{"as_of":"2026-08-22T15:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:568fac65009ec9c5bf1b57136295c39e80eccfbad37e2a7a18a83520eb9ece59","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:24:03.050815Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:33:13.351087Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-09T09:06:06.142107Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":"2412.06724","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-07-09T09:06:06.142107Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","venue":"cs.DB","work_id":"069a1f14-c6e1-440f-93a5-46ea34c2897b","year":2024},"citing_paper":{"arxiv_id":"2503.04338","last_updated":"2026-04-27T08:04:44Z","snapshot_observed_at":"2026-08-18T18:27:34.060489Z","submitted_at":"2025-03-06T11:34:49Z","title":"In-depth Analysis of Graph-based RAG in a Unified Framework","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-23T01:36:25.057478Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2503.04338"},"observation_digest":"sha256:5567876f79637523aa77c029642c7f222d16b4ee711055993f3d1a19847a4d89","observation_id":"03bd48d0-9c1c-4d18-8f53-c556c39f519b","resolution":{"observed_at":"2026-05-23T01:37:22.065634Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-08-07T14:33:13.351087Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18458","last_updated":"2025-06-01T16:00:34Z","snapshot_observed_at":"2026-08-16T16:04:23.826018Z","submitted_at":"2025-05-24T01:57:12Z","title":"A Survey of LLM $\\times$ DATA","version":3},"reference_index":237,"source":"pdf_text","source_observed_at":"2026-08-07T14:33:13.351087Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2505.18458"},"observation_digest":"sha256:cf66e5559a20ba17e0e29f809ca5320b7e1ac27266ff6cd469f6aefd4efc6473","observation_id":"9dc64887-7725-4472-942a-8b8b5d439184","resolution":{"observed_at":"2026-08-07T14:33:13.351087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":"2412.06724","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-07-09T09:06:06.142107Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","venue":"cs.DB","work_id":"069a1f14-c6e1-440f-93a5-46ea34c2897b","year":2024},"citing_paper":{"arxiv_id":"2509.21465","last_updated":"2026-05-15T11:35:18Z","snapshot_observed_at":"2026-08-16T23:38:46.600327Z","submitted_at":"2025-09-25T19:30:39Z","title":"Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-21T22:05:11.146329Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2509.21465"},"observation_digest":"sha256:d23489197955b6f57741fa8593638d7caff401496fdd865de079be9e4ab5a700","observation_id":"318b8211-2ecb-463f-b9bf-8f1729860a59","resolution":{"observed_at":"2026-05-21T22:05:41.501087Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":"2412.06724","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-07-09T09:06:06.142107Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","venue":"cs.DB","work_id":"069a1f14-c6e1-440f-93a5-46ea34c2897b","year":2024},"citing_paper":{"arxiv_id":"2605.12376","last_updated":"2026-06-04T17:58:18Z","snapshot_observed_at":"2026-08-15T10:05:43.294700Z","submitted_at":"2026-05-12T16:42:38Z","title":"ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-13T05:56:36.312877Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2605.12376"},"observation_digest":"sha256:e1217229374291c6782e3a42dffba155ebd0740b532183f85c4dc425aed0e6c2","observation_id":"9d9529d6-b78a-4fa8-8512-b3d1ec81c0b8","resolution":{"observed_at":"2026-05-13T05:57:22.467954Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":"2412.06724","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-07-09T09:06:06.142107Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","venue":"cs.DB","work_id":"069a1f14-c6e1-440f-93a5-46ea34c2897b","year":2024},"citing_paper":{"arxiv_id":"2605.12376","last_updated":"2026-06-04T17:58:18Z","snapshot_observed_at":"2026-08-15T10:05:43.294700Z","submitted_at":"2026-05-12T16:42:38Z","title":"ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T22:18:45.189576Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2605.12376"},"observation_digest":"sha256:4c6dffebfae5fac54968a4a580048d21622fbda9c2255063eff0ee345b2e0986","observation_id":"2d3e578e-9c67-4409-9df5-ed58c4b9f938","resolution":{"observed_at":"2026-07-01T14:05:46.756866Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":"2412.06724","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-07-09T09:06:06.142107Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","venue":"cs.DB","work_id":"069a1f14-c6e1-440f-93a5-46ea34c2897b","year":2024},"citing_paper":{"arxiv_id":"2606.25388","last_updated":"2026-06-24T04:35:35Z","snapshot_observed_at":"2026-08-17T16:50:51.097951Z","submitted_at":"2026-06-24T04:35:35Z","title":"TabClean: