{"as_of":"2026-08-14T18:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:da482ea883e4456fcffb6a38924674cce7cbadaa4d2a49fa3a89534ceabaf11b","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:52:45.604805Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2508.20824/citation-record","integrity":"/paper/2508.20824/integrity","json":"/paper/2508.20824/citation-record.json","paper":"/paper/2508.20824"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.279174Z","title":"Journal of machine Learning research 3(Jan), 993–1022 (2003)","venue":null,"work_id":"f1bf46af-3590-4a87-b79b-673a4be5673b","year":2003},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.399214Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:416593206130f6625a82a2879bde47118ae070d4266398bd95e500c0e09df026","observation_id":"4dfbf1ad-a813-499e-b57e-1023252a916e","resolution":{"observed_at":"2026-08-05T14:52:46.284600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.403859Z","title":"UCI Machine Learning Repository (2010), DOI: https://doi.org/10.24432/C5H30K","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.403859Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:a8e753b087ba6ff5fc715e573c8f3ce863505d7898945b38e5ff26a51b84e104","observation_id":"8a6f2ac8-a4f3-4c58-8b02-378be4fdd78b","resolution":{"observed_at":"2026-08-05T14:52:45.403859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.265024Z","title":"In: 2019 IEEE International Conference on Data Mining (ICDM)","venue":null,"work_id":"9756abc9-ceae-47c1-9184-646b3b2eaa45","year":2019},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.408326Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:ee05d366b7cad4044e9cea1df1c607913768ecad94518644e271df0f9c81544e","observation_id":"84ae18f4-83d7-417f-b514-a54a1b4c5e0b","resolution":{"observed_at":"2026-08-05T14:52:46.269384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.251355Z","title":"ACM SIGKDD Explorations Newsletter22(2), 35–50 (2021)","venue":null,"work_id":"95d406ae-2c28-4cce-994f-22e525ab2407","year":2021},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.413451Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:4afe021a6ab20a237d2df3e6e33e7dd4c5f6157e2cdae5b3a86fec6c2cb1902f","observation_id":"3369a334-856f-4629-a4d7-3d1a7bd1abd9","resolution":{"observed_at":"2026-08-05T14:52:46.255677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.417451Z","title":"UCI Machine Learning Repository (2009), DOI: https://doi.org/10.24432/C56S3T","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.417451Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:678334fab353741940a9b6d4cda59bef36309df1abf092caab8e7e9e5eff0e23","observation_id":"3ef4b686-4412-4232-8d69-c62a10074bd3","resolution":{"observed_at":"2026-08-05T14:52:45.417451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24432/c5gc82","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"UCI Machine Learning Reposi- tory (2009), DOI: https://doi.org/10.24432/C5GC82","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"98fca11f-0671-4583-859a-37c8a0bd428d","year":2009},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.421858Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:cce3114c349a4f1a2c4da4da0f65d7284d34ed069a15b24a583a82c8527555c5","observation_id":"41a64a54-0d1f-41c7-81d3-df71ade5ca8c","resolution":{"observed_at":"2026-08-05T14:52:45.691982Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.237084Z","title":"Infor- mation Sciences 189, 176–190 (2012)","venue":null,"work_id":"3c3dabe6-f76c-4e68-8a9b-ab022a2dd8fb","year":2012},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.426677Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:67050f18458537131492b6f917a8223d652147147d0bd63cc5350661d538679c","observation_id":"827046d3-b757-46ca-803d-4668020c4d50","resolution":{"observed_at":"2026-08-05T14:52:46.241648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.223079Z","title":"The Journal of Machine Learning Research20(1), 1997–2017 (2019)","venue":null,"work_id":"aa23eef9-8787-4edb-ae79-8184b1e98023","year":1997},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.430891Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:3e4c86c898099331b8bcf4b9eb00b94b594ce808b3de39716f12970f2940ea47","observation_id":"cf49f14e-e718-41b2-b9e1-7daaf45392c7","resolution":{"observed_at":"2026-08-05T14:52:46.227597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.208523Z","title":"The