Paper Citation Record · LEDGER

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion

As of 21 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2505.03178.

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

pith.paper-citation-record.v1
2505.03178 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:02:56.447339Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:24:16.326086Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T15:24:17.323714Z

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy17
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4665de83-1e3f-45a2-a77d-937479fd0e76 · outbound

This paper cites Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.869537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be51c908-3be2-4633-bb34-eb3b735dafae · outbound

This paper cites Curse of rarity for autonomous vehicles,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Curse of rarity for autonomous vehicles,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.337282Z

Source-reported events for the cited work

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Observation 8210fa0d-bda7-4bec-97be-2b5b952db250 · outbound

This paper cites Trafficsim: Learning to simulate realistic multi-agent behaviors,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Trafficsim: Learning to simulate realistic multi-agent behaviors,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.850885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e6a95db9-a794-49db-92f7-70cbe4623d40 · outbound

This paper cites Learn- ing naturalistic driving environment with statistical realism,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Learn- ing naturalistic driving environment with statistical realism,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.838593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 719ed36d-bc6d-46be-8c6c-9d6e22d2f495 · outbound

This paper cites Adaptive stress testing for autonomous vehicles,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Adaptive stress testing for autonomous vehicles,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.348781Z

Source-reported events for the cited work

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Observation 24125d44-f796-4f93-912e-6cafe4b61c33 · outbound

This paper cites Training adversarial agents to exploit weaknesses in deep control policies,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Training adversarial agents to exploit weaknesses in deep control policies,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.818917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3eb20d50-8a03-4cb1-a2b4-54da3ae5a870 · outbound

This paper cites Generat- ing useful accident-prone driving scenarios via a learned traffic prior,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Generat- ing useful accident-prone driving scenarios via a learned traffic prior,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.356679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c25996ba-b25e-4bbb-bc27-5751ce78cb5a · outbound

This paper cites Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.360035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27e62bda-5b60-4734-bda2-73b6f8a8d942 · outbound

This paper cites Safe-sim: Safety-critical closed-loop traffic simulation with diffusion- controllable adversaries,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Safe-sim: Safety-critical closed-loop traffic simulation with diffusion- controllable adversaries,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.792707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d8c8b92d-9a35-47d6-979c-64b69079a6a7 · outbound

This paper cites Advdiffuser: Generating adversarial safety-critical driving scenarios via guided diffusion,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Advdiffuser: Generating adversarial safety-critical driving scenarios via guided diffusion,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.779325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 631a4431-fbd5-46cc-9554-74672f72ba12 · outbound

This paper cites TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.369771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05530283-1e95-40df-b5f0-a71dbebbbbe9 · outbound

This paper cites Madiff: Offline multi-agent learning with diffusion models,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Madiff: Offline multi-agent learning with diffusion models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.766261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 22f47527-8600-4b94-8e54-4c66df35a015 · outbound

This paper cites Can post encroach- ment time substitute intersection characteristics in crash prediction models?.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Can post encroach- ment time substitute intersection characteristics in crash prediction models?

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.755629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d559abb-7ebf-462f-b228-7e79ad7dcce1 · outbound

This paper cites Congested traffic states in empirical observations and microscopic simulations,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Congested traffic states in empirical observations and microscopic simulations,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.380229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 299eab36-5479-4182-880c-4e9bd5719e32 · outbound

This paper cites General lane-changing model mobil for car-following models,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion General lane-changing model mobil for car-following models,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.383439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0522bc67-9966-4a1e-9749-ba8c0ab7fab9 · outbound

This paper cites Bits: Bi-level imitation for traffic simulation,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Bits: Bi-level imitation for traffic simulation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.729073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d5c3e5a9-35f6-48b2-8ae8-f96ac77f51a8 · outbound

This paper cites Trafficbots: Towards world models for autonomous driving simulation and motion prediction,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Trafficbots: Towards world models for autonomous driving simulation and motion prediction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.718078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b0da854c-6d08-4085-b12c-43e485f2e9a0 · outbound

