TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation | Paper Citation Record

Observation 4f9ecc9c-c2be-4f1f-b9e2-d831ef94d61e · outbound

This paper cites Testing autonomous cars for feature interaction failures using many-objective search.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Testing autonomous cars for feature interaction failures using many-objective search

Reference 1

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Observation a97841ba-4c4e-401d-a8a4-fc0ba8286f19 · outbound

This paper cites Generating adversarial driving scenarios in high- fidelity simulators.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Generating adversarial driving scenarios in high- fidelity simulators

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.630545Z

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Observation 0411c624-9b39-4bc4-8440-c5776645c851 · outbound

This paper cites Generating traffic scenarios via in- context learning to learn better motion planner.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Generating traffic scenarios via in- context learning to learn better motion planner

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.665599Z

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Observation dbc277ce-fd6b-48aa-a976-f3abf8ef1af0 · outbound

This paper cites System card: Claude opus 4 & claude sonnet 4.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation System card: Claude opus 4 & claude sonnet 4

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.603691Z

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Observation 28d70537-d145-4286-8605-f3acfa7592fa · outbound

This paper cites Ontology based scene creation for the development of automated ve- hicles.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Ontology based scene creation for the development of automated ve- hicles

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.643705Z

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Observation 251c2a7b-9a06-4046-93a4-bf49a8d6a48b · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.677745Z

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Observation 4550c19f-aecd-4178-a79a-0b0aef6a0baa · outbound

This paper cites Behavexplor: Behavior diversity guided testing for autonomous driving systems.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Behavexplor: Behavior diversity guided testing for autonomous driving systems

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.645254Z

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Observation 35d5ed99-bce4-45a8-8261-b6e7d2119016 · outbound

This paper cites Sledge: Synthesizing driving environments with generative models and rule-based traffic.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Sledge: Synthesizing driving environments with generative models and rule-based traffic

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.612382Z

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Observation 10079056-b36b-43f4-9d03-9bec5aac9e22 · outbound

This paper cites Deepseek-v3 technical report, 2024.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Deepseek-v3 technical report, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.625484Z

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Observation edc8fff0-16d2-48e0-a4f2-f940d683959a · outbound

This paper cites TARGET: traffic rule-based test generation for autonomous driving via validated llm-guided knowledge extraction.IEEE Trans.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation TARGET: traffic rule-based test generation for autonomous driving via validated llm-guided knowledge extraction.IEEE Trans

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.674072Z

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Observation e29a0432-c7e1-42c7-be35-42d762fcb7a6 · outbound

This paper cites Meta-sim2: Unsupervised learning of scene structure for synthetic data generation.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Meta-sim2: Unsupervised learning of scene structure for synthetic data generation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.684209Z

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Observation 4dc1a23b-ecdb-487c-8c95-492ffe058e18 · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Learning to collide: An adaptive safety-critical scenarios gen- erating method

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.623951Z

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Observation 2a642654-2ec6-49dd-a224-f9ca7d7b6a87 · outbound

This paper cites Cmts: A condi- tional multiple trajectory synthesizer for generating safety- critical driving scenarios.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Cmts: A condi- tional multiple trajectory synthesizer for generating safety- critical driving scenarios

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.614044Z

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Observation f46c4bf0-386c-48ea-a6c6-c186d113ef3c · outbound

This paper cites CARLA: An open urban driving simulator.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation CARLA: An open urban driving simulator

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.640491Z

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Observation e3db6655-70a0-450a-8618-e6751619f2e4 · outbound

This paper cites ScenicNL: Generating Probabilistic Scenario Programs from Natural Language.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation ScenicNL: Generating Probabilistic Scenario Programs from Natural Language

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.405142Z

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Observation fc491421-4f2b-4ffd-b2f5-a600d5407be8 · outbound

This paper cites Fremont, Edward Kim, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Fremont, Edward Kim, Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Alberto L