Journal of Supercomputing 80(18), 26394–26434 (2024)","venue":null,"work_id":"6a841a5f-b8f1-459e-bcf8-e343dbd51096","year":2024},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.434908Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:d911f3ebee0e05fbe2ac87bee86e9815c17f5a4e8e6b2aab2aea2318bd412b22","observation_id":"93e466b2-aace-400c-923c-ae7388e4f720","resolution":{"observed_at":"2026-08-05T14:52:46.213170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.193699Z","title":"Knowledge- Based Systems 212, 106622 (2021)","venue":null,"work_id":"adaf8a79-221d-4358-881b-7512032bbb97","year":2021},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.439173Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:d111c2da9f791b704431184c254217dfed4bbc2dec66e431dbd748069d5d4a06","observation_id":"0eee8665-9570-4729-b9e9-96fb6039b8b3","resolution":{"observed_at":"2026-08-05T14:52:46.198402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1901.07329","last_updated":"2020-02-26T14:21:28Z","snapshot_observed_at":"2026-08-14T17:27:40.464754Z","submitted_at":"2019-01-22T14:33:23Z","title":"The autofeat Python Library for Automated Feature Engineering and Selection","version":4},"cited_work":{"arxiv_id":"1901.07329","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.07329","snapshot_observed_at":"2026-08-05T14:52:45.771191Z","title":"The autofeat Python Library for Automated Feature Engineering and Selection","venue":"cs.LG","work_id":"9d71bf32-bb9b-4121-95cf-402f558bd2f8","year":2019},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.443643Z"},"links":{"cited_paper":"/paper/1901.07329","citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:abc255f6326717ed381efef78ecae88b03f7665f6e76d030f7263cefc16b0217","observation_id":"8dd2525b-3fdf-4889-8c4f-d3b9450d2ded","resolution":{"observed_at":"2026-08-05T14:52:45.775569Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.177994Z","title":"In: Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I","venue":null,"work_id":"f5c1d640-3e83-4dc2-8992-9565617bc9a1","year":2019},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.448531Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:a9bf6c231276b14da068cf6241b4712f5a2ce2c64d8c8c74736145714d7894f0","observation_id":"8f74bcec-d0fd-4892-bae2-92753efc057a","resolution":{"observed_at":"2026-08-05T14:52:46.183400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.163680Z","title":null,"venue":null,"work_id":"e56fbba3-8ebe-438c-b50f-0b8fff32b871","year":2022},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.452766Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:f04d6da9c2d73233d249b98314eb9541e21f2f7a570df9e1fffb3b4e0d242235","observation_id":"b5d5752f-ca1d-4b0b-b4b8-2fac2598e666","resolution":{"observed_at":"2026-08-05T14:52:46.168094Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.148625Z","title":null,"venue":null,"work_id":"667131f9-fac6-4a9f-9414-27fedc0b14f1","year":2024},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.456867Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:5379ce556e62aef3372818ffe61c1bb70e071f8e6a5f81b200cad77c915511c4","observation_id":"05506e13-403d-47a7-b9b8-6df5d13f7e32","resolution":{"observed_at":"2026-08-05T14:52:46.153198Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.461102Z","title":"UCI Machine Learning Repository (1989), DOI: https://doi.org/10.24432/C52P4X","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.461102Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:90afef9a796049da9879fa196342edac2e5025c9695ae30e5f151bcc9b3317c5","observation_id":"d539004e-ece0-40dc-91cb-e28651f4655d","resolution":{"observed_at":"2026-08-05T14:52:45.461102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.134396Z","title":"Jour- nal of Statistics Education4 (1996), http://www.amstat.org/publications/jse/ v4n1/datasets.johnson.html, bodyfat data retrieved from the American Statis- tical