Paper Citation Record · LEDGER

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment

As of 6 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 4 inbound Pith citation observations for arXiv:2509.20102.

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

pith.paper-citation-record.v1
2509.20102 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T14:13:18.951079Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:37:03.489316Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T03:06:29.991156Z

Reference resolution

53 of 53 outbound references displayed

verified exact11
verified fuzzy40
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96b68671-c7a5-4b99-869f-e7098f405370 · outbound

This paper cites write newline.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment write newline

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.313288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation def83f11-7578-417a-8d9f-ce686c4b2ff8 · outbound

This paper cites Git Re-Basin: Merging Models modulo Permutation Symmetries.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Git Re-Basin: Merging Models modulo Permutation Symmetries

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T14:16:27.855345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0c21d985-49eb-4a28-b5c2-e8bbf083077d · outbound

This paper cites An end-to-end curriculum learning approach for autonomous driving scenarios.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment An end-to-end curriculum learning approach for autonomous driving scenarios

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.315624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 096cb082-a1b7-425f-8831-765cee55cd92 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Constitutional AI: Harmlessness from AI Feedback

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-18T14:16:27.850639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4d5e9b7e-b9bc-40d6-be25-89af16a76ded · outbound

This paper cites Advdo: Realistic adversarial attacks for trajectory prediction.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Advdo: Realistic adversarial attacks for trajectory prediction

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.320224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d515af7c-3b2a-4391-8e87-23af76559141 · outbound

This paper cites Reinforcement learning with human feedback for realistic traffic simulation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Reinforcement learning with human feedback for realistic traffic simulation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.318014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8b1dff08-ee9a-49ad-882b-c4cc72270f38 · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Safe-sim: Safety-critical closed-loop traffic simulation with diffusion-controllable adversaries

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.322451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fc4c54ac-658d-497d-892f-12368b2a34f2 · outbound

This paper cites FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:16:27.889600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8b5c9039-7b4d-4add-80d1-710a9fb27ee9 · outbound

This paper cites Rift: Closed-loop rl fine-tuning for realistic and controllable traffic simulation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Rift: Closed-loop rl fine-tuning for realistic and controllable traffic simulation

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:16:27.877397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c42e6b52-7812-4170-87f6-29c49d1e5ed8 · outbound

This paper cites Multi-objective optimization.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Multi-objective optimization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.288497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ab5be468-afd4-4c70-9d84-b5725a323113 · outbound

This paper cites Learning to collide: An adaptive safety-critical scenarios generating method.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Learning to collide: An adaptive safety-critical scenarios generating method

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.276781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c11763fe-be53-4a6c-966d-b0247cfe37c2 · outbound

This paper cites A survey on safety-critical driving scenario generation—a methodological perspective.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment A survey on safety-critical driving scenario generation—a methodological perspective

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.267222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 357d0ebf-bdab-420e-828a-ea6427881cbf · outbound

This paper cites Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.232353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6f6ae13e-4f45-4bac-8f15-8ed3bb8628e0 · outbound

This paper cites Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.254471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1d00c788-461e-45b9-815b-faaeee64b89f · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Dense reinforcement learning for safety validation of autonomous vehicles

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.247024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 25474396-0864-43f0-822c-da59bd6bf09f · outbound

This paper cites Linear mode connectivity and the lottery ticket hypothesis.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Linear mode connectivity and the lottery ticket hypothesis

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.242004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 335dbcfd-3fde-4b09-b7fc-528a046b0121 · outbound

This paper cites Addressing function approximation error in actor-critic methods.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Addressing function approximation error in actor-critic methods

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.279572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8044280c-04d0-4c01-852b-43b4e3118188 · outbound

This paper cites Densetnt: End-to-end trajectory prediction from dense goal sets.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Densetnt: End-to-end trajectory prediction from dense goal sets

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.227749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e7c3f884-3d5d-434b-9319-be65a634b306 · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.286090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d1a9eeff-4119-4adb-9ab7-ba5872437a4d · outbound

This paper cites Editing Models with Task Arithmetic.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Editing Models with Task Arithmetic

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-18T14:16:27.864655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 16b5cfc4-7d22-43a2-8ad1-db5d19f394e0 · outbound

This paper cites Deep learning without poor local minima.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Deep learning without poor local minima

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.310501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c2c05b3d-2c6e-4ac6-963d-41d57077597a · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Training adversarial agents to exploit weaknesses in deep control policies

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.308204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c58a82aa-f9b1-461c-99f0-dc4cfe5b0505 · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.303294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 01b8332f-e789-46b6-9b42-4f6eb318d573 · outbound

This paper cites Curse of rarity for autonomous vehicles.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Curse of rarity for autonomous vehicles

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.300824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf08f3d4-cd6d-489e-b6bf-02136cbba05d · outbound

This paper cites PEO: Improving Bi-Factorial Preference Alignment with Post-Training Policy Extrapolation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment PEO: Improving Bi-Factorial Preference Alignment with Post-Training Policy Extrapolation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:16:27.893560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b0b1e676-5486-4428-a14e-29bdc19edebb · outbound

This paper cites Improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.298086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 05f1e37f-feed-47e8-a188-31fddd6c8657 · outbound

This paper cites Bayesian fault injection safety testing for highly automated vehicles with uncertainty.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Bayesian fault injection safety testing for highly automated vehicles with uncertainty

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.295709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a889edac-d8cc-4f03-a555-2cd34d187f38 · outbound