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.689335Z

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Observation 419045ac-8003-48f8-8fc8-72747e53a865 · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Addressing function approximation error in actor-critic methods

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.594875Z

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Observation bbd033e8-46c4-44e0-a929-721d4a24991b · outbound

This paper cites Generating effective test cases for self-driving cars from police reports.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Generating effective test cases for self-driving cars from police reports

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.662219Z

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Observation 922c820f-762f-497b-bf1a-582fa7768c9a · outbound

This paper cites Sovar: Build generalizable scenarios from accident reports for autonomous driving testing.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Sovar: Build generalizable scenarios from accident reports for autonomous driving testing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.600167Z

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Observation b4296619-827d-4547-b9d0-8af23ac6f41f · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.655658Z

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Observation d609f7fc-2379-4c94-acb0-7d44fb7deb7a · outbound

This paper cites Liao, Esin Durmus, Alex Tamkin, and Deep Ganguli.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Liao, Esin Durmus, Alex Tamkin, and Deep Ganguli

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.591464Z

Source-reported events for the cited work

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Observation 966ddd5b-cd57-42a6-b60f-db626a1df38b · outbound

This paper cites Meta-sim: Learning to generate synthetic datasets.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Meta-sim: Learning to generate synthetic datasets

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.618853Z

Source-reported events for the cited work

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Observation 7d85a595-6094-45cb-be19-a1e024a53db6 · outbound

This paper cites Drivefuzz: Discovering autonomous driving bugs through driving quality- guided fuzzing.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Drivefuzz: Discovering autonomous driving bugs through driving quality- guided fuzzing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.637200Z

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Observation 73d8ac4a-dc76-48dc-8517-e72493a72b2c · outbound

This paper cites an unresolved cited work.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-07-09T21:06:35.607101Z

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Observation 6e825bb0-b42a-48e3-b3ad-b04b0e0ee2b2 · outbound

This paper cites Scenario factory: Creating safety-critical traffic scenarios for automated vehicles.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Scenario factory: Creating safety-critical traffic scenarios for automated vehicles

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.609089Z

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Observation 9cf0a097-f29e-4aae-a14a-1406b2f60b50 · outbound

This paper cites Kochenderfer, Ole J.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Kochenderfer, Ole J

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.615674Z

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Observation 060a8660-893e-46e8-96ea-1e5aadcfd64d · outbound

This paper cites Av-fuzzer: Finding safety violations in autonomous driving systems.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Av-fuzzer: Finding safety violations in autonomous driving systems

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.669016Z

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Observation 42b3f876-2f43-4112-a497-325dd6f00ad5 · outbound

This paper cites Flame: Factuality- aware alignment for large language models.Advances in Neural Information Processing Systems, 37:115588–115614,.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Flame: Factuality- aware alignment for large language models.Advances in Neural Information Processing Systems, 37:115588–115614,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.635552Z

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Observation 2c170a88-f2df-487e-9947-d5fd46535b55 · outbound

This paper cites Targeting requirements violations of autonomous driving systems by dynamic evolutionary search.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Targeting requirements violations of autonomous driving systems by dynamic evolutionary search

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.589807Z

Source-reported events for the cited work

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Observation f61c970b-402c-4b74-8940-34f4bbaf5f5e · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:24:32.407270Z

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Observation 1d12d608-4f6b-468c-96a1-9d41877250b0 · outbound

This paper cites Llama-3.2-3B-Instruct.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Llama-3.2-3B-Instruct

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.620620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 641c8bc0-5110-4973-8ee0-a462b9effbf3 · outbound

This paper cites Standing general order on crash reporting.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Standing general order on crash reporting

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.638911Z

Source-reported events for the cited work

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Observation 001a0602-5a52-423b-944e-53f7a22380bf · outbound

This paper cites GPT-4o System Card.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation GPT-4o System Card

