{"as_of":"2026-08-12T16:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:01057fca53e5f058b55357b13f7f05cf4030187a88efb09b63547611a19d2531","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:21:22.070801Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T19:30:45.111981Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05850","snapshot_observed_at":"2026-08-04T19:30:45.111981Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09234","last_updated":"2025-09-11T08:12:38Z","snapshot_observed_at":"2026-08-08T05:31:35.054631Z","submitted_at":"2025-09-11T08:12:38Z","title":"Agentic LLMs for Question Answering over Tabular Data","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-04T19:30:45.111981Z"},"links":{"cited_paper":"/paper/2412.05850","citing_paper":"/paper/2509.09234"},"observation_digest":"sha256:a6d1fe557cd917355427129a800883bdb75b1320d85dbb6260174e5e2b828441","observation_id":"4d512271-410a-469b-8ff1-72c469334c9c","resolution":{"observed_at":"2026-08-04T19:30:45.111981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.05850/citation-record","integrity":"/paper/2412.05850/integrity","json":"/paper/2412.05850/citation-record.json","paper":"/paper/2412.05850"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.06241","last_updated":"2019-06-03T05:30:41Z","snapshot_observed_at":"2026-08-12T12:25:38.385541Z","submitted_at":"2019-05-15T15:22:01Z","title":"Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.06241","snapshot_observed_at":"2026-08-11T20:21:21.933656Z","title":"https://arxiv.org/abs/1905.06241","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.933656Z"},"links":{"cited_paper":"/paper/1905.06241","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:0714f7929b7463463491a69d845b6d88e77cad6be22c135b74c2e321a38b6515","observation_id":"dcd740b5-191c-48b3-96de-91dd01c26f7a","resolution":{"observed_at":"2026-08-11T20:21:21.933656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01093","last_updated":"2021-06-10T02:49:10Z","snapshot_observed_at":"2026-08-09T05:56:09.114187Z","submitted_at":"2021-06-02T11:53:35Z","title":"LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01093","snapshot_observed_at":"2026-08-11T20:21:21.938688Z","title":"https:// arxiv.org/abs/2106.01093","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.938688Z"},"links":{"cited_paper":"/paper/2106.01093","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:5bd98be681bb9c23b9ff219c6d0af850d6cbc6994661c69cd2b27bb63a3c6512","observation_id":"6dffd851-6577-4e65-8817-00bd761ff0f4","resolution":{"observed_at":"2026-08-11T20:21:21.938688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.04689","last_updated":"2021-04-14T07:06:55Z","snapshot_observed_at":"2026-08-12T12:25:37.447861Z","submitted_at":"2021-04-10T05:48:28Z","title":"ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser","version":2},"cited_work":{"arxiv_id":"2104.04689","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.04689","snapshot_observed_at":"2026-08-11T20:21:22.443212Z","title":"ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser","venue":"cs.CL","work_id":"54740c84-3b59-46f4-a621-fb954d1636d8","year":2021},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.943056Z"},"links":{"cited_paper":"/paper/2104.04689","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:15071997b23f5bb25c7ac2a2cfd2e12acab87f9c7084037cab1f182a246b283c","observation_id":"c2ba2f49-9e83-4068-9722-56ef30e8f697","resolution":{"observed_at":"2026-08-11T20:21:22.448218Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.03125","last_updated":"2020-04-07T04:51:04Z","snapshot_observed_at":"2026-08-09T10:30:30.423675Z","submitted_at":"2020-04-07T04:51:04Z","title":"RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.03125","snapshot_observed_at":"2026-08-11T20:21:21.947696Z","title":"https://arxiv.org/abs/2004.03125","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.947696Z"},"links":{"cited_paper":"/paper/2004.03125","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:fce019c0a5116b9ba4a278b9dd2849a8824786a4cf97d4765c3c47da7ec90014","observation_id":"61785c82-7d9d-4b5d-a4ff-e98da53d54fd","resolution":{"observed_at":"2026-08-11T20:21:21.947696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-11T20:21:21.952499Z","title":"https://arxiv.org/abs/ 