{"as_of":"2026-08-08T03:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:73145479208941018734be25945caaa5a65d2cae48f597e70e5a97328fc9421b","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:36:12.621689Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-15T19:56:33.623913Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-08-07T14:36:12.621689Z","title":"Preprint, arXiv:2410.06011","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.18363","last_updated":"2025-05-23T20:42:36Z","snapshot_observed_at":"2026-08-07T14:30:25.653265Z","submitted_at":"2025-05-23T20:42:36Z","title":"SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:36:12.621689Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2505.18363"},"observation_digest":"sha256:c34d4a4a399c26ef885741210e6356a5b9d1eb4451eaac03cdf7e02c628e715b","observation_id":"b167db15-fe72-4943-af85-166a11cebea9","resolution":{"observed_at":"2026-08-07T14:36:12.621689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-08-07T13:12:22.398528Z","title":"Large language model enhanced text- to-sql generation: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22698","last_updated":"2025-05-28T14:31:14Z","snapshot_observed_at":"2026-08-07T13:05:39.306915Z","submitted_at":"2025-05-28T14:31:14Z","title":"Design and testing of an agent chatbot supporting decision making with public transport data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:22.398528Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2505.22698"},"observation_digest":"sha256:35db93c3ee9f52a52adfadd2690ac16458e2c175efdad9f8dc82e9b89ce1553c","observation_id":"4be7d91c-5d78-4920-8216-68437ba86030","resolution":{"observed_at":"2026-08-07T13:12:22.398528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-08-07T00:41:26.045268Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13188","last_updated":"2025-06-16T07:55:44Z","snapshot_observed_at":"2026-08-07T00:34:23.435428Z","submitted_at":"2025-06-16T07:55:44Z","title":"SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T00:41:26.045268Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2506.13188"},"observation_digest":"sha256:069d0813714ae68bacd0354e5a3f356bde2d4f7e2ff19e4fc3c601405ba013e6","observation_id":"ee762e8c-faa4-4c16-b487-5244e5ada837","resolution":{"observed_at":"2026-08-07T00:41:26.045268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-08-06T19:44:01.871291Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.04748","last_updated":"2025-07-07T08:19:17Z","snapshot_observed_at":"2026-08-06T22:56:27.170678Z","submitted_at":"2025-07-07T08:19:17Z","title":"LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T19:44:01.871291Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2507.04748"},"observation_digest":"sha256:00b64733ebbbfeca3c171b44af331a931ecb0b674f9c48cfbde56207c14b657f","observation_id":"28a1f14c-8fec-43d9-b2e9-a0bbb54e5ba9","resolution":{"observed_at":"2026-08-06T19:44:01.871291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-08-05T15:31:15.349649Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19807","last_updated":"2025-08-27T11:50:42Z","snapshot_observed_at":"2026-08-05T15:31:14.634045Z","submitted_at":"2025-08-27T11:50:42Z","title":"Bootstrapping Learned Cost Models with Synthetic SQL Queries","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T15:31:15.349649Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2508.19807"},"observation_digest":"sha256:db94fc0cb9b66ca7f9ada25425dcf020c59ec8d3a44b71f3227bc6a8f4e80916","observation_id":"0908d21d-6698-46a5-a7de-548141c814bf","resolution":{"observed_at":"2026-08-05T15:31:15.349649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":"2410.06011","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Which year has the most number of races? The most number of races refers to max(round)","venue":null,"work_id":"c18ee21b-0166-48ff-99c7-ab966804e0ca","year":2024},"citing_paper":{"arxiv_id":"2602.21480","last_updated":"2026-04-13T13:29:15Z","snapshot_observed_at":"2026-07-06T22:46:58.146051Z","submitted_at":"2026-02-25T01:12:35Z","title":"Both Ends Count! Just How Good are LLM Agents at \"Text-to-Big SQL\"?","version":4},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-15T19:52:53.443887Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2602.21480"},"observation_digest":"sha256:a6dc395f93c63c3ab9dafba0ca7cba772c3c805f9d69648217999ffd823f0be9","observation_id":"b5955f86-f8ca-4b02-becb-f3ea51f35871","resolution":{"observed_at":"2026-05-15T19:56:33.627707Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":"2410.06011","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Which year has the most number of races? The most number of races refers to max(round)","venue":null,"work_id":"c18ee21b-0166-48ff-99c7-ab966804e0ca","year":2024},"citing_paper":{"arxiv_id":"2604.16493","last_updated":"2026-04-13T18:00:05Z","snapshot_observed_at":"2026-07-06T23:03:48.168078Z","submitted_at":"2026-04-13T18:00:05Z","title":"NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-05-10T15:48:27.632459Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2604.16493"},"observation_digest":"sha256:97c8a91b47b893981a2be3183f565aac966a02d695edb5896a3601187cc5fe7c","observation_id":"8382b7c6-fd2f-401f-be2a-7a62b99adcdb","resolution":{"observed_at":"2026-05-11T09:50:59.262207Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":"2410.06011","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Which year has the most number of races? The most number of races refers to max(round)","venue":null,"work_id":"c18ee21b-0166-48ff-99c7-ab966804e0ca","year":2024},"citing_paper":{"arxiv_id":"2605.04065","last_updated":"2026-05-07T04:49:30Z","snapshot_observed_at":"2026-07-06T23:16:54.673178Z","submitted_at":"2026-04-11T07:26:04Z","title":"Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs","version":2},"reference_index":129,"source":"arxiv_source","source_observed_at":"2026-05-10T16:58:10.013475Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2605.04065"},"observation_digest":"sha256:e994144a9673a00c05f0a2ceb0bdd6993cc17fa95d165ceb4d88a7ec1e1340f8","observation_id":"dc3355d0-c509-4be0-b05b-2c27118be14b","resolution":{"observed_at":"2026-05-11T07:45:59.818117Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey","version":1},"cited_work":{"arxiv_id":"2410.06011","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.06011","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Which year has the most number of races? The most number of races refers to max(round)","venue":null,"work_id":"c18ee21b-0166-48ff-99c7-ab966804e0ca","year":2024},"citing_paper":{"arxiv_id":"2605.04066","last_updated":"2026-05-07T04:57:40Z","snapshot_observed_at":"2026-08-02T15:49:26.057284Z","submitted_at":"2026-04-11T07:34:59Z","title":"Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning","version":2},"reference_index":114,"source":"arxiv_source","source_observed_at":"2026-05-10T16:51:19.555272Z"},"links":{"cited_paper":"/paper/2410.06011","citing_paper":"/paper/2605.04066"},"observation_digest":"sha256:4f9841f29f95e366995ce8d0924b104fede1a16cdb6999124f09d215f05abec8","observation_id":"ee1045f5-6aca-4b24-87d8-945907f1bd60","resolution":{"observed_at":"2026-05-11T08:01:00.741856Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.06011/citation-record","integrity":"/paper/2410.06011/integrity","json":"/paper/2410.06011/citation-record.json","paper":"/paper/2410.06011"},"outbound":[],"paper":{"arxiv_id":"2410.06011","last_updated":"2024-10-08T13:09:52Z","latest_version":1,"primary_category":"cs.DB","snapshot_observed_at":"2026-07-06T19:29:46.997580Z","submitted_at":"2024-10-08T13:09:52Z","title":"Large Language Model Enhanced Text-to-SQL Generation: A Survey"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2410.06011."}