{"as_of":"2026-08-14T12:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bb9c1526ae25e3d49300175c2f1c23cf5bbad8a77e85aeb7261fd65116bba606","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:43:23.942697Z","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":3,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.10478","last_updated":"2024-12-25T16:38:51Z","snapshot_observed_at":"2026-08-12T22:02:47.716058Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.10478","snapshot_observed_at":"2026-08-10T15:43:23.942697Z","title":"Large language models for constructing and optimizing machine learning workflows: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13743","last_updated":"2025-01-23T15:18:22Z","snapshot_observed_at":"2026-08-12T07:03:21.689221Z","submitted_at":"2025-01-23T15:18:22Z","title":"GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T15:43:23.942697Z"},"links":{"cited_paper":"/paper/2411.10478","citing_paper":"/paper/2501.13743"},"observation_digest":"sha256:61ecc913d8269b5ba74b7561abe7731ef69f8549d2661949b81b4af7fe923e30","observation_id":"26bff0c4-8624-4848-b7dc-7423fb4df9ae","resolution":{"observed_at":"2026-08-10T15:43:23.942697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.10478","last_updated":"2024-12-25T16:38:51Z","snapshot_observed_at":"2026-08-12T22:02:47.716058Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.10478","snapshot_observed_at":"2026-08-07T15:14:12.563231Z","title":"Large language models for construct- ing and optimizing machine learning workflows: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15741","last_updated":"2025-05-21T16:48:28Z","snapshot_observed_at":"2026-08-13T13:35:00.317948Z","submitted_at":"2025-05-21T16:48:28Z","title":"Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T15:14:12.563231Z"},"links":{"cited_paper":"/paper/2411.10478","citing_paper":"/paper/2505.15741"},"observation_digest":"sha256:3eaf74bcc90b584be031b5806a5921f3ef246160bae17070774fbde70785ee18","observation_id":"d08eeae4-c531-4a4a-b32b-5a387878bd6a","resolution":{"observed_at":"2026-08-07T15:14:12.563231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.10478","last_updated":"2024-12-25T16:38:51Z","snapshot_observed_at":"2026-08-12T22:02:47.716058Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.10478","snapshot_observed_at":"2026-08-05T12:24:02.299995Z","title":"Large language models for constructing and optimizing machine learning workflows: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.01684","last_updated":"2025-09-01T18:04:10Z","snapshot_observed_at":"2026-08-14T00:53:24.154749Z","submitted_at":"2025-09-01T18:04:10Z","title":"Reinforcement Learning for Machine Learning Engineering Agents","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T12:24:02.299995Z"},"links":{"cited_paper":"/paper/2411.10478","citing_paper":"/paper/2509.01684"},"observation_digest":"sha256:1317ee0d3bcda4d029a1cd333c51a0702e8fe5113add53161989492fc06a0ec1","observation_id":"33638af2-a3fa-4291-bb47-147f2d248376","resolution":{"observed_at":"2026-08-05T12:24:02.299995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.10478","last_updated":"2024-12-25T16:38:51Z","snapshot_observed_at":"2026-08-12T22:02:47.716058Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.10478","snapshot_observed_at":"2026-08-05T04:50:31.914745Z","title":"Large language models for constructing and optimizing machine learning workflows: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05946","last_updated":"2025-09-07T06:46:03Z","snapshot_observed_at":"2026-08-12T11:27:02.882063Z","submitted_at":"2025-09-07T06:46:03Z","title":"Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial","version":1},"reference_index":115,"source":"pdf_text","source_observed_at":"2026-08-05T04:50:31.914745Z"},"links":{"cited_paper":"/paper/2411.10478","citing_paper":"/paper/2509.05946"},"observation_digest":"sha256:b7171fbafbbb3d53aaba0ce766edf30043c9e0890f751f7364610f9dc7d43ab8","observation_id":"920c4d75-fe6f-488a-8116-9fcf2d7154a9","resolution":{"observed_at":"2026-08-05T04:50:31.914745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.10478","last_updated":"2024-12-25T16:38:51Z","snapshot_observed_at":"2026-08-12T22:02:47.716058Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","version":2},"cited_work":{"arxiv_id":"2411.10478","doi":"10.48550/arxiv.2411.10478","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.10478","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi:10.48550/arXiv.2411.10478","venue":"arXiv (Cornell University)","work_id":"3a39c6a6-0698-4d42-8b90-91af1267f675","year":2024},"citing_paper":{"arxiv_id":"2604.11945","last_updated":"2026-04-13T18:36:02Z","snapshot_observed_at":"2026-08-06T00:14:32.114662Z","submitted_at":"2026-04-13T18:36:02Z","title":"AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T16:41:29.548607Z"},"links":{"cited_paper":"/paper/2411.10478","citing_paper":"/paper/2604.11945"},"observation_digest":"sha256:2fc1ced93c8e5200ba82b402e29804851c8d666163ff4c12e38f5b3be0b73e4d","observation_id":"cd7a1d6c-64ed-44c2-82a7-57b7ccde90a0","resolution":{"observed_at":"2026-05-10T16:45:37.087772Z","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":"2411.10478","last_updated":"2024-12-25T16:38:51Z","snapshot_observed_at":"2026-08-12T22:02:47.716058Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","version":2},"cited_work":{"arxiv_id":"2411.10478","doi":"10.48550/arxiv.2411.10478","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.10478","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi:10.48550/arXiv.2411.10478","venue":"arXiv (Cornell University)","work_id":"3a39c6a6-0698-4d42-8b90-91af1267f675","year":2024},"citing_paper":{"arxiv_id":"2604.20261","last_updated":"2026-04-22T07:09:30Z","snapshot_observed_at":"2026-08-14T11:50:56.872435Z","submitted_at":"2026-04-22T07:09:30Z","title":"Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-10T00:39:05.990912Z"},"links":{"cited_paper":"/paper/2411.10478","citing_paper":"/paper/2604.20261"},"observation_digest":"sha256:d008244f95d81294f511a58d793d5ffeb92a4e6227874124ed8c5fdaf061f14c","observation_id":"9c26504a-4a45-4c6d-82fb-c9bd88a2c398","resolution":{"observed_at":"2026-05-10T00:39:48.428308Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2411.10478/citation-record","integrity":"/paper/2411.10478/integrity","json":"/paper/2411.10478/citation-record.json","paper":"/paper/2411.10478"},"outbound":[],"paper":{"arxiv_id":"2411.10478","last_updated":"2024-12-25T16:38:51Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T22:02:47.716058Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey"},"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 6 inbound Pith citation observations for arXiv:2411.10478."}