Paper Citation Record · LEDGER

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents

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.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.05850 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:21:22.070801Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:30:45.111981Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy5
unresolved23
parse uncertain0
malformed identifier1
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dcd740b5-191c-48b3-96de-91dd01c26f7a · outbound

This paper cites Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.933656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dffd851-6577-4e65-8817-00bd761ff0f4 · outbound

This paper cites LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.938688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2ba2f49-9e83-4068-9722-56ef30e8f697 · outbound

This paper cites ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T20:21:22.448218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 61785c82-7d9d-4b5d-a4ff-e98da53d54fd · outbound

This paper cites RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.947696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d59ed22-0753-4450-8cd6-7e0f96f2f4e8 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.952499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdf61eb5-106e-45d2-947f-57abb5ebbda6 · outbound

This paper cites Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.957055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5a96fb4-0278-490f-8549-e0be1f6f2542 · outbound

This paper cites Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.962233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 364ac8d4-8f02-42fe-a3b7-1328240549de · outbound

This paper cites S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.967047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17bc5298-86be-4546-bc90-382b8c655d37 · outbound

This paper cites Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T20:21:22.355774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4ffc914d-38d1-403d-a51a-45d87ef449ef · outbound

This paper cites Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.976156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98a9951e-6128-42e0-b4ff-e4b30d071494 · outbound

This paper cites A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.981603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c5ad7fd-5574-498a-8c5e-46272ccaaa94 · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.986444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b325c50-a18e-49f0-b547-4171e7d7da01 · outbound

This paper cites DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.991581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bb6ee2c-8356-452a-8ee1-8fd08e20b517 · outbound

This paper cites A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:21.996220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20a1500b-bf54-44e0-9030-225e1103a2d8 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.000950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60159889-573e-4963-8d71-129f81f6644c · outbound

This paper cites In: Adaptive Agents and Multi-Agent Systems (2006).

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents In: Adaptive Agents and Multi-Agent Systems (2006)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:22.573835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7df70267-ae26-4790-82d9-f844f4ff0233 · outbound

This paper cites LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.008963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 920ddb6a-075c-40f5-a5c7-c1f27a3c27de · outbound

This paper cites Scaling Instructable Agents Across Many Simulated Worlds.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Scaling Instructable Agents Across Many Simulated Worlds

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.013303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e01b482a-c26c-4f5d-9fac-d104ff7b8f96 · outbound

This paper cites DBCopilot: Natural Language Querying over Massive Databases via Schema Routing.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents DBCopilot: Natural Language Querying over Massive Databases via Schema Routing

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.017553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bce3785-05e6-4116-8b8a-e551480ad0e0 · outbound

This paper cites MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.021902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca30302b-92c2-4df5-ba05-4a3bb3790f15 · outbound

This paper cites TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.026493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f487f75e-5c2c-42ee-9f59-5dedee2b695c · outbound

This paper cites TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.030507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6c28e75-58cf-4e36-a081-b4a9da1b877b · outbound

This paper cites GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.034421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26a5c927-4dc5-4169-89ee-272f8146d8f0 · outbound

This paper cites SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.038234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a703947-c152-44b1-bf1f-e28f08e5ed50 · outbound

This paper cites (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp

Reference 25

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T20:21:22.555513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8b05a4bd-5cc6-475e-be63-3b12606be84f · outbound

This paper cites IEEE Transactions on Big Data 9(01), 118–132 (2023).

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents IEEE Transactions on Big Data 9(01), 118–132 (2023)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:22.539356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d4836d1d-3709-428c-8910-329d26123479 · outbound

This paper cites Building Cooperative Embodied Agents Modularly with Large Language Models.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Building Cooperative Embodied Agents Modularly with Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.050235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7209b28f-27cc-4d36-9310-2a62cd5756a9 · outbound

This paper cites Knowledge-Based Systems 205, 106290 (2020).

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Knowledge-Based Systems 205, 106290 (2020)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:22.525083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2739ac32-6fb1-4572-a223-6667cd09c6d0 · outbound

This paper cites Neural Computing and Applications, 1–14 (2022).

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Neural Computing and Applications, 1–14 (2022)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:22.510554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c298bc72-843b-42c7-adac-8df21c8df351 · outbound

This paper cites IEEE Transactions on Neural Networks and Learning Systems (2023).

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents IEEE Transactions on Neural Networks and Learning Systems (2023)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:21:22.494186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6dd76124-28d7-4e73-88bd-0c49d3bb812f · outbound

This paper cites Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

Reference 31

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T20:21:22.126569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c3b7c9c4-6222-4036-8d4e-d216382ed2c0 · outbound

This paper cites Semantic Evaluation for Text-to-SQL with Distilled Test Suites.

Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents Semantic Evaluation for Text-to-SQL with Distilled Test Suites

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T20:21:22.070801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4d512271-410a-469b-8ff1-72c469334c9c · inbound

Agentic LLMs for Question Answering over Tabular Data cites this paper.

Agentic LLMs for Question Answering over Tabular Data Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T19:30:45.111981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.