Association","venue":null,"work_id":"7dc31a39-ff74-471f-a04d-1b2f6db42424","year":1996},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.465453Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:f1fba59c9410185104920dd506a68263cf5ea4ba2de007d84016b27917cfe4a0","observation_id":"8ac7e656-2e33-427c-ad73-18144c966e99","resolution":{"observed_at":"2026-08-05T14:52:46.139086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.121093Z","title":null,"venue":null,"work_id":"67ab9de8-ad58-4a17-9cd5-13ff2f0dfa8c","year":null},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.469357Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:906a9ec59b3d3731adc5032b3ca8bcf2173e461c5c365b2a6976c0963d2e0870","observation_id":"1c7a1862-e1ec-4c59-ba88-9163d67793d1","resolution":{"observed_at":"2026-08-05T14:52:46.125353Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.108642Z","title":"In: 2015 IEEE international conference on data science and advanced analytics (DSAA)","venue":null,"work_id":"1276140f-5498-49f4-9fc3-52a64008994a","year":2015},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.473894Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:ee239518cc030a0b1f170a8d25efdcd84fb57c90b1f803a6337ef80b0645e24e","observation_id":"37524117-0c8a-4503-ba17-8723beb6bb2f","resolution":{"observed_at":"2026-08-05T14:52:46.112724Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.095718Z","title":"ACM Computing Surveys (CSUR) 54(8), 1–36 (2021)","venue":null,"work_id":"9c28127d-1d07-4c7e-9530-ef93a4b68265","year":2021},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.477737Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:61d48baf97e1bb5fecd34fd7bca8e07b7b20675c19599dbda709f611b8e65d6b","observation_id":"0e78114b-365c-46e1-a6dd-6c5d5eb13663","resolution":{"observed_at":"2026-08-05T14:52:46.099860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.082323Z","title":"In: 2016 IEEE 16th International Conference on Data Mining (ICDM)","venue":null,"work_id":"197ac616-d8e3-49bf-8df7-b6a2b46e9bef","year":2016},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.481859Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:09da7fee6e7023547fec7f9f1644e943f4d31103b3922e990de4a3b35fa22216","observation_id":"50a856e4-3535-4cf1-a5f5-0ad1ff917e29","resolution":{"observed_at":"2026-08-05T14:52:46.086875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.068062Z","title":null,"venue":null,"work_id":"f7358dba-7376-4a04-8924-12a731660027","year":2024},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.485641Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:71596f057b0c73be64ae420426e9a0874a7493d0c0685ffa9325fa30ee625b97","observation_id":"be9a7957-4aa5-487d-9740-6c3e6726bde2","resolution":{"observed_at":"2026-08-05T14:52:46.072363Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.054581Z","title":"Transformation10(10), 10 (2016)","venue":null,"work_id":"31c732d5-773d-4a30-b55e-915571347e1f","year":2016},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.489729Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:0c18d3eaaaa9c9aa1c0da587ad3a9915b4aea5bbc04be19137bddc51821f7f0a","observation_id":"a0cf83e3-2d2b-493e-9003-1896d8e5d2e7","resolution":{"observed_at":"2026-08-05T14:52:46.058972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.040622Z","title":"In: Proceedings of the AAAI Conference on Artificial Intelligence","venue":null,"work_id":"552cb9ef-c9ba-47b7-a3cd-711c1c10031d","year":2018},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.494626Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:e67f07e6cec8e163ac5f43850c4f6b5e7267f5664c17882fc5c3a35258978a33","observation_id":"94e24b2d-d055-4e3f-b84e-1e75fe37afdd","resolution":{"observed_at":"2026-08-05T14:52:46.044986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.027175Z","title":"In: 2016 IEEE 16th international con- ference on data mining workshops (ICDMW)","venue":null,"work_id":"11c7c5d0-bf7a-4192-a5ed-804fd2422c28","year":2016},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.498568Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:8f38b7948b128d636d9da421629d79f67e9ed10591cb6a9fc74bd7b3d330e5e3","observation_id":"f0f3030f-2ee6-470a-809b-91be13e59158","resolution":{"observed_at":"2026-08-05T14:52:46.031525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.014212Z","title":"IEEE Transactions on Electronics Packaging Manufacturing24(3), 214–221 (2001)","venue":null,"work_id":"afcd24ec-da11-47b4-ac1c-3343ae7e9263","year":2001},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.502734Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:c149febe4b698d99cb0fcba7d6b30117aaadeae549c118ab9907ab8630033caa","observation_id":"c3f54baa-9309-4822-a3de-9aa69bf5098e","resolution":{"observed_at":"2026-08-05T14:52:46.018428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1706.00327","last_updated":"2017-06-01T14:44:34Z","snapshot_observed_at":"2026-07-06T05:45:11.551067Z","submitted_at":"2017-06-01T14:44:34Z","title":"One