This paper cites Llm-attacker: Enhancing closed-loop adversarial scenario generation for autonomous driving with large language models.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Llm-attacker: Enhancing closed-loop adversarial scenario generation for autonomous driving with large language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.293190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ff35c4a7-a63a-4448-9b18-674bf99cfe5f · outbound

This paper cites Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:16:27.881808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 35ceeed8-ad39-4dd7-adc4-4c4ec2bd5524 · outbound

This paper cites Exploring the roles of large language models in reshaping transportation systems: A survey, framework, and roadmap.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Exploring the roles of large language models in reshaping transportation systems: A survey, framework, and roadmap

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.290875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2241e421-f5a5-4c84-a76d-9c091955c753 · outbound

This paper cites Training language models to follow instructions with human feedback.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Training language models to follow instructions with human feedback

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.283429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 76e709db-85fa-4415-8668-cb68421e97eb · outbound

This paper cites Aed: Automatic discovery of effective and diverse vulnerabilities for autonomous driving policy with large language models.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Aed: Automatic discovery of effective and diverse vulnerabilities for autonomous driving policy with large language models

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:16:27.885795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 03f6a91a-88f6-42bf-8037-61de7043c64b · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Direct preference optimization: Your language model is secretly a reward model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.273644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f418556b-74b4-4c23-9146-abd7415a1a66 · outbound

This paper cites Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.270741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b1c459b1-2c32-45d8-a01d-77a9adc57eb2 · outbound

This paper cites Goose: Goal-conditioned reinforcement learning for safety-critical scenario generation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Goose: Goal-conditioned reinforcement learning for safety-critical scenario generation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.264170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7958ea57-5e67-45aa-8bb8-ecbdc3439eaa · outbound

This paper cites Adversarial and reactive traffic agents for realistic driving simulation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Adversarial and reactive traffic agents for realistic driving simulation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.260614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 02b2cbf0-1e42-4cbc-88db-222aff74edb6 · outbound

This paper cites Generating useful accident-prone driving scenarios via a learned traffic prior.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Generating useful accident-prone driving scenarios via a learned traffic prior

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.257709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f13a1cf6-f82d-49ef-a3b4-0b10872089b3 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-18T14:16:27.873340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3b53b18f-0bc8-470a-bbf9-8219bff3a079 · outbound

This paper cites Decoding-time language model alignment with multiple objectives.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Decoding-time language model alignment with multiple objectives

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.252041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 03d3adde-b8ad-4801-84f2-88aec287624b · outbound

This paper cites Seal: Towards safe autonomous driving via skill-enabled adversary learning for closed-loop scenario generation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Seal: Towards safe autonomous driving via skill-enabled adversary learning for closed-loop scenario generation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.249458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4a4d8d4c-d679-4a56-9af5-87acd104daa7 · outbound

This paper cites Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-18T14:16:27.868996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9dbebecd-1f25-4912-aea5-1798453a85dc · outbound

This paper cites Advsim: Generating safety-critical scenarios for self-driving vehicles.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Advsim: Generating safety-critical scenarios for self-driving vehicles

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.244751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a562d042-76b7-413f-8d9b-0211b5c91d2b · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.237436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c7129aca-84b8-46e7-91ff-369caff7cbdc · outbound

This paper cites Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:16:27.860144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 13ba1e73-1d82-4481-802a-c89fb16f6873 · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Advdiffuser: Generating adversarial safety-critical driving scenarios via guided diffusion

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.234990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 81d0dab7-167b-4dc5-8aa6-813ffcbfff90 · outbound

This paper cites Safebench: A benchmarking platform for safety evaluation of autonomous vehicles.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Safebench: A benchmarking platform for safety evaluation of autonomous vehicles

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.230003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e7c2186f-204c-4dff-8c34-86ae97ce7760 · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.225173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7bb4cd7a-608a-4e77-9f7a-65a2043589fd · outbound

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

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Cat: Closed-loop adversarial training for safe end-to-end driving

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.222414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c6bd32d4-1a37-4e29-bfa1-02ced2f85a9b · outbound

This paper cites On adversarial robustness of trajectory prediction for autonomous vehicles.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment On adversarial robustness of trajectory prediction for autonomous vehicles

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.219846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1df07e83-9706-43e6-aeef-1742d58fd650 · outbound

This paper cites Language-guided traffic simulation via scene-level diffusion.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Language-guided traffic simulation via scene-level diffusion

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.239779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a9a0b49-45e2-423d-9ff5-0bc9e31bfdf4 · outbound

This paper cites @esa (Ref.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment @esa (Ref

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:16:28.305921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9f149dd8-9b2f-4656-94f8-c4ad5481fc60 · outbound

This paper cites an unresolved cited work.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-05-18T14:16:28.217463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 08e15cd9-f752-430e-b373-41714edb1ff9 · outbound

This paper cites Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation.

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:16:27.897502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

Observation c2ade741-bd71-42f6-9a97-e89e143b69c6 · inbound

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving cites this paper.

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving Steerable Adversarial Scenario Generation through Test-Time Preference Alignment

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T18:37:03.489316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97f9eb0a-d90c-4e22-90f2-15c057003030 · inbound

EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents cites this paper.

EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents Steerable Adversarial Scenario Generation through Test-Time Preference Alignment

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-02T03:06:29.992845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 32f5e6f3-c04f-4215-93cd-a861f215ec99 · inbound

CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation cites this paper.

CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation Steerable Adversarial Scenario Generation through Test-Time Preference Alignment

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-11T19:04:23.977767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a92c527-ae9e-4254-a376-df972b415a06 · inbound

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning cites this paper.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Steerable Adversarial Scenario Generation through Test-Time Preference Alignment

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-14T10:22:08.922396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.