha256:f9c3cfb08ab11e2af814961da8a02d1ec4bd17e8e204c4f8ed3d560fc90f5781","observation_id":"06fb30e8-5a6f-4287-9872-b6b1d1a4de1d","resolution":{"observed_at":"2026-07-03T23:29:02.918096Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.03403/citation-record","integrity":"/paper/2305.03403/integrity","json":"/paper/2305.03403/citation-record.json","paper":"/paper/2305.03403"},"outbound":[],"paper":{"arxiv_id":"2305.03403","last_updated":"2023-09-28T21:13:21Z","latest_version":5,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-05T14:20:30.734769Z","submitted_at":"2023-05-05T09:58:40Z","title":"Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2305.03403."}