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:24:32.402798Z

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Observation c2654619-3b3f-4c28-a1c7-3f76736f151d · outbound

This paper cites GPT-4.1 nano.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation GPT-4.1 nano

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.610710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 149509bd-483b-438e-b9ac-4291081441f7 · outbound

This paper cites GPT-5 system card.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation GPT-5 system card

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.617227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2e131837-d37c-40d1-bf71-ff26c6e9c722 · outbound

This paper cites Scenario diffusion: Controllable driving scenario gen- eration with diffusion.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Scenario diffusion: Controllable driving scenario gen- eration with diffusion

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.648617Z

Source-reported events for the cited work

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Observation ec389886-dbde-48e5-bbda-858696b4ad1c · outbound

This paper cites Qwen3-32b-fp8.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Qwen3-32b-fp8

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.680930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ab8b3379-1ef7-413d-8e9c-e9b5c47ee983 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.601920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 715e13f3-f40f-4e97-bc46-8e97caf83dca · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Generating useful accident-prone driving scenarios via a learned traffic prior

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.691010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bc59c1d9-b289-40d3-b46f-10b8b9d5865d · outbound

This paper cites Automated scenario generation for regression testing of autonomous vehicles.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Automated scenario generation for regression testing of autonomous vehicles

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.667355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 12903fe4-7883-492f-990d-bee7ce54c31a · outbound

This paper cites Scanlon, Kristofer D.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Scanlon, Kristofer D

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.632164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 32ceffb8-b932-4a7f-9e3d-061532ce48be · outbound

This paper cites Proximal Policy Optimization Algorithms.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Proximal Policy Optimization Algorithms

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:24:32.410687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 257b68c7-dc33-468d-8f4e-eaef8f7cff91 · outbound

This paper cites Role- play with large language models, 2023.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Role- play with large language models, 2023

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.652210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aeacb0e6-f946-41f9-9c9c-c4926b0e8fbe · outbound

This paper cites Talk2traffic: Interactive and editable traffic scenario generation for autonomous driving with multimodal large lan- guage model.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Talk2traffic: Interactive and editable traffic scenario generation for autonomous driving with multimodal large lan- guage model

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.646950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4a285737-aea7-4734-bd7d-af0fb48ddffb · outbound

This paper cites Lawbreaker: An approach for specifying traffic laws and fuzzing autonomous vehicles.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Lawbreaker: An approach for specifying traffic laws and fuzzing autonomous vehicles

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.598461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ba0fa7f0-df13-4369-9b2c-aef77351df41 · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Trafficsim: Learning to simulate realistic multi- agent behaviors

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.682530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd2bce6c-94e9-43d9-a05b-120b1b16db48 · outbound

This paper cites Language conditioned traffic gen- eration.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Language conditioned traffic gen- eration

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.642073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0d34e2c5-e264-4421-b150-b802f1fab79a · outbound

This paper cites Legend: A top-down approach to scenario generation of autonomous driving systems as- sisted by large language models.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Legend: A top-down approach to scenario generation of autonomous driving systems as- sisted by large language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.605439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c287f1cb-29c6-40e4-8607-f1cb3fb8dc9a · outbound

This paper cites Carla scenario run- ner.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Carla scenario run- ner

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.687675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fcb44294-daf5-4d6a-b3b7-747fd3a82547 · outbound

This paper cites sentence-transformers/all- mpnet-base-v2.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation sentence-transformers/all- mpnet-base-v2

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.657319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e459c23-f96f-45bd-bccd-8499b922df92 · outbound

This paper cites an unresolved cited work.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Unresolved cited work

Reference 51

Resolution: parse uncertain; raw_fallback, observed 2026-07-09T21:06:35.658864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 414e49ad-b1bd-4904-931b-37adfee7dc2a · outbound

This paper cites Generating critical test scenarios for autonomous driving systems via influential be- havior patterns.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Generating critical test scenarios for autonomous driving systems via influential be- havior patterns