Reusable LLM-Synthesized Programs for Tabular Data Cleaning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-25T19:35:06.090910Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2606.25388"},"observation_digest":"sha256:181ff6f705bfc15e4c66e4517dfbb684034da7d6d4a5fa19dcc8d6888de2ed26","observation_id":"aa096206-f693-4130-870e-33e259e7da8f","resolution":{"observed_at":"2026-07-04T20:50:11.319616Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-07-11T15:02:34.391204Z","title":"arXiv preprint arXiv:2412.06724 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04694","last_updated":"2026-07-06T05:52:33Z","snapshot_observed_at":"2026-08-19T17:42:34.838184Z","submitted_at":"2026-07-06T05:52:33Z","title":"Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-07-11T15:02:34.391204Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2607.04694"},"observation_digest":"sha256:cc7a5f4970c9ed8558e0e23beeeecae31c0298f749cc7a235f17fded176a343b","observation_id":"c6d741e6-7b0c-4bf9-bc5c-c4a38327bef9","resolution":{"observed_at":"2026-07-11T15:02:34.391204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":"2412.06724","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-07-09T09:06:06.142107Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","venue":"cs.DB","work_id":"069a1f14-c6e1-440f-93a5-46ea34c2897b","year":2024},"citing_paper":{"arxiv_id":"2607.07504","last_updated":"2026-07-08T15:00:16Z","snapshot_observed_at":"2026-08-19T18:41:38.781319Z","submitted_at":"2026-07-08T15:00:16Z","title":"Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-09T09:01:10.366890Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2607.07504"},"observation_digest":"sha256:874ded5fe526bda5843526eef123f8e61aaeffc3677b3fe50a64c86163346575","observation_id":"ee6846d7-14ac-4868-99d4-8a12d99aa43f","resolution":{"observed_at":"2026-07-09T09:06:06.143880Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-08-01T22:23:21.849092Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15781","last_updated":"2026-07-24T07:58:32Z","snapshot_observed_at":"2026-08-21T19:20:58.556453Z","submitted_at":"2026-07-17T09:32:54Z","title":"AgentFAIR: A Multi-Agent Collaborative Framework for FAIRness Evaluation of Geospatial Datasets","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T22:23:21.849092Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2607.15781"},"observation_digest":"sha256:1e5b2abe3b6ef6e25686df7ec252e93c310798bd8c815de0a79a1884089b2c65","observation_id":"90c4f7d8-9658-4e50-b463-2921520f1ada","resolution":{"observed_at":"2026-08-01T22:23:21.849092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-08-01T12:46:28.186889Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.26593","last_updated":"2026-07-29T08:14:00Z","snapshot_observed_at":"2026-08-17T09:20:58.762544Z","submitted_at":"2026-07-29T08:14:00Z","title":"ASARL: Autonomous Social-Aware Relevance Learning for QQ Search","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T12:46:28.186889Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2607.26593"},"observation_digest":"sha256:c7509230dd60b23e5a9f4af4eb04183fe0faf27ff5645590e0be93bb302446ba","observation_id":"728f5adc-6310-48d5-91a2-922471942bed","resolution":{"observed_at":"2026-08-01T12:46:28.186889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06724","snapshot_observed_at":"2026-08-04T02:02:24.664176Z","title":"Autodcworkflow: Llm-based data cleaning workflow auto-generation and benchmark,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00018","last_updated":"2026-06-29T14:15:46Z","snapshot_observed_at":"2026-08-16T08:17:24.357286Z","submitted_at":"2026-06-29T14:15:46Z","title":"CITBench: A Comprehensive Benchmark for Interactive Tabular Data Processing with LLMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T02:02:24.664176Z"},"links":{"cited_paper":"/paper/2412.06724","citing_paper":"/paper/2608.00018"},"observation_digest":"sha256:eab8afc9f4a32ebbe86fe19e751ad16644ce863ac60d5f02ffb581569ad67d6e","observation_id":"23d57a55-fd37-44de-b31b-d1c211e8e885","resolution":{"observed_at":"2026-08-04T02:02:24.664176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.06724/citation-record","integrity":"/paper/2412.06724/integrity","json":"/paper/2412.06724/citation-record.json","paper":"/paper/2412.06724"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:02.899708Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.899708Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:5a83278c54d087a643f24eef3357ed74e91f87d8f24bfd3b62dfcf1484dac739","observation_id":"6d3e9b29-45b8-4731-95ff-1db9d319642c","resolution":{"observed_at":"2026-08-11T19:24:02.899708Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:02.904219Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.904219Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:d9ab103392073fae3df671571e364d12daeb8f76e48d764fa08abcd8793e3435","observation_id":"9f2fae1c-74ae-40a4-bdc4-fa9721694ef2","resolution":{"observed_at":"2026-08-11T19:24:02.904219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18681","last_updated":"2024-04-29T13:24:23Z","snapshot_observed_at":"2026-08-21T16:38:46.338178Z","submitted_at":"2024-04-29T13:24:23Z","title":"LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18681","snapshot_observed_at":"2026-08-11T19:24:02.908071Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.908071Z"},"links":{"cited_paper":"/paper/2404.18681","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:59b60affea3cccc09507c262b58d7ff81e045123ed2fa2bded766ba7c4c4e403","observation_id":"ac05d618-3570-42cd-a494-313e85680d9a","resolution":{"observed_at":"2026-08-11T19:24:02.908071Z","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":"2026-08-11T19:24:04.065324Z","title":null,"venue":null,"work_id":"e8b61153-5e25-462f-b548-b52b7c8541da","year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.912727Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:f4e44e099c81542f865f9af69480faa56295a7b01cba0599f1cb0fd17bcafed7","observation_id":"ba9f0200-ac75-4586-915f-16daf2a01840","resolution":{"observed_at":"2026-08-11T19:24:04.069824Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.02164","last_updated":"2020-06-14T19:14:22Z","snapshot_observed_at":"2026-08-14T04:55:53.397142Z","submitted_at":"2019-09-05T00:25:17Z","title":"TabFact: A Large-scale Dataset for Table-based Fact Verification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.02164","snapshot_observed_at":"2026-08-11T19:24:02.916279Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.916279Z"},"links":{"cited_paper":"/paper/1909.02164","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:7959719b3129a2a85af768b90f40e43fa8c194a70b97e74166b67132912534ab","observation_id":"86a3b2cd-235c-42d3-8116-a412a7f7cc9a","resolution":{"observed_at":"2026-08-11T19:24:02.916279Z","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":"2026-08-11T19:24:04.053676Z","title":null,"venue":null,"work_id":"e4f57935-9751-4702-80c1-2d2102738b03","year":2023},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.920300Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:12b688daacccb46b4dd890caa0dc0f698f36a093b1614091a936b9b7616714ec","observation_id":"2e424141-e3db-449c-b598-f3c81ab0dad0","resolution":{"observed_at":"2026-08-11T19:24:04.057475Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02875","last_updated":"2023-03-01T03:21:40Z","snapshot_observed_at":"2026-08-16T16:26:26.509923Z","submitted_at":"2022-10-06T12:55:17Z","title":"Binding Language Models in Symbolic Languages","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02875","snapshot_observed_at":"2026-08-11T19:24:02.924422Z","title":"Smith, and Tao Yu","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.924422Z"},"links":{"cited_paper":"/paper/2210.02875","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:21d1bd22feb176520b392a65dc43bcb7e54f509c62e29c8b4d1befce6a454f5f","observation_id":"de555c54-28f0-4a11-be10-dc5c8cac9837","resolution":{"observed_at":"2026-08-11T19:24:02.924422Z","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":"2026-08-11T19:24:04.041331Z","title":null,"venue":null,"work_id":"c50920fb-9ed6-4fc6-b22f-9e045431594d","year":2013},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.928398Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:9fac053e0c408bb84ae813011e7ef27d183615b28efcba21b2d76dfe339c51f0","observation_id":"4094ac3f-ccb5-4e89-80bf-13d9a1fadc36","resolution":{"observed_at":"2026-08-11T19:24:04.045056Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:04.030562Z","title":null,"venue":null,"work_id":"a43ca