Paper Citation Record · LEDGER

Learning to Drive from a World Model

As of 20 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 2 inbound Pith citation observations for arXiv:2504.19077.

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

pith.paper-citation-record.v1
2504.19077 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:08:44.899109Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:52:08.571333Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T09:32:16.630654Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy25
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2bc1d294-d21e-47ce-9ab2-337921f3e798 · outbound

This paper cites A framework for be- havioural cloning.

Learning to Drive from a World Model A framework for be- havioural cloning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.737495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 402f34da-b945-45e6-888c-98ccb843f124 · outbound

This paper cites Video pretraining (vpt): Learning to act by watching unlabeled online videos.

Learning to Drive from a World Model Video pretraining (vpt): Learning to act by watching unlabeled online videos

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.720256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f2e01577-e2ee-4313-ae7d-b5fa2d6bb166 · outbound

This paper cites Navigation world models, 2024.

Learning to Drive from a World Model Navigation world models, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.689069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b940e35c-d5af-4647-9626-f644f2ad6ca0 · outbound

This paper cites End to End Learning for Self-Driving Cars.

Learning to Drive from a World Model End to End Learning for Self-Driving Cars

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.484310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dd67067-1607-4db3-9e2d-7731ddcaa432 · outbound

This paper cites Multimodal trajectory predictions for autonomous driving using deep convolutional networks.

Learning to Drive from a World Model Multimodal trajectory predictions for autonomous driving using deep convolutional networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.620293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 557bcff5-196d-4d5a-b577-3bc96cba6f56 · outbound

This paper cites CARLA: An open urban driving simulator.

Learning to Drive from a World Model CARLA: An open urban driving simulator

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.494754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e453a9ed-9543-4d5b-8482-2447b7fc20ef · outbound

This paper cites Impala: Scalable dis- tributed deep-rl with importance weighted actor-learner ar- chitectures.

Learning to Drive from a World Model Impala: Scalable dis- tributed deep-rl with importance weighted actor-learner ar- chitectures

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.593346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e339d53f-9616-454a-84c3-dc6d11284cd8 · outbound

This paper cites Scaling rec- tified flow transformers for high-resolution image synthesis.

Learning to Drive from a World Model Scaling rec- tified flow transformers for high-resolution image synthesis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.451317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 271fb28c-bc19-402b-923e-4d3f4002c48b · outbound

This paper cites Shortcut learning in deep neural networks.

Learning to Drive from a World Model Shortcut learning in deep neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.355189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b72d3d14-367b-4668-83d6-16eef4ff0cf8 · outbound

This paper cites Recurrent world models facilitate policy evolution.

Learning to Drive from a World Model Recurrent world models facilitate policy evolution

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.337593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1b75b4b-5589-4375-8bfb-d59749aa0d83 · outbound

This paper cites Gaia-1: A generative world model for au- tonomous driving, 2023.

Learning to Drive from a World Model Gaia-1: A generative world model for au- tonomous driving, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.152472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45a54bd9-1ff0-4757-8a41-7397065baa8f · outbound

This paper cites V ehicle dynamics.

Learning to Drive from a World Model V ehicle dynamics

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.136814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9dda75b9-27da-4127-8a70-34fac2351c9c · outbound

This paper cites Learning to drive in a day.

Learning to Drive from a World Model Learning to drive in a day

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.060697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e011bba1-d74d-449e-9bb6-6bb50a8d01e9 · outbound

This paper cites Mourikis.

Learning to Drive from a World Model Mourikis

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.018341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d294a739-1744-4549-969a-6869c7b3d58a · outbound

This paper cites Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning.

Learning to Drive from a World Model Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:46.000622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82a274f6-786e-4917-847d-b8e3c408e2ae · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Learning to Drive from a World Model Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.643927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30461292-6cad-4d77-a17b-5fced782c37b · outbound

This paper cites Mourikis and Stergios I.