button machine for automating feature engineering in relational databases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.00327","snapshot_observed_at":"2026-08-05T14:52:45.506689Z","title":"arXiv preprint arXiv:1706.00327 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.506689Z"},"links":{"cited_paper":"/paper/1706.00327","citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:a759e253005bc07a0bde3071ab97b545bf949993ba6ae5ff4e4633298296c791","observation_id":"ae4115b2-d50f-45ea-a5db-fcabc895a81b","resolution":{"observed_at":"2026-08-05T14:52:45.506689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:46.000869Z","title":"In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management","venue":null,"work_id":"5b1c0f15-ff03-45d8-99ec-b70fb1b056fa","year":2021},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.511163Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:8727f4a97b08f630d423b2d92823be27f93960b38bc01b786a1b627fdaa402f2","observation_id":"e456b52f-682c-4694-97e8-0fb4d43802c3","resolution":{"observed_at":"2026-08-05T14:52:46.005123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.515293Z","title":"UCI Machine Learning Repository (1999), DOI: https://doi.org/10.24432/C59W2D","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.515293Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:aba96826ab35029e662332b332805965d67f3e007c9b3b067cfcd25b085e6b40","observation_id":"0c05a138-b51f-4eda-b97a-4111a3ffb22c","resolution":{"observed_at":"2026-08-05T14:52:45.515293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.987452Z","title":"In: Pro- ceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V","venue":null,"work_id":"c2c04172-1b85-4079-ab07-8b96b8e2f376","year":2025},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.519349Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:77ac63185c3e96c7a133ebb4d992f2b25dd3c430653e24deb2728ce78f00463b","observation_id":"5a25f437-8072-4f06-bd1d-77ff5d9b4a3f","resolution":{"observed_at":"2026-08-05T14:52:45.991745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.974243Z","title":"In: 2024 IEEE International Conference on Big Data (BigData)","venue":null,"work_id":"0a170f9b-a6b5-4089-bd94-a1cfe69befb7","year":2024},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.523662Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:d7bdce9c14d5c1f27f7e57ada53862b5405bf27aff75c4678bdc36267545a87f","observation_id":"e5b889ea-10b9-4a74-a447-868e3003a65b","resolution":{"observed_at":"2026-08-05T14:52:45.978608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.960971Z","title":null,"venue":null,"work_id":"64fba461-d91c-4130-a124-3f62a3ebd84c","year":2024},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.527873Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:712141d87d8def23f5566ac96930a8de99058bb0279d4313313a7e8c0a750822","observation_id":"e2063b15-51fd-489a-a2a0-b5d685201b8d","resolution":{"observed_at":"2026-08-05T14:52:45.965316Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.947719Z","title":null,"venue":null,"work_id":"f3f722c3-0862-4026-a13f-e71a496750d2","year":null},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.531874Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:2f31bc65edd480e33273d834c6004232f81c50e893db971737ad676f5d69bda7","observation_id":"b78fe2f2-89d5-4b91-be2f-50f2f9548aa4","resolution":{"observed_at":"2026-08-05T14:52:45.951952Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.933738Z","title":"In: Proceedings of the genetic and evolutionary computation conference 