21d-64e8-4836-9760-63db03d8bf2b","year":2013},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.932117Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:806873e5dccb3bedef16e37089af736b5d1ed4fe27966f3ce164b19a95ba4a13","observation_id":"f68cd9f9-9518-4cb6-be41-c326c63a0662","resolution":{"observed_at":"2026-08-11T19:24:04.034292Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T19:24:02.935492Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.935492Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:e1b90d5a8622963a95e42b6ffd6592ce22e19d3d1d71545a8260a52335a3cf1c","observation_id":"3d8165e7-ed1e-47a3-95f9-6c81ee0b84ee","resolution":{"observed_at":"2026-08-11T19:24:02.935492Z","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":"2026-08-11T19:24:04.019913Z","title":"Eltabakh, Zan Ahmad Naeem, Mohammad Shahmeer Ahmad, Mourad Ouzzani, and Nan Tang","venue":null,"work_id":"466dedd5-a294-4216-816a-15e72ecc0305","year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.939453Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:5ea8daf5b8185543c3a655100ab37a0bcb58583782d75786aac963fbfca8cb0c","observation_id":"8f076514-9b13-4e8f-9375-fdedd06106d4","resolution":{"observed_at":"2026-08-11T19:24:04.023875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04770","last_updated":"2023-01-12T00:15:40Z","snapshot_observed_at":"2026-08-22T14:21:16.924863Z","submitted_at":"2023-01-12T00:15:40Z","title":"KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution","version":1},"cited_work":{"arxiv_id":"2301.04770","doi":"10.48550/arxiv.2301.04770","metadata_source":"pith","pith_arxiv_id":"2301.04770","snapshot_observed_at":"2026-08-12T00:16:22.474752Z","title":"KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution","venue":"cs.CL","work_id":"ae63364b-0747-4f9a-93ca-896d2506e0b2","year":2023},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.942906Z"},"links":{"cited_paper":"/paper/2301.04770","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:f869f32aff62714ca2cc79cd47cf87cfd9b9a3a9f9e72fa7f2bb88c38725fda5","observation_id":"85808ad4-a686-4cca-9abe-191f35145f5a","resolution":{"observed_at":"2026-08-11T19:24:03.139384Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14361","last_updated":"2024-04-26T19:02:23Z","snapshot_observed_at":"2026-08-18T11:17:15.212859Z","submitted_at":"2024-04-22T17:15:32Z","title":"Better Synthetic Data by Retrieving and Transforming Existing Datasets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14361","snapshot_observed_at":"2026-08-11T19:24:02.946668Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.946668Z"},"links":{"cited_paper":"/paper/2404.14361","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:abedd3f588e091c04d73c39ef07337dbcda7d9289dfa77d3026abad8076e6858","observation_id":"a2e0b2d0-3ef1-4a7a-924c-c6a55250f753","resolution":{"observed_at":"2026-08-11T19:24:02.946668Z","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":"2026-08-11T19:24:04.007336Z","title":null,"venue":null,"work_id":"e767cb0f-a253-4c2d-aa69-aacaf88607f6","year":2020},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.950602Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:aad4e8e98a1f36a56003ec7d6c6794b6e210fd6226b7fbdad434cd8d921aec9c","observation_id":"c2a8e880-0f75-4aea-8479-175cdd31e9ff","resolution":{"observed_at":"2026-08-11T19:24:04.011388Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-08-17T20:30:34.016254Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-11T19:24:02.954349Z","title":"Jiang, Alexandre Sablayrolles, and etc","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.954349Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:d78718e135194859a4d0417d6191275e879550a7894c10a7bb7bbf45799547e6","observation_id":"dc2e8651-c5a1-484f-b877-a8bd4287786c","resolution":{"observed_at":"2026-08-11T19:24:02.954349Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:02.958551Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.958551Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:c31ec4e5a8660aab8919c75518655f966c7f663902efe6ffd76ed28debeb4f16","observation_id":"9c9afa64-7dfb-4fd2-a6d6-8b95a2847378","resolution":{"observed_at":"2026-08-11T19:24:02.958551Z","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":"2026-08-11T19:24:03.988550Z","title":null,"venue":null,"work_id":"64138061-a722-4bea-ba5b-1b3328fdadda","year":2011},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.962607Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:76d9d3458aedf5d915fef2a979c6a686f6f955393e8ce67ef69b7d4fa12196d8","observation_id":"c2956e7f-c6be-4d47-b963-a5861c2aa2ae","resolution":{"observed_at":"2026-08-11T19:24:03.993080Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/p19-1586","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:03.106363Z","title":null,"venue":null,"work_id":"1befaf36-91c0-4460-8f35-efcddc9836a3","year":2019},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.966183Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:b73de1e92f4a3ffa024e3c4a2610426f7c61dc1f818ad87a62aeb9e8f7a154b3","observation_id":"c4e20b9c-df8b-43b7-8ba0-9a6da27b858b","resolution":{"observed_at":"2026-08-11T19:24:03.111268Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.970691Z","title":null,"venue":null,"work_id":"ad6bdfaf-82ea-4db0-95e2-618a45fb9644","year":2016},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.969992Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:0cec029ea66d3557d18794b189ba415867c243ccfce5347c36b14aab47a4c99f","observation_id":"a23d16dd-ffd2-484f-9365-19090fdd7b28","resolution":{"observed_at":"2026-08-11T19:24:03.975192Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.958810Z","title":null,"venue":null,"work_id":"a70a4fbf-59a9-430c-a2b8-c2a6a366d327","year":2023},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.973644Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:41f043b81ddc053853871e62db93964af1a4ce9bcf363e96ffb81c7c8d6426b3","observation_id":"ff855991-d885-4454-822f-ee001faf78e9","resolution":{"observed_at":"2026-08-11T19:24:03.962792Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.946957Z","title":null,"venue":null,"work_id":"1a5ebff3-89ab-40a8-8663-be2399033b82","year":2021},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.977065Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:5d4bd048fbcd0488efc6d39e3df266b18a904a007fdc22fd1972d579309f05f1","observation_id":"3b324d4f-31ae-4249-a21f-9f4dd3a692c2","resolution":{"observed_at":"2026-08-11T19:24:03.950890Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:03.767912Z","title":null,"venue":null,"work_id":"8cee764f-059e-474d-b50e-145906d5a46a","year":2020},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.980255Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:514ac4ac14e41987a57ce1c681e00323ed846678a787c49422449dc7ab44b1de","observation_id":"deeff390-9e5a-4ac2-ba0f-7d9aceff1c08","resolution":{"observed_at":"2026-08-11T19:24:03.771261Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.934856Z","title":null,"venue":null,"work_id":"a7a18747-5c4e-4ebe-bc62-eca5147b44e4","year":2019},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.983649Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:adffce42b647896813a8f8b8f37e138463650554113feca2796f2d1bec6657dd","observation_id":"1d62386e-5dec-4c15-9b4a-18aa86a529c0","resolution":{"observed_at":"2026-08-11T19:24:03.938473Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.924471Z","title":null,"venue":null,"work_id":"000376c4-6c3b-4614-b267-520911c56ece","year":2019},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.987284Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:89f97458cca56c2b8072543571ee18511847b848eaa9d6c3fad1e12b1021b413","observation_id":"92aa4c08-c90f-4dc1-9200-c8980350a8e9","resolution":{"observed_at":"2026-08-11T19:24:03.927905Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.11366","last_updated":"2025-03-14T13:04:39Z","snapshot_observed_at":"2026-08-19T04:04:25.868471Z","submitted_at":"2025-03-14T13:04:39Z","title":"Step-by-Step Data Cleaning Recommendations to Improve ML Prediction Accuracy","version":1},"cited_work":{"arxiv_id":"2503.11366","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.11366","snapshot_observed_at":"2026-08-11T19:24:03.598723Z","title":"Step-by-Step Data Cleaning Recommendations to Improve ML Prediction