Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LMDrive: Closed-Loop End-to-End Driving with Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2312.07488.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:37:07.072292Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T00:20:50.463722Z
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 44e3eb50-33c1-40c1-b1d3-947599904a18 · inbound
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security cites this paper.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Reference 152
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-17T00:57:26.673014Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0bc78738-a895-492a-8a9c-cba99b8b141d · inbound
CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning cites this paper.
CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:37:07.072292Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8682bfc-9686-4646-a071-af17c38d800f · inbound
Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving cites this paper.
Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T00:20:50.466740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7c8d7078-2c7b-4cad-a178-ced6218b842c · inbound
2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model cites this paper.
2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:32:33.037882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d49688d1-aedf-4c03-b07a-b342b08a9024 · inbound
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning cites this paper.
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T05:50:57.245731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation b28847db-6794-4779-abf2-11b7ab06677d · inbound
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning cites this paper.
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T20:50:36.617721Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 98fea146-e806-48a9-990e-080a4856fe87 · inbound
Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving cites this paper.
Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T22:49:35.391193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.