Paper Citation Record · LEDGER

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving

As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2608.10386.

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

pith.paper-citation-record.v1
2608.10386 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:51:43.997955Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact1
verified fuzzy12
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f2adf06-b1dc-453d-90a5-2dbb927f861c · outbound

This paper cites Preparing a nation for autonomous vehicles: oppor- tunities, barriers and policy recommendations.Transportation Research Part A: Policy and Practice, 77:167–181, 2015.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Preparing a nation for autonomous vehicles: oppor- tunities, barriers and policy recommendations.Transportation Research Part A: Policy and Practice, 77:167–181, 2015

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.495045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 90b2f4b5-3eff-4d64-be58-bcb22c643603 · outbound

This paper cites Virtual testing of automated driving systems: A survey on validation methods.IEEE Access, 10:40149–40169, 2022.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Virtual testing of automated driving systems: A survey on validation methods.IEEE Access, 10:40149–40169, 2022

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.470304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7763fc11-43ec-4a27-b593-79e2a0cd67d9 · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Deep reinforcement learning for autonomous driving: A survey

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T00:51:43.902014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3866bd1-08dc-4e46-896d-8921cd8bb208 · outbound

This paper cites an unresolved cited work.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:51:44.440766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c445162c-71de-403a-a5f4-2d8e2dd98ad5 · outbound

This paper cites A Survey of World Models for Autonomous Driving.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving A Survey of World Models for Autonomous Driving

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T00:51:43.912861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad5d9268-3887-4219-afbf-c268827597f9 · outbound

This paper cites A review of motion planning techniques for automated vehicles.IEEE Transactions on Intelligent Transportation Systems, 17(4):1135–1145, 2016.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving A review of motion planning techniques for automated vehicles.IEEE Transactions on Intelligent Transportation Systems, 17(4):1135–1145, 2016

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.423135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1045b66-7549-40aa-bb39-00d6fa5889cd · outbound

This paper cites Planning and decision-making for autonomous vehicles.Annual Review of Control, Robotics, and Autonomous Systems, 1: 187–210, 2018.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Planning and decision-making for autonomous vehicles.Annual Review of Control, Robotics, and Autonomous Systems, 1: 187–210, 2018

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.402621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1fc72c53-2e08-4204-9fca-7aa4ed6910de · outbound

This paper cites Predictive active steering control for autonomous vehicle systems.IEEE Transactions on Control Systems Technology, 15(3):566–580, 2007.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Predictive active steering control for autonomous vehicle systems.IEEE Transactions on Control Systems Technology, 15(3):566–580, 2007

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.383648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 725417a8-c345-48dd-af70-c8dbe7afa8e8 · outbound

This paper cites A survey on imitation learning techniques for end-to-end autonomous vehicles.IEEE Transactions on Intelligent Transportation Systems, 23(9):14128–14147, 2022.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving A survey on imitation learning techniques for end-to-end autonomous vehicles.IEEE Transactions on Intelligent Transportation Systems, 23(9):14128–14147, 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.362434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f997ac2b-a689-4013-9032-debe5912fe3d · outbound

This paper cites End-to-end driving via conditional imitation learning.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving End-to-end driving via conditional imitation learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.346179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0341e64c-cc2e-465b-897d-4018182258d6 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Proximal Policy Optimization Algorithms

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T00:51:43.945373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca449485-8d8d-4087-be9f-ec125c8d590a · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.329334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 90c97f47-741e-4bb0-9d37-a2fbe2f11ef1 · outbound

This paper cites When to trust your model: Model-based policy optimization.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving When to trust your model: Model-based policy optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T00:51:43.955391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92012f6e-7caf-4a9e-a427-9022fc0b7bb7 · outbound

This paper cites Learning latent dynamics for planning from pixels.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Learning latent dynamics for planning from pixels

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.298474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 681afd05-ff94-436d-a7dc-7dcf4afb2519 · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Dream to Control: Learning Behaviors by Latent Imagination

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T00:51:43.965965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1b95e7f-8668-471f-9141-06c311cd6a7f · outbound

This paper cites Mastering diverse control tasks through world models.Nature, 640(8059):647–653, 2025.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Mastering diverse control tasks through world models.Nature, 640(8059):647–653, 2025

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.279815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7c66ac9-dee1-4f50-9bb5-adedfca7af68 · outbound

This paper cites an unresolved cited work.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:51:44.257881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 91d65c51-0f51-44ae-b69e-ef8e63ccace0 · outbound

This paper cites Raw2drive: Reinforcement learning with aligned world models for end-to-end autonomous driving (in carla v2).arXiv preprint arXiv:2505.16394, 2025.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Raw2drive: Reinforcement learning with aligned world models for end-to-end autonomous driving (in carla v2).arXiv preprint arXiv:2505.16394, 2025

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T00:51:43.980667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eef7fdc5-3f66-4db4-b463-695e85b0756c · outbound

This paper cites Reinforcement Learning with Trajectory Feedback.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Reinforcement Learning with Trajectory Feedback

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-12T00:51:44.049063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 59660f36-5aee-4fe9-96e2-b52b20fa0fa9 · outbound

This paper cites Planning and acting in partially observable stochastic domains.Artificial Intelligence, 101(1-2):99–134, 1998.

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Planning and acting in partially observable stochastic domains.Artificial Intelligence, 101(1-2):99–134, 1998

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.240955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 79998f78-6f0f-4229-8e19-b68f27bbcc2f · outbound

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

Dreamer-SAC: Off-Policy Learning in Latent World Models for Sample-Efficient Autonomous Driving Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:51:44.224014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.