{"as_of":"2026-08-08T04:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:882c446fa4f4dfe40fe80eba1a963365fedb565ded81b979699dc2d967302fa5","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:09:04.122733Z","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":"2026-07-01T16:15:49.723373Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":"2403.09732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-07-01T16:15:49.723373Z","title":"arXiv preprint arXiv:2403.09732 , year=","venue":null,"work_id":"cc472432-a71d-42a7-9efe-0245eb71a912","year":2024},"citing_paper":{"arxiv_id":"2405.16755","last_updated":"2024-11-25T19:43:07Z","snapshot_observed_at":"2026-08-02T16:46:24.597803Z","submitted_at":"2024-05-27T01:54:16Z","title":"CHESS: Contextual Harnessing for Efficient SQL Synthesis","version":3},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-05-19T11:24:22.789901Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2405.16755"},"observation_digest":"sha256:f75debd6f8c018e1fc17763523103ae2bc8b79bfa3c7a4424d713227b7e843a2","observation_id":"5fb0f1f0-476f-494f-8954-1adab94f939e","resolution":{"observed_at":"2026-05-19T11:24:22.984285Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-08-07T14:09:04.122733Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19956","last_updated":"2025-07-22T08:42:57Z","snapshot_observed_at":"2026-08-07T14:00:34.690209Z","submitted_at":"2025-05-26T13:19:10Z","title":"DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T14:09:04.122733Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2505.19956"},"observation_digest":"sha256:eacc998ec5bd1cb67165d7618c196523297964756058bfb8606f2f471c107966","observation_id":"1451f352-4625-4e75-a81b-9afa6126cdde","resolution":{"observed_at":"2026-08-07T14:09:04.122733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-08-07T13:12:38.970876Z","title":"Pet-sql: A prompt-enhanced two-stage text-to-sql framework with cross-consistency","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23838","last_updated":"2025-05-28T13:23:38Z","snapshot_observed_at":"2026-08-07T13:07:18.579375Z","submitted_at":"2025-05-28T13:23:38Z","title":"Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:38.970876Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2505.23838"},"observation_digest":"sha256:34adab62ded77e92e8744d54ee0b014801c5072fe4d23117473b4bceee6fb980","observation_id":"f12833c8-9598-4aef-9bd1-ca58b901d297","resolution":{"observed_at":"2026-08-07T13:12:38.970876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-08-07T01:05:26.805058Z","title":"Pet-sql: A prompt-enhanced two-round refinement of text-to-sql with cross-consistency","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11986","last_updated":"2025-06-13T17:46:02Z","snapshot_observed_at":"2026-08-08T01:07:48.504739Z","submitted_at":"2025-06-13T17:46:02Z","title":"Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:26.805058Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2506.11986"},"observation_digest":"sha256:b814727bbbcc247a440c651b72cb982d7b0f212376b4f075d2bf0299cb4d47c1","observation_id":"61a5816f-ef18-4f49-abce-70901c034bc3","resolution":{"observed_at":"2026-08-07T01:05:26.805058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":"2403.09732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-07-01T16:15:49.723373Z","title":"arXiv preprint arXiv:2403.09732 , year=","venue":null,"work_id":"cc472432-a71d-42a7-9efe-0245eb71a912","year":2024},"citing_paper":{"arxiv_id":"2507.04701","last_updated":"2026-04-06T11:21:35Z","snapshot_observed_at":"2026-08-03T23:10:23.802142Z","submitted_at":"2025-07-07T06:50:46Z","title":"XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-19T06:52:57.718359Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2507.04701"},"observation_digest":"sha256:f9312c3e3503033d9168b38895ff51e1dea77d0ffae361325bad0ddcef75a4cc","observation_id":"54ea313c-fa8c-4998-a9b3-c53fe4213f59","resolution":{"observed_at":"2026-05-19T06:52:59.939124Z","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":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":"2403.09732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-07-01T16:15:49.723373Z","title":"arXiv preprint arXiv:2403.09732 , year=","venue":null,"work_id":"cc472432-a71d-42a7-9efe-0245eb71a912","year":2024},"citing_paper":{"arxiv_id":"2601.17942","last_updated":"2026-01-25T18:38:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-01-25T18:38:58Z","title":"LLM-Based SQL Generation: Prompting, Self-Refinement, and Adaptive Weighted Majority Voting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-16T10:59:09.811335Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2601.17942"},"observation_digest":"sha256:9f34cc2acdf8f860cfe6867985854eaa5e47e8cc7619a9fbe4679a66890cc07b","observation_id":"fbd4210a-f73c-479e-b313-7224d4fef36e","resolution":{"observed_at":"2026-05-16T11:00:51.699489Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":"2403.09732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-07-01T16:15:49.723373Z","title":"arXiv preprint arXiv:2403.09732 , year=","venue":null,"work_id":"cc472432-a71d-42a7-9efe-0245eb71a912","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":130,"source":"arxiv_source","source_observed_at":"2026-05-10T16:58:10.013475Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2605.04065"},"observation_digest":"sha256:a45c8a7dd9051905c9416047c0f86e986dce92996c27434d0e191b20393d6ae8","observation_id":"e7b01ae5-bb03-49d9-b8ad-7ff762b80274","resolution":{"observed_at":"2026-05-11T07:45:59.763216Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":"2403.09732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-07-01T16:15:49.723373Z","title":"arXiv preprint arXiv:2403.09732 , year=","venue":null,"work_id":"cc472432-a71d-42a7-9efe-0245eb71a912","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":115,"source":"arxiv_source","source_observed_at":"2026-05-10T16:51:19.555272Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2605.04066"},"observation_digest":"sha256:8b671211a581ed5b51d7cf3cdaebb4a8f682e70e106dbdf94affb3a11a2b4d91","observation_id":"247e79c8-0f14-4017-8c70-c5452ceb6a1a","resolution":{"observed_at":"2026-05-11T08:01:00.861330Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":"2403.09732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-07-01T16:15:49.723373Z","title":"arXiv preprint arXiv:2403.09732 , year=","venue":null,"work_id":"cc472432-a71d-42a7-9efe-0245eb71a912","year":2024},"citing_paper":{"arxiv_id":"2606.23693","last_updated":"2026-04-29T10:33:16Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-04-29T10:33:16Z","title":"EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-07-01T08:57:37.907354Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2606.23693"},"observation_digest":"sha256:11567a58c9ca44541ec6548af8289bf7ba40cc09b9b0e9ea9e9d887ed652a750","observation_id":"96574c03-2c0e-4a13-9fd5-d8dca52fb108","resolution":{"observed_at":"2026-07-01T09:05:36.873932Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2403.09732","last_updated":"2024-06-02T02:58:53Z","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","version":4},"cited_work":{"arxiv_id":"2403.09732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.09732","snapshot_observed_at":"2026-07-01T16:15:49.723373Z","title":"arXiv preprint arXiv:2403.09732 , year=","venue":null,"work_id":"cc472432-a71d-42a7-9efe-0245eb71a912","year":2024},"citing_paper":{"arxiv_id":"2606.28601","last_updated":"2026-06-26T20:49:46Z","snapshot_observed_at":"2026-08-07T19:45:11.404373Z","submitted_at":"2026-06-26T20:49:46Z","title":"Database Context Compression for Text-to-SQL on Real-World Large Databases","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-30T00:49:34.390197Z"},"links":{"cited_paper":"/paper/2403.09732","citing_paper":"/paper/2606.28601"},"observation_digest":"sha256:702c7fffa6bc346b98240d81aabf49ac28bc5e658463c92e205049e7c8e590ac","observation_id":"1a854930-e209-491c-987a-1abbda36f576","resolution":{"observed_at":"2026-07-01T16:15:49.724862Z","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/2403.09732/citation-record","integrity":"/paper/2403.09732/integrity","json":"/paper/2403.09732/citation-record.json","paper":"/paper/2403.09732"},"outbound":[],"paper":{"arxiv_id":"2403.09732","last_updated":"2024-06-02T02:58:53Z","latest_version":4,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T17:44:49.990624Z","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency"},"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 10 inbound Pith citation observations for arXiv:2403.09732."}