2016","venue":null,"work_id":"3c02a22a-975c-4069-a42d-1dd10f8d8d94","year":2016},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.536265Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:86867f175df2e0e7e5a3ee8cb5907093a2e3c38d6ebc127ac5eee336a11194b3","observation_id":"1a017169-70b9-46bb-95d6-0d148ca77eaf","resolution":{"observed_at":"2026-08-05T14:52:45.938241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.919542Z","title":null,"venue":null,"work_id":"f0647a35-b749-485d-a292-0deb0509da2f","year":2022},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.540991Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:f28f109a1ef69d44f3f6af7dee500355d491c1f46ea652ea2f83aed9ec7f83ce","observation_id":"ebbe30f9-469d-423c-9dfe-0d657f1d843b","resolution":{"observed_at":"2026-08-05T14:52:45.923636Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.906621Z","title":null,"venue":null,"work_id":"ba686107-692c-4eb7-bdab-0566d5bc5263","year":2018},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.545537Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:0ef491a70044b47688f72b69b445039c319acf3361f1966c09c4a34ce90d8965","observation_id":"8958c7fb-f578-4723-af15-a4a3c0845fef","resolution":{"observed_at":"2026-08-05T14:52:45.910771Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.893541Z","title":null,"venue":null,"work_id":"d23191f0-34b1-409e-bffc-343dc14b701d","year":2024},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.549962Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:a6be81bb704b372b6d50d08819a08605a4fb7c0ba83f6b214fa098df9dbb061a","observation_id":"a0eb3e74-79d2-492d-b113-b80abe47f0bb","resolution":{"observed_at":"2026-08-05T14:52:45.897692Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.880245Z","title":null,"venue":null,"work_id":"460bb3a7-77a5-41fe-9e66-357d3ad65496","year":null},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.554151Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:78f27d3d54387ad4ac05d9f8aa52a565e12bc0704cb93344128ea415f04d5403","observation_id":"41deb697-0d54-495f-b0e2-7d0b2f2a2b60","resolution":{"observed_at":"2026-08-05T14:52:45.884409Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.866606Z","title":"Memetic Computing8(1), 3–15 (2016)","venue":null,"work_id":"9182e5d2-acd6-450f-9c6a-5dae42fbc414","year":2016},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.558643Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:858b18eab934507abe6dc4b9d161e2294157b8239afd64873d9e2415feb24bc9","observation_id":"fd5d68b0-9baf-42b9-8937-2367e5fdda6a","resolution":{"observed_at":"2026-08-05T14:52:45.870797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.852997Z","title":"Pattern Recognition93, 404–417 (2019)","venue":null,"work_id":"a1d3b990-1b13-4bab-bd69-0b29d8a19ea1","year":2019},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.562952Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:5a37892296d5c56309425a9bad2f8c676c11a0f33de7f5aab203b39479b81e94","observation_id":"775b81ad-e248-45cc-9245-9e45540b2fa6","resolution":{"observed_at":"2026-08-05T14:52:45.857360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.838442Z","title":"In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","venue":null,"work_id":"4178e297-f482-4ebe-9891-d1113a529e25","year":2022},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.566976Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:cac7d8d20636204aa6ed8f364165a3488e2e5b7599cbff04974ff2ed350797b9","observation_id":"af95bbc2-fb3f-4fae-849d-14a98aade557","resolution":{"observed_at":"2026-08-05T14:52:45.842960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.824793Z","title":"Advances in Neural Information Processing Systems36, 43563–43578 (2023)","venue":null,"work_id":"c6f70505-341b-4bdf-8514-c4349728bb90","year":2023},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.571104Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:a12b6128df9d798ec9bca61eaf334a90e70afcf6d1a6f0de5062b225d7525bd5","observation_id":"345f2166-bc50-4df0-94b1-65eb5aba23e5","resolution":{"observed_at":"2026-08-05T14:52:45.829067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2501.14889","last_updated":"2026-05-25T10:35:54Z","snapshot_observed_at":"2026-08-13T14:01:47.804741Z","submitted_at":"2025-01-24T19:24:20Z","title":"Iterative Feature Space Optimization through Incremental Adaptive Evaluation","version":2},"cited_work":{"arxiv_id":"2501.14889","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.14889","snapshot_observed_at":"2026-08-05T14:52:45.736206Z","title":"Iterative Feature Space Optimization through Incremental Adaptive