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.593158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3466dae2-acb3-479c-9dcb-37adf99e3159 · outbound

This paper cites Multi- modal traffic scenario generation for autonomous driving system testing.Proc.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Multi- modal traffic scenario generation for autonomous driving system testing.Proc

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.675792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 949699a7-27a2-470c-9c4c-3cbf34fb63dd · outbound

This paper cites Automated generation of virtual driving scenarios from test drive data.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Automated generation of virtual driving scenarios from test drive data

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.650459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fd7e9a9-1b9f-4a98-9b40-85757e1a8a47 · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Advsim: Generating safety-critical scenarios for self-driving vehicles

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.670682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 481e1974-c88e-42d8-874c-c803cd606cf1 · outbound

This paper cites Self-instruct: Aligning language models with self-generated instructions.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Self-instruct: Aligning language models with self-generated instructions

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.660594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 877a844b-95ee-4777-9883-dfb205be656e · outbound

This paper cites Adversarial prefer- ence learning for robust LLM alignment.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Adversarial prefer- ence learning for robust LLM alignment

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.685971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 75db12aa-472a-4a8f-a74d-28d604286e60 · outbound

This paper cites CodecLM: Aligning Language Models with Tailored Synthetic Data.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.396708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0d48a7d-b282-4865-91b8-3c9dcf77f44e · outbound

This paper cites Chain-of- thought prompting elicits reasoning in large language mod- els.Advances in Neural Information Processing Systems, 35: 24824–24837, 2022.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Chain-of- thought prompting elicits reasoning in large language mod- els.Advances in Neural Information Processing Systems, 35: 24824–24837, 2022

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.596724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f8539935-d235-49f3-8270-c01c1b1d9e6f · outbound

This paper cites Selfcodealign: Self-alignment for code generation.Advances in Neural In- formation Processing Systems, 37:62787–62874, 2024.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Selfcodealign: Self-alignment for code generation.Advances in Neural In- formation Processing Systems, 37:62787–62874, 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.587938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 756dfdcb-23a2-4140-ad19-9153a58e697a · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Safebench: a benchmarking platform for safety evaluation of autonomous vehicles

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.622281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b124b88f-3d7c-48ef-a445-6a38c3500fa9 · outbound

This paper cites Wizardlm: Empowering large pre-trained language models to follow complex instructions.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Wizardlm: Empowering large pre-trained language models to follow complex instructions

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.633865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 17ccb924-be64-4463-af66-ee0b25d82fd7 · outbound

This paper cites Qwen3 Technical Report.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Qwen3 Technical Report

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-06-30T09:24:32.407567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e43a3310-7993-41c1-a1be-459f2ec4b6ff · outbound

This paper cites Surfelgan: Synthesizing realistic sensor data for autonomous driving.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Surfelgan: Synthesizing realistic sensor data for autonomous driving

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.653948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f9d2e5ff-bbac-4a16-bd6e-07b3c438cd76 · outbound

This paper cites Youtube.https://www.youtube.com, 2025.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Youtube.https://www.youtube.com, 2025

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.679295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c73722e3-9cbc-4054-a538-5d478d8c3ba2 · outbound

This paper cites Self- rewarding language models.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Self- rewarding language models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.628847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 27605bf1-0032-4bf0-a1e7-2c2b07897a1d · outbound

This paper cites Chatscene: Knowledge- enabled safety-critical scenario generation for autonomous vehicles.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Chatscene: Knowledge- enabled safety-critical scenario generation for autonomous vehicles

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.627213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2993034d-d2cf-41a2-af74-26e24cf9e090 · outbound

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

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation Cat: Closed-loop adversarial training for safe end-to-end driving

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:06:35.663921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2211bfeb-8f14-4eaf-bb43-921e4c0ba0e9 · outbound

This paper cites This is a one-way road.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation This is a one-way road

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.399654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Reference resolution

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