Accuracy","venue":"cs.DB","work_id":"fe354c59-212f-4575-ad3c-71c6679ee3d5","year":2025},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.991343Z"},"links":{"cited_paper":"/paper/2503.11366","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:0f7a8987dc3be5a79c7f68b42a191d8242b887eaaebc897d84c87e4336a59bfa","observation_id":"a921005e-ab7e-46cc-899b-0868e455415e","resolution":{"observed_at":"2026-08-11T19:24:03.602981Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.913966Z","title":null,"venue":null,"work_id":"0af002a0-0c12-441b-bdb2-ff0439f3cc88","year":2022},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.994873Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:335468e748c2eb4fd666ddc47d7712e554f761ac93ca0379a1daca03525a2146","observation_id":"b80d7c1c-5bc8-4e28-9cbc-fd3a1b6251aa","resolution":{"observed_at":"2026-08-11T19:24:03.917455Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.903028Z","title":null,"venue":null,"work_id":"b97adce7-bfd4-4d3b-acce-69d3a8b8eec6","year":2010},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:02.998473Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:570036989a403cec378388f8a08634920f0e5c40be4fc1b7b5bd43a6de556303","observation_id":"695842e4-683a-4e43-a5b4-dee76ffd4aec","resolution":{"observed_at":"2026-08-11T19:24:03.906745Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:03.582518Z","title":"Lee, and Richard Y","venue":null,"work_id":"f1fe9c5e-1588-40b9-8bec-bdb622eeb12e","year":2002},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.002124Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:11e60675abb213626081e915215049ef3efad648c34a10ec5be4896d38b54320","observation_id":"a0225fec-5f17-47e6-aa96-3ba7749cc7b0","resolution":{"observed_at":"2026-08-11T19:24:03.586632Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08291","last_updated":"2025-06-02T00:45:04Z","snapshot_observed_at":"2026-08-16T14:10:18.642201Z","submitted_at":"2024-03-13T06:54:15Z","title":"CleanAgent: Automating Data Standardization with LLM-based Agents","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08291","snapshot_observed_at":"2026-08-11T19:24:03.005429Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.005429Z"},"links":{"cited_paper":"/paper/2403.08291","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:0c38575c6f893c7f66694b014a2c7027d5319961db11e7f93bbba5e39121f1bc","observation_id":"f50b0d58-c5f5-4f88-8e7c-644671a85c66","resolution":{"observed_at":"2026-08-11T19:24:03.005429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.00820","last_updated":"2017-02-02T20:25:41Z","snapshot_observed_at":"2026-08-14T21:18:13.342219Z","submitted_at":"2017-02-02T20:25:41Z","title":"HoloClean: Holistic Data Repairs with Probabilistic Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.00820","snapshot_observed_at":"2026-08-11T19:24:03.008851Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.008851Z"},"links":{"cited_paper":"/paper/1702.00820","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:48be271df496ab463748b574f0d384accd77be84c8ce9e7021102a7ce07aaa64","observation_id":"8321aa7f-f8d8-4da6-9311-0f44c3d7dc77","resolution":{"observed_at":"2026-08-11T19:24:03.008851Z","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":"2026-08-11T19:24:03.891200Z","title":null,"venue":null,"work_id":"0917dbdb-18d9-4484-bcc3-3625ca393587","year":2019},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.012208Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:696daabe467a340fb4c604d47717d818d3392b394fc1b75fbb742fe72938dd11","observation_id":"638c42b8-3510-448c-88ce-fedde6e54bc7","resolution":{"observed_at":"2026-08-11T19:24:03.895896Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00118","last_updated":"2024-10-02T15:22:49Z","snapshot_observed_at":"2026-08-21T05:15:28.840279Z","submitted_at":"2024-07-31T19:13:07Z","title":"Gemma 2: Improving Open Language Models at a Practical Size","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00118","snapshot_observed_at":"2026-08-11T19:24:03.015357Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.015357Z"},"links":{"cited_paper":"/paper/2408.00118","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:de52d5389564037a2bf139287f4069340c2e8fa71d60aee64a13a590b1a14d41","observation_id":"ece63c6d-2c92-4fd3-9f22-1e483ccfde55","resolution":{"observed_at":"2026-08-11T19:24:03.015357Z","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":"2026-08-11T19:24:03.879357Z","title":null,"venue":null,"work_id":"f03ba5fe-7641-446e-ac5f-2a2805efb399","year":2023},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.018653Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:a9f3bbb0e7bbd3cc86f88f85c2d5b36ff26def9610640bc34382027b7c1e0830","observation_id":"234f196e-ddce-4c4f-8a93-527ecb938711","resolution":{"observed_at":"2026-08-11T19:24:03.883521Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.867439Z","title":null,"venue":null,"work_id":"94280d17-361d-4a86-b204-589bf5247ad7","year":2012},