Paper Citation Record · LEDGER

When Large Language Models Meet Vector Databases: A Survey

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2402.01763.

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

pith.paper-citation-record.v1
2402.01763 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:17:12.310643Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:24:39.940148Z

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 225362e2-31bb-4db6-bad7-12e8dea1e43e · inbound

SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering cites this paper.

SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering When Large Language Models Meet Vector Databases: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:42:56.490519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74f55d53-48d4-427a-838f-b23a514d5b49 · inbound

SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models cites this paper.

SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models When Large Language Models Meet Vector Databases: A Survey

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T13:31:30.198543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d21e547-7f70-4a2d-b510-e5bc727feb89 · inbound

Addressing the sustainable AI trilemma: a case study on LLM agents and RAG cites this paper.

Addressing the sustainable AI trilemma: a case study on LLM agents and RAG When Large Language Models Meet Vector Databases: A Survey

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T20:34:34.739029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e30b88c-a796-445e-be87-7981f1b421f4 · inbound

MINT: Multi-Vector Search Index Tuning cites this paper.

MINT: Multi-Vector Search Index Tuning When Large Language Models Meet Vector Databases: A Survey

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-22T17:51:55.232461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a618379f-1270-4ac8-9a9a-b2192417ecd0 · inbound

Bang for the Buck: Vector Search on Cloud CPUs cites this paper.

Bang for the Buck: Vector Search on Cloud CPUs When Large Language Models Meet Vector Databases: A Survey

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:12.310643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dfd1c87-bd5b-42c1-9ef9-1f03c45640f8 · inbound

HENN: A Hierarchical Epsilon Net Navigation Graph for Approximate Nearest Neighbor Search cites this paper.

HENN: A Hierarchical Epsilon Net Navigation Graph for Approximate Nearest Neighbor Search When Large Language Models Meet Vector Databases: A Survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:06.369833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c32a7b35-9a0f-4e6f-9b96-792a9a7f5b7c · inbound

DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search cites this paper.

DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search When Large Language Models Meet Vector Databases: A Survey

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:58.996812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 575a285d-fca6-400a-81f9-c3b91fe1eb12 · inbound

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows cites this paper.

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows When Large Language Models Meet Vector Databases: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:25:14.863256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21d105b4-8e57-4a89-b795-80e729746cca · inbound

Hallucination Detection with Small Language Models cites this paper.

Hallucination Detection with Small Language Models When Large Language Models Meet Vector Databases: A Survey

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:41.231064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bea7725e-4ab6-49f4-9476-62be8b6471ad · 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 When Large Language Models Meet Vector Databases: A Survey

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T17:57:49.904820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2731010-478a-4b2a-bdfe-d5ffe2ecbb45 · inbound

Provably Secure Retrieval-Augmented Generation cites this paper.

Provably Secure Retrieval-Augmented Generation When Large Language Models Meet Vector Databases: A Survey

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T05:56:20.272731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa88b888-69fd-4fde-a5c7-422b8dac93f3 · inbound

A Survey on Retrieval And Structuring Augmented Generation with Large Language Models cites this paper.

A Survey on Retrieval And Structuring Augmented Generation with Large Language Models When Large Language Models Meet Vector Databases: A Survey

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-15T15:56:21.433327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 119df05d-0d15-45a3-abb5-43236a06280c · inbound

DGAI: Decoupled On-Disk Graph-Based ANN Index for Efficient Updates and Queries cites this paper.

DGAI: Decoupled On-Disk Graph-Based ANN Index for Efficient Updates and Queries When Large Language Models Meet Vector Databases: A Survey

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:42:22.544911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1d69dd18-d514-4364-a06d-8eb1ba76a813 · inbound

RAGe: A Retrieval-Augmented Generation Evaluation Framework cites this paper.

RAGe: A Retrieval-Augmented Generation Evaluation Framework When Large Language Models Meet Vector Databases: A Survey

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:24:39.941667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 252343ac-118b-48e2-9355-5df298bf67bc · inbound

Stateful Multi-Agent LLMs for Cross-View Interface Alignment in Automotive Model-Based Systems Engineering cites this paper.

Stateful Multi-Agent LLMs for Cross-View Interface Alignment in Automotive Model-Based Systems Engineering When Large Language Models Meet Vector Databases: A Survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T00:36:44.894219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.