1810.04805","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.952499Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:7bbc5d520d16e8ef4a6ace18cc2d45271437cf661f2970e222b5f6f3552527de","observation_id":"8d59ed22-0753-4450-8cd6-7e0f96f2f4e8","resolution":{"observed_at":"2026-08-11T20:21:21.952499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.08205","last_updated":"2019-05-29T02:50:00Z","snapshot_observed_at":"2026-08-07T07:07:12.547469Z","submitted_at":"2019-05-20T16:44:00Z","title":"Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.08205","snapshot_observed_at":"2026-08-11T20:21:21.957055Z","title":"https://arxiv.org/abs/1905.08205","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.957055Z"},"links":{"cited_paper":"/paper/1905.08205","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:bbdee7a63e0d21c929e6ba1b0f1dd1b3ca53491ae7e9c541fcc456b9817bf68b","observation_id":"bdf61eb5-106e-45d2-947f-57abb5ebbda6","resolution":{"observed_at":"2026-08-11T20:21:21.957055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.07207","last_updated":"2022-03-08T06:47:17Z","snapshot_observed_at":"2026-08-06T08:07:23.771593Z","submitted_at":"2022-01-18T18:59:45Z","title":"Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.07207","snapshot_observed_at":"2026-08-11T20:21:21.962233Z","title":"https: //arxiv.org/abs/2201.07207","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.962233Z"},"links":{"cited_paper":"/paper/2201.07207","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:50e8529373bd8b0ed19bbcaf188abd0608f292b7356da362e254d503da3fc9b6","observation_id":"a5a96fb4-0278-490f-8549-e0be1f6f2542","resolution":{"observed_at":"2026-08-11T20:21:21.962233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06958","last_updated":"2022-03-14T09:49:15Z","snapshot_observed_at":"2026-08-12T12:27:26.424207Z","submitted_at":"2022-03-14T09:49:15Z","title":"S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.06958","snapshot_observed_at":"2026-08-11T20:21:21.967047Z","title":"https://arxiv.org/abs/2203.06958","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.967047Z"},"links":{"cited_paper":"/paper/2203.06958","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:b867d3f18833041ef221f73d452ce94d6bfb431314bedb187c4809ba2875a042","observation_id":"364ac8d4-8f02-42fe-a3b7-1328240549de","resolution":{"observed_at":"2026-08-11T20:21:21.967047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.00557","last_updated":"2020-11-03T22:22:57Z","snapshot_observed_at":"2026-08-04T18:44:49.700060Z","submitted_at":"2020-02-03T04:52:47Z","title":"Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker","version":2},"cited_work":{"arxiv_id":"2002.00557","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.00557","snapshot_observed_at":"2026-08-11T20:21:22.350753Z","title":"Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker","venue":"cs.CL","work_id":"7123b3cb-9191-4f81-aeeb-885c4b5ba62f","year":2020},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.971721Z"},"links":{"cited_paper":"/paper/2002.00557","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:bf5deca7437e1e1aaeacf5431ab6a84217e51dc5e5d5f0691fa1f7734c393e1d","observation_id":"17bc5298-86be-4546-bc90-382b8c655d37","resolution":{"observed_at":"2026-08-11T20:21:22.355774Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03111","last_updated":"2023-11-15T04:56:25Z","snapshot_observed_at":"2026-07-06T15:23:29.240615Z","submitted_at":"2023-05-04T19:02:29Z","title":"Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03111","snapshot_observed_at":"2026-08-11T20:21:21.976156Z","title":"https://arxiv.org/abs/2305.03111","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.976156Z"},"links":{"cited_paper":"/paper/2305.03111","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:1ebfc8bd2cbf2628431866b8c6b5c2326f8ddf01b9eb15891286389841373b31","observation_id":"4ffc914d-38d1-403d-a51a-45d87ef449ef","resolution":{"observed_at":"2026-08-11T20:21:21.976156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.13547","last_updated":"2023-03-12T04:22:01Z","snapshot_observed_at":"2026-08-12T12:24:50.107331Z","submitted_at":"2023-03-12T04:22:01Z","title":"A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.13547","snapshot_observed_at":"2026-08-11T20:21:21.981603Z","title":"https://arxiv.org/abs/2303.13547","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.981603Z"},"links":{"cited_paper":"/paper/2303.13547","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:b1e2214ddada8cf5f4068bed6dd0971ede44a885142189d6480050a86f6a25e0","observation_id":"98a9951e-6128-42e0-b4ff-e4b30d071494","resolution":{"observed_at":"2026-08-11T20:21:21.981603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13461","last_updated":"2019-10-29T18:01:00Z","snapshot_observed_at":"2026-07-06T08:33:12.534026Z","submitted_at":"2019-10-29T18:01:00Z","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13461","snapshot_observed_at":"2026-08-11T20:21:21.986444Z","title":"https: //arxiv.org/abs/1910.13461","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.986444Z"},"links":{"cited_paper":"/paper/1910.13461","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:29569ec223bba4a0908a816788100a57b42ecc5074cd3144e6d0799b6a922e8b","observation_id":"2c5ad7fd-5574-498a-8c5e-46272ccaaa94","resolution":{"observed_at":"2026-08-11T20:21:21.986444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11015","last_updated":"2023-11-02T20:30:12Z","snapshot_observed_at":"2026-08-12T12:26:09.323307Z","submitted_at":"2023-04-21T15:02:18Z","title":"DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11015","snapshot_observed_at":"2026-08-11T20:21:21.991581Z","title":"https://arxiv.org/abs/2304.11015","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.991581Z"},"links":{"cited_paper":"/paper/2304.11015","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:0fd5b29fc7c7906fcef8f9e8ec0bd48d1c6e2b1eb3ea2732f2235d6219b100a3","observation_id":"5b325c50-a18e-49f0-b547-4171e7d7da01","resolution":{"observed_at":"2026-08-11T20:21:21.991581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.13629","last_updated":"2022-08-29T14:24:13Z","snapshot_observed_at":"2026-08-11T20:11:07.628949Z","submitted_at":"2022-08-29T14:24:13Z","title":"A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.13629","snapshot_observed_at":"2026-08-11T20:21:21.996220Z","title":"https://arxiv.org/abs/2208.13629","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:21.996220Z"},"links":{"cited_paper":"/paper/2208.13629","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:4b678c123e668daf292494052c7e376c2e6cfd67285f56e2be4b47f000884513","observation_id":"6bb6ee2c-8356-452a-8ee1-8fd08e20b517","resolution":{"observed_at":"2026-08-11T20:21:21.996220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.10683","last_updated":"2023-09-19T15:14:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-10-23T17:37:36Z","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.10683","snapshot_observed_at":"2026-08-11T20:21:22.000950Z","title":"https://arxiv.org/abs/1910.10683","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.000950Z"},"links":{"cited_paper":"/paper/1910.10683","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:b06a6170facdfadfc292783ed0e2635f9e1fe7bfff7fab0a8a2266b2bcf09393","observation_id":"20a1500b-bf54-44e0-9030-225e1103a2d8","resolution":{"observed_at":"2026-08-11T20:21:22.000950Z","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":"2026-08-11T20:21:22.568135Z","title":"In: Adaptive Agents and Multi-Agent Systems (2006)","venue":null,"work_id":"d6648d2b-6921-4791-8a1f-62308836cda2","year":2006},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.004927Z"},"links":{"citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:ef52169b18cbb6d1e23b2c353a2793ccce29fccd8655ec825371e9f441532772","observation_id":"60159889-573e-4963-8d71-129f81f6644c","resolution":{"observed_at":"2026-08-11T20:21:22.573835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04088","last_updated":"2023-03-30T04:50:44Z","snapshot_observed_at":"2026-08-09T23:21:40.566096Z","submitted_at":"2022-12-08T05:46:32Z","title":"LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04088","snapshot_observed_at":"2026-08-11T20:21:22.008963Z","title":"https://arxiv.org/abs/2212.04088","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.008963Z"},"links":{"cited_paper":"/paper/2212.04088","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:2b79efdb034d406b82325e023ce8dd29e338b5a977ee294ab4d9911b666e5d54","observation_id":"7df70267-ae26-4790-82d9-f844f4ff0233","resolution":{"observed_at":"2026-08-11T20:21:22.008963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10179","last_updated":"2024-10-11T16:30:24Z","snapshot_observed_at":"2026-08-12T12:27:19.697497Z","submitted_at":"2024-03-13T17:50:32Z","title":"Scaling Instructable Agents Across Many Simulated Worlds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10179","snapshot_observed_at":"2026-08-11T20:21:22.013303Z","title":"https://arxiv.org/abs/2404.10179","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.013303Z"},"links":{"cited_paper":"/paper/2404.10179","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:674843ebde5c7760779cbf1fb9fbc039a25bd2fc50e90d5c3a84316baa38dd51","observation_id":"920ddb6a-075c-40f5-a5c7-c1f27a3c27de","resolution":{"observed_at":"2026-08-11T20:21:22.013303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03463","last_updated":"2025-02-22T08:32:05Z","snapshot_observed_at":"2026-08-12T12:26:08.240989Z","submitted_at":"2023-12-06T12:37:28Z","title":"DBCopilot: Natural Language Querying over Massive Databases via Schema