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.022072Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:c716335c91cbeb89da4c41c975c81b4f11a5d773003992e6aa376c53763c076f","observation_id":"cbaf170f-616b-4615-adf8-d1e637f12b4a","resolution":{"observed_at":"2026-08-11T19:24:03.871345Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.855154Z","title":null,"venue":null,"work_id":"5dc7c8bb-c99f-47b3-98f2-756b25f0d8db","year":2018},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.025212Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:3b6fcb8d9f57c94477a46458e1a9ab1b0f3de4608679fc24237e494c6373d9dc","observation_id":"47315f49-086a-4491-a11e-17d246e8ebf4","resolution":{"observed_at":"2026-08-11T19:24:03.858677Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.844332Z","title":null,"venue":null,"work_id":"c1bfa432-a64a-4a26-8c72-9842e33328aa","year":2022},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.029769Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:d4f3d8e9ae92400e39c8cc1b2eecbf918ea8d1ee65ac3ba9835dd252a5bb2805","observation_id":"55f58d29-721f-433d-8182-ebb62c39f2bf","resolution":{"observed_at":"2026-08-11T19:24:03.848024Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.033747Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.033747Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:a604e9d3bcb4f410ebefa273453e2f48bc37a419c8c8d06c3b6add38bed8de82","observation_id":"9d2d9ba0-cf39-46ba-940e-66035871167a","resolution":{"observed_at":"2026-08-11T19:24:03.033747Z","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":"2026-08-11T19:24:03.833749Z","title":null,"venue":null,"work_id":"4acd57d4-bc2e-4c11-a6fe-f8af7f6a1441","year":1996},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.037108Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:eca2c131e082817dd960f28b2263fb0d951e21d861ec075d0c399a6a20b85a64","observation_id":"9a706449-5fa1-4356-9275-98747d08b7d7","resolution":{"observed_at":"2026-08-11T19:24:03.837491Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T19:24:03.821229Z","title":null,"venue":null,"work_id":"36ebad6e-afe4-4592-b630-9d930a07799f","year":2023},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.040269Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:f706156f25f5053ed999bbdccdaf2e1a6405a76f645ddfad867dbcf82d80c4bb","observation_id":"d3e70e25-ad94-4c69-a5c8-aff80f9cc6a9","resolution":{"observed_at":"2026-08-11T19:24:03.825076Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:03.399746Z","title":"Wang and Diane M","venue":null,"work_id":"01300974-8ed0-4c6d-8124-d15456d7bbbb","year":1996},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.043676Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:f4529d8aa8ab4b1854bf7c81abd0b61a5d8313a976216f1d2236ff8d1354d626","observation_id":"26ccb36a-f4fc-4579-9f11-77a168800639","resolution":{"observed_at":"2026-08-11T19:24:03.403580Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04398","last_updated":"2024-01-19T01:05:05Z","snapshot_observed_at":"2026-08-16T14:28:54.148674Z","submitted_at":"2024-01-09T07:46:26Z","title":"Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04398","snapshot_observed_at":"2026-08-11T19:24:03.047057Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.047057Z"},"links":{"cited_paper":"/paper/2401.04398","citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:0a4cf8ecd900f0c0a57a7f0127628ee03479672860554c8dc4b822a4ba4e0173","observation_id":"c5b01593-f8c8-42a9-ae03-66d8debfc691","resolution":{"observed_at":"2026-08-11T19:24:03.047057Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:24:03.050815Z","title":"Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T19:24:03.050815Z"},"links":{"citing_paper":"/paper/2412.06724"},"observation_digest":"sha256:bd12e234a034fd6167fd9069cbfc7919fbec6725c7aa592b37c8cae36b29cd9c","observation_id":"8d329b5d-4e50-4269-afe3-6f160a761828","resolution":{"observed_at":"2026-08-11T19:24:03.050815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.06724","last_updated":"2025-08-23T21:01:08Z","latest_version":3,"primary_category":"cs.DB","snapshot_observed_at":"2026-08-20T12:31:19.016915Z","submitted_at":"2024-12-09T18:13:27Z","title":"AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":35,"verified_exact":5,"verified_fuzzy":1},"total_outbound_references":42},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 11 inbound Pith citation observations for arXiv:2412.06724."}