Paper Citation Record · LEDGER

Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2409.06450.

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

pith.paper-citation-record.v1
2409.06450 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:34:44.228032Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:16:27.359137Z

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 016cbd1a-d3a4-4ee7-8e7b-2e2462f63294 · inbound

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles cites this paper.

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:44.228032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 020b9a41-9529-44f4-9899-2a9c1c16e87e · inbound

Generative AI for Testing of Autonomous Driving Systems: A Survey cites this paper.

Generative AI for Testing of Autonomous Driving Systems: A Survey Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 141

Resolution: unresolved; no resolver link, observed 2026-08-05T15:24:15.882341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1e4aef2-2ded-4b11-8d24-37cf6e41e91b · inbound

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models cites this paper.

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:20.211840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8614ccb-aca9-47bd-8eb2-0ea4cd8013d8 · inbound

A Survey on the Applications of Generative Artificial Intelligence in Automated Driving Systems Test Scenario Generation Methods cites this paper.

A Survey on the Applications of Generative Artificial Intelligence in Automated Driving Systems Test Scenario Generation Methods Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T15:54:39.739654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a8b17e7-e50a-4c32-b8f8-4ae23509e25c · inbound

Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models cites this paper.

Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:15:57.122065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation de2d067b-0ddb-4045-affb-bf7d90ce2811 · inbound

Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models cites this paper.

Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:27.372236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ffc5aed4-3b64-4e07-902a-b1beb5e56042 · inbound

Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving cites this paper.

Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T02:17:11.688613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.