Paper Citation Record · LEDGER

Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2409.16663.

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

pith.paper-citation-record.v1
2409.16663 v5

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 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:45.731296Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7019197f-7021-44fe-af2f-a0c6cfa73017 · inbound

GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control cites this paper.

GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:45.731296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6053ebb8-e7a2-4598-b764-97b5e24be9ea · inbound

SimScale: Learning to Drive via Real-World Simulation at Scale cites this paper.

SimScale: Learning to Drive via Real-World Simulation at Scale Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:04.139385Z

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 5710d0a7-b4b9-480c-9b83-a64e35200002 · inbound

Latent Chain-of-Thought World Modeling for End-to-End Driving cites this paper.

Latent Chain-of-Thought World Modeling for End-to-End Driving Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:04.139385Z

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 7d756018-a410-4012-93a2-94ea00bc6590 · inbound

Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation cites this paper.

Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T06:23:15.212552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04e18436-af84-4de2-ae56-8eba56c6ce31 · inbound

Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach cites this paper.

Robotic Affection -- Opportunities of AI-based haptic interactions to improve social robotic touch through a multi-deep-learning approach Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:04.139385Z

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 341dcc03-3fc1-4fde-9baf-e9f3d19f3d03 · inbound

Scaling Self-Play for End-to-End Driving cites this paper.

Scaling Self-Play for End-to-End Driving Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:04.139385Z

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 f17fdc6f-c3f0-4d01-bfe4-cee261d40b1e · inbound

OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model cites this paper.

OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-07T02:17:04.139385Z

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.