Routing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03463","snapshot_observed_at":"2026-08-11T20:21:22.017553Z","title":"https://arxiv.org/abs/ 2312.03463","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.017553Z"},"links":{"cited_paper":"/paper/2312.03463","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:f7c6332f7f1c4bb26f50a9c8b5fe481d5184366fa3c6b956d9d865dbeb6c58e9","observation_id":"e01b482a-c26c-4f5d-9fac-d104ff7b8f96","resolution":{"observed_at":"2026-08-11T20:21:22.017553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11242","last_updated":"2025-03-18T02:12:21Z","snapshot_observed_at":"2026-08-12T12:26:11.851481Z","submitted_at":"2023-12-18T14:40:20Z","title":"MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11242","snapshot_observed_at":"2026-08-11T20:21:22.021902Z","title":"https://arxiv.org/abs/2312.11242 15","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.021902Z"},"links":{"cited_paper":"/paper/2312.11242","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:5346160219d9c0ed0883281d861a7e9960cba534b0bc6549ce20028d31893f91","observation_id":"4bce3785-05e6-4116-8b8a-e551480ad0e0","resolution":{"observed_at":"2026-08-11T20:21:22.021902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.09769","last_updated":"2018-04-25T19:35:56Z","snapshot_observed_at":"2026-08-11T15:14:15.157061Z","submitted_at":"2018-04-25T19:35:56Z","title":"TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.09769","snapshot_observed_at":"2026-08-11T20:21:22.026493Z","title":"https://arxiv.org/abs/1804.09769","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.026493Z"},"links":{"cited_paper":"/paper/1804.09769","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:e973fd8c198f6c5a3dec5b14013a0796dccfaf598cf836ad76f5d0d1b1860756","observation_id":"ca30302b-92c2-4df5-ba05-4a3bb3790f15","resolution":{"observed_at":"2026-08-11T20:21:22.026493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.08314","last_updated":"2020-05-17T17:26:40Z","snapshot_observed_at":"2026-08-10T12:14:33.431052Z","submitted_at":"2020-05-17T17:26:40Z","title":"TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.08314","snapshot_observed_at":"2026-08-11T20:21:22.030507Z","title":"https://arxiv.org/abs/2005.08314","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.030507Z"},"links":{"cited_paper":"/paper/2005.08314","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:369ff234f765ed3392defb59abf64c72753f1fb23e8fc46c9509f347341d127a","observation_id":"f487f75e-5c2c-42ee-9f59-5dedee2b695c","resolution":{"observed_at":"2026-08-11T20:21:22.030507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.13845","last_updated":"2021-05-29T01:30:29Z","snapshot_observed_at":"2026-08-12T12:26:15.849448Z","submitted_at":"2020-09-29T08:17:58Z","title":"GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.13845","snapshot_observed_at":"2026-08-11T20:21:22.034421Z","title":"https://arxiv.org/abs/2009.13845","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.034421Z"},"links":{"cited_paper":"/paper/2009.13845","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:6d9c48b71868bade28b85b42fb5911feb26fe06b91c8dbaa81ef82dce6e90e34","observation_id":"e6c28e75-58cf-4e36-a081-b4a9da1b877b","resolution":{"observed_at":"2026-08-11T20:21:22.034421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05237","last_updated":"2018-10-25T20:33:55Z","snapshot_observed_at":"2026-08-12T12:27:20.545379Z","submitted_at":"2018-10-11T20:24:13Z","title":"SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05237","snapshot_observed_at":"2026-08-11T20:21:22.038234Z","title":"https://arxiv.org/abs/1810.05237","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.038234Z"},"links":{"cited_paper":"/paper/1810.05237","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:2941a06e7c32a71d44a2e9a2f7b78cdbaca4249b237791079cc2a2a1ebd602cf","observation_id":"26a5c927-4dc5-4169-89ee-272f8146d8f0","resolution":{"observed_at":"2026-08-11T20:21:22.038234Z","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":"2026-08-11T20:21:22.550361Z","title":"(eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp","venue":null,"work_id":"091b229f-8e7e-4648-8786-0fbca8a947e9","year":2018},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.042206Z"},"links":{"citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:288f8c54d25b3f111610a36780f92abfcc8446b399ca666e02052aef53957781","observation_id":"1a703947-c152-44b1-bf1f-e28f08e5ed50","resolution":{"observed_at":"2026-08-11T20:21:22.555513Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T20:21:22.534957Z","title":"IEEE Transactions on Big Data 9(01), 118–132 (2023)","venue":null,"work_id":"c3e4f3ea-dc72-49d0-a6ac-aa933b6a0178","year":2023},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.046208Z"},"links":{"citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:448d77ef7ec2aa70d8b624d94028bd549e46b2ae58894afc27843b95dce210cb","observation_id":"8b05a4bd-5cc6-475e-be63-3b12606be84f","resolution":{"observed_at":"2026-08-11T20:21:22.539356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.02485","last_updated":"2024-02-17T05:27:56Z","snapshot_observed_at":"2026-08-02T22:53:12.613357Z","submitted_at":"2023-07-05T17:59:27Z","title":"Building Cooperative Embodied Agents Modularly with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.02485","snapshot_observed_at":"2026-08-11T20:21:22.050235Z","title":"https://arxiv.org/abs/2307.02485","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.050235Z"},"links":{"cited_paper":"/paper/2307.02485","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:2290b503d2b144d4946b2f3fec6e694de89785f9012d3369f7c798d1c87da4e7","observation_id":"d4836d1d-3709-428c-8910-329d26123479","resolution":{"observed_at":"2026-08-11T20:21:22.050235Z","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":"2026-08-11T20:21:22.520865Z","title":"Knowledge-Based Systems 205, 106290 (2020)","venue":null,"work_id":"0a8cfab3-6ceb-4774-9fa4-0ee7237a7b1c","year":2020},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.054398Z"},"links":{"citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:53406646c00ee3b6284e764e182cfa85ab3fd9f0cac324d408880f1b790e4105","observation_id":"7209b28f-27cc-4d36-9310-2a62cd5756a9","resolution":{"observed_at":"2026-08-11T20:21:22.525083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T20:21:22.506076Z","title":"Neural Computing and Applications, 1–14 (2022)","venue":null,"work_id":"0de655e0-6c73-43b7-8b4b-528b1b2b6750","year":2022},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.058600Z"},"links":{"citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:399181b45feff0743dbb3ee7f085ffdedbd7b26900d39530576d675d98588228","observation_id":"2739ac32-6fb1-4572-a223-6667cd09c6d0","resolution":{"observed_at":"2026-08-11T20:21:22.510554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T20:21:22.489059Z","title":"IEEE Transactions on Neural Networks and Learning Systems (2023)","venue":null,"work_id":"0c960d26-15bc-4f84-82bd-9afb8c7a1928","year":2023},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.062474Z"},"links":{"citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:2a342d6cdada17c18fdae7d534c5f4bf74873dea7c8db6ffd89e82837cf3bfaa","observation_id":"c298bc72-843b-42c7-adac-8df21c8df351","resolution":{"observed_at":"2026-08-11T20:21:22.494186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.00786","last_updated":"2019-09-10T00:40:29Z","snapshot_observed_at":"2026-08-12T12:26:29.910394Z","submitted_at":"2019-09-02T16:24:57Z","title":"Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions","version":2},"cited_work":{"arxiv_id":"1909.00786","doi":null,"metadata_source":"pith","pith_arxiv_id":"1909.00786","snapshot_observed_at":"2026-08-11T20:21:22.120225Z","title":"Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions","venue":"cs.CL","work_id":"9198808b-961a-4568-a811-459c38e307b3","year":2019},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.066513Z"},"links":{"cited_paper":"/paper/1909.00786","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:7fa7ce640dbaff4de5b7a0572895f59116a600708030a665027ed49f91a7f757","observation_id":"6dd76124-28d7-4e73-88bd-0c49d3bb812f","resolution":{"observed_at":"2026-08-11T20:21:22.126569Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02840","last_updated":"2020-10-06T16:04:12Z","snapshot_observed_at":"2026-08-06T22:05:09.108307Z","submitted_at":"2020-10-06T16:04:12Z","title":"Semantic Evaluation for Text-to-SQL with Distilled Test Suites","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02840","snapshot_observed_at":"2026-08-11T20:21:22.070801Z","title":"https://arxiv.org/abs/2010.02840 17","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:22.070801Z"},"links":{"cited_paper":"/paper/2010.02840","citing_paper":"/paper/2412.05850"},"observation_digest":"sha256:7e8b8a5593f62638cf54edb0a0a953c953f093697ad0f55993914b52d2607de0","observation_id":"c3b7c9c4-6222-4036-8d4e-d216382ed2c0","resolution":{"observed_at":"2026-08-11T20:21:22.070801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.05850","last_updated":"2024-12-08T08:16:19Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-12T12:25:14.806198Z","submitted_at":"2024-12-08T08:16:19Z","title":"Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":1,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":32},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2412.05850."}