Learning to Drive from a World Model Mourikis and Stergios I

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.734423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1177c3c2-a285-4723-a027-76b7ad898eec · outbound

This paper cites Estimating the mean and variance of the target probability distribution.

Learning to Drive from a World Model Estimating the mean and variance of the target probability distribution

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.717813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a355c36d-6623-4fe6-aa35-0301693e4521 · outbound

This paper cites Scalable diffusion models with transformers.

Learning to Drive from a World Model Scalable diffusion models with transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.681895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e86380ac-933c-4266-94c7-6806fb7d5de9 · outbound

This paper cites Sim-to-real transfer of robotic con- trol with dynamics randomization.

Learning to Drive from a World Model Sim-to-real transfer of robotic con- trol with dynamics randomization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.650487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 44a9aec2-ce43-47c8-b513-ad51901e63d4 · outbound

This paper cites Language models are unsu- pervised multitask learners.

Learning to Drive from a World Model Language models are unsu- pervised multitask learners

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.778106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f52eb31c-79e5-43ba-abef-6cb3a030892d · outbound

This paper cites Gaussian dropout as an information bottleneck layer.

Learning to Drive from a World Model Gaussian dropout as an information bottleneck layer

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.506650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5607b1ff-a508-4f81-bf71-7279fa2140bc · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Learning to Drive from a World Model High-resolution image synthesis with latent diffusion models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.788708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ee3ef2d-3c6c-4fc2-b832-535d0a1658db · outbound

This paper cites CAD2RL: real single- image flight without a single real image.

Learning to Drive from a World Model CAD2RL: real single- image flight without a single real image

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.480293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 641caa82-c7fa-43fd-9406-20a2776c7f3b · outbound

This paper cites Learning a driving simulator,.

Learning to Drive from a World Model Learning a driving simulator,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.799268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b72fcd4c-7b49-4a4c-8a13-a0c53006fd34 · outbound

This paper cites A commute in data: The comma2k19 dataset, 2018.

Learning to Drive from a World Model A commute in data: The comma2k19 dataset, 2018

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.399675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a21fa219-0d74-4677-a712-833a82282ecc · outbound

This paper cites View morphing.

Learning to Drive from a World Model View morphing

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.382696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a4a4d516-093f-4e3d-b5db-3f7ea873fcb9 · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

Learning to Drive from a World Model Domain randomization for transferring deep neural networks from simulation to the real world

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.240390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a7692a1d-39b2-4e7c-b327-08c32c0de20f · outbound

This paper cites Diffusion models are real-time game engines.

Learning to Drive from a World Model Diffusion models are real-time game engines

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.161330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b908ddd3-606d-4d35-8367-83e5aca93eb0 · outbound

This paper cites Fastvit: A fast hybrid vision transformer using structural reparameterization.

Learning to Drive from a World Model Fastvit: A fast hybrid vision transformer using structural reparameterization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.146487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f76d79fd-c668-4d09-aaff-c8d0205ea5ec · outbound

This paper cites Attention is all you need.

Learning to Drive from a World Model Attention is all you need

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.130101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 477d5be4-4199-45e7-b272-f946f671b386 · outbound

This paper cites Understanding and improving layer normaliza- tion.

Learning to Drive from a World Model Understanding and improving layer normaliza- tion

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T06:08:45.002247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78a21ca1-98c6-4fe4-833f-ec4fc91ff12b · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Learning to Drive from a World Model The unreasonable effectiveness of deep features as a perceptual metric

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T06:08:44.899109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b41bb051-1809-4dfd-8ba1-39cf1c8611e8 · inbound

ReSim: Reliable World Simulation for Autonomous Driving cites this paper.

ReSim: Reliable World Simulation for Autonomous Driving Learning to Drive from a World Model

Reference 121

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:32:16.633622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9092d167-fe71-4a4e-933f-3008efa8abf9 · inbound

Interpretability and Generalization Bounds for Learning Spatial Physics cites this paper.

Interpretability and Generalization Bounds for Learning Spatial Physics Learning to Drive from a World Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T19:52:08.571333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.