This paper cites Smart: Scalable multi-agent real-time motion generation via next-token prediction,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Smart: Scalable multi-agent real-time motion generation via next-token prediction,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.705285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d3855579-adcd-4bc4-8633-4406ff9aea95 · outbound

This paper cites Dense reinforcement learning for safety validation of autonomous vehicles,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Dense reinforcement learning for safety validation of autonomous vehicles,

Reference 19

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Source-reported events for the cited work

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Observation 3272f825-c66c-4ff5-ab4c-33af4ee35020 · outbound

This paper cites King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.399682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ad98ed6-7a5a-4a0b-960b-89959e83d2ed · outbound

This paper cites Cat: Closed-loop adversarial training for safe end-to-end driving,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Cat: Closed-loop adversarial training for safe end-to-end driving,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.403302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c2f13ce-5768-4420-9238-d9bd67c7a5c7 · outbound

This paper cites Denoising diffusion probabilistic models,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Denoising diffusion probabilistic models,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.407415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 627ee878-1c18-4c25-9bb4-1e06363af2a1 · outbound

This paper cites Planning with diffusion for flexible behavior synthesis,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Planning with diffusion for flexible behavior synthesis,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.663065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 299b8c71-ff66-4c85-840e-cdf8ef58d5a9 · outbound

This paper cites Is Conditional Generative Modeling all you need for Decision-Making?.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Is Conditional Generative Modeling all you need for Decision-Making?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.414279Z

Source-reported events for the cited work

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Observation c8bafe2d-7874-4f31-861a-86365aca6f7a · outbound

This paper cites Guided conditional diffusion for controllable traffic simulation,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Guided conditional diffusion for controllable traffic simulation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.650651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bfb214f0-e724-4260-9dbf-af100448a890 · outbound

This paper cites Language-guided traffic simulation via scene-level diffu- sion,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Language-guided traffic simulation via scene-level diffu- sion,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.421015Z

Source-reported events for the cited work

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Observation 84a04c6d-40f0-4f3e-822f-9402abd8197c · outbound

This paper cites Versatile scene-consistent traffic scenario generation as optimization with diffusion,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Versatile scene-consistent traffic scenario generation as optimization with diffusion,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.424628Z

Source-reported events for the cited work

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Observation da2cbe95-47ee-4eeb-87ae-fda96e995aa0 · outbound

This paper cites Scenediffuser: Efficient and controllable driving simulation initialization and rollout,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Scenediffuser: Efficient and controllable driving simulation initialization and rollout,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.629868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e73e6cfc-917b-4734-8952-7f24ba99b3a7 · outbound

This paper cites Classifier-Free Diffusion Guidance.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Classifier-Free Diffusion Guidance

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.430047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9da30060-bd35-458c-8a5b-b6cb421d11ec · outbound

This paper cites A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.617422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 729e6ac9-7f61-4e45-9a82-a39907702a28 · outbound

This paper cites The round dataset: A drone dataset of road user trajectories at roundabouts in germany,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion The round dataset: A drone dataset of road user trajectories at roundabouts in germany,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.605850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 41df85ed-5e62-48cd-88bd-245fcc91d7d2 · outbound

This paper cites Trajeglish: Traffic Modeling as Next-Token Prediction.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Trajeglish: Traffic Modeling as Next-Token Prediction

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.440277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69b78213-5c1e-4f5d-b981-99fe6b62d219 · outbound

This paper cites Motion transformer with global intention localization and local movement refinement,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Motion transformer with global intention localization and local movement refinement,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T00:02:56.443995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ed1be3e-2c1c-491d-98f6-4bf2790930e3 · outbound

This paper cites Post encroachment time threshold identification for right-turn related crashes at unsignalized intersections on intercity highways under mixed traffic,.

RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion Post encroachment time threshold identification for right-turn related crashes at unsignalized intersections on intercity highways under mixed traffic,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:02:56.587167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 74cd981d-2e84-4707-9750-b8a7c05bb5df · inbound

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

Generative AI for Testing of Autonomous Driving Systems: A Survey RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion

Reference 226

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:24:17.328513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.