Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Query Optimization Method Utilizing Large Language Models
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.06902.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T17:57:50.063701Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T14:00:53.390048Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5164badd-cc99-42c3-b39a-b4febaaaa1cf · inbound
DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges cites this paper.
DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges A Query Optimization Method Utilizing Large Language Models
Reference 76
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:57:50.063701Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b934e760-bb25-46f7-997d-12f1c35eed58 · inbound
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer cites this paper.
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer A Query Optimization Method Utilizing Large Language Models
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:56:43.049524Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f4c8d4c-1279-49f9-b117-a068cc134674 · inbound
Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server cites this paper.
Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server A Query Optimization Method Utilizing Large Language Models
Reference 82
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T14:00:53.392705Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 73d1611c-4698-474e-a1a5-fe10982ba86b · inbound
Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server cites this paper.
Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server A Query Optimization Method Utilizing Large Language Models
Reference 80
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T18:33:13.972641Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8facb7d-544f-49f6-a8c4-d365c41f555f · inbound
Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning cites this paper.
Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning A Query Optimization Method Utilizing Large Language Models
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T11:01:30.006953Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.