Evaluation","venue":"cs.LG","work_id":"232a3aaa-8fca-4d1d-83b7-3cb1b45022f6","year":2025},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.575985Z"},"links":{"cited_paper":"/paper/2501.14889","citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:5fef152d0dfc5ec9222c75dfa569e4a769f6c7a7c24aa2cfaaadb58c1fb49d1f","observation_id":"059b9ce7-a4ed-47f1-8815-fa4530d3270e","resolution":{"observed_at":"2026-08-05T14:52:45.741316Z","resolver_source":"local_arxiv","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":"2306.16893","last_updated":"2023-06-29T12:29:21Z","snapshot_observed_at":"2026-08-14T16:00:03.986383Z","submitted_at":"2023-06-29T12:29:21Z","title":"Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.16893","snapshot_observed_at":"2026-08-05T14:52:45.580647Z","title":"arXiv preprint arXiv:2306.16893 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.580647Z"},"links":{"cited_paper":"/paper/2306.16893","citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:607187effe24f7d965b842c4c8df5052a7f190bb3f1fd21d70bccb4d26d4d6c5","observation_id":"219e9771-a262-4c4a-bce1-c9e0509c093f","resolution":{"observed_at":"2026-08-05T14:52:45.580647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.811957Z","title":"In: Pro- ceedings of the 2023 SIAM International Conference on Data Mining (SDM)","venue":null,"work_id":"5e183f17-9044-4c8d-8650-b83452f3cfaa","year":2023},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.585125Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:06a1742029eb1699a95c4d9194b93e8edc01bf128d191b4a38f2b95272e80380","observation_id":"92934930-e075-42c4-afcb-1844b837d2b4","resolution":{"observed_at":"2026-08-05T14:52:45.815881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.588705Z","title":"UCI Machine Learning Repository (2008), DOI: https://doi.org/10.24432/C5NG6W","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.588705Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:ccb9ff6d718922e9ea49df6b765efa7099ecc94ac2876fb47d13165c57aae566","observation_id":"696ddf95-8fc2-4a72-b5ea-baf8c90ea411","resolution":{"observed_at":"2026-08-05T14:52:45.588705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24432/c5vk5d","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"UCI Machine Learning Repository (2014), DOI: https://doi.org/10.24432/C5VK5D","venue":"UC Irvine","work_id":"e4d1cb43-7d29-4365-be12-3c9750806fa7","year":2014},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.593123Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:3935896b1416e2a5b53d91820d10e2b6fb115a64ebfb2717e4ecf0c4795a9600","observation_id":"d4f970fe-c008-46c6-9a2b-99f762203bb7","resolution":{"observed_at":"2026-08-05T14:52:45.653182Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.798891Z","title":null,"venue":null,"work_id":"c7629194-d775-4043-a5b2-fceff04489ff","year":2022},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.597346Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:f1fa03eaff2b8d715f2d20fbbea5e2e1ed446807db2ed7b289b032fdc96ca346","observation_id":"f5d56de4-8637-4249-b9f4-7a7c087034af","resolution":{"observed_at":"2026-08-05T14:52:45.802621Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:52:45.784991Z","title":"In: International Conference on Automated Machine Learning","venue":null,"work_id":"32c497d1-5e87-4baf-8617-316a217bfca6","year":2022},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.601173Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:5d4a0730b0a401f634fed1cb0d14d09ed5836f184a411566cb743567331393b1","observation_id":"1da893b6-11a9-4273-a60a-19db98affb8e","resolution":{"observed_at":"2026-08-05T14:52:45.789987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":"10.24432/c54598","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"UC Irvine","work_id":"b4025abc-3d00-47c3-bca3-7473da4e230f","year":1988},"citing_paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T14:52:45.604805Z"},"links":{"citing_paper":"/paper/2508.20824"},"observation_digest":"sha256:42d8554d08f8ec8e90c9e78131c0ffccc411c98b366bc4c5d9066dfe7cc64f2d","observation_id":"98998c22-feee-499c-b3d2-7dc74658efe5","resolution":{"observed_at":"2026-08-05T14:52:45.640016Z","resolver_source":"doi","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"}}],"paper":{"arxiv_id":"2508.20824","last_updated":"2025-08-28T14:21:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T03:00:15.712692Z","submitted_at":"2025-08-28T14:21:08Z","title":"GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":18,"verified_exact":4,"verified_fuzzy":26},"total_outbound_references":49},"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 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2508.20824."}