Paper Citation Record · LEDGER

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2507.13729.

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

pith.paper-citation-record.v1
2507.13729 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:23:41.783008Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T19:18:40.244556Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T19:45:01.648924Z

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy37
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation abfd15ea-b439-4971-8796-24e579e60356 · outbound

This paper cites Anomaly detection in multi-agent trajectories for automated driving,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Anomaly detection in multi-agent trajectories for automated driving,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:48.612376Z

Source-reported events for the cited work

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

Observation b2d8d0b7-a859-462e-8cc1-24e04f54d67e · outbound

This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:48.491021Z

Source-reported events for the cited work

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

Observation a82f4ff5-93a5-4b0c-b1a2-dbc2fbbf49fb · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Scalability in perception for autonomous driving: Waymo open dataset,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:48.377655Z

Source-reported events for the cited work

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

Observation 43a2d1da-95bd-4600-acdf-0b528937d444 · outbound

This paper cites Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:48.262463Z

Source-reported events for the cited work

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

Observation 80c75645-347e-4812-ad3b-bb44e7690b16 · outbound

This paper cites SLEDGE: Synthe- sizing Driving Environments with Generative Models and Rule-Based Traffic,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework SLEDGE: Synthe- sizing Driving Environments with Generative Models and Rule-Based Traffic,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:48.141307Z

Source-reported events for the cited work

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

Observation 78b1b082-0f85-4659-9a82-6665ea24ad0b · outbound

This paper cites Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:39.054292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3e0b44d-fb60-460a-9fed-2832cb442233 · outbound

This paper cites Simnet: Learn- ing reactive self-driving simulations from real-world obser- vations,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Simnet: Learn- ing reactive self-driving simulations from real-world obser- vations,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:48.020999Z

Source-reported events for the cited work

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

Observation 4a5793d5-62d9-47b7-aacf-0ef530a32e28 · outbound

This paper cites Scenegen: Learning to generate realistic traffic scenes,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Scenegen: Learning to generate realistic traffic scenes,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.893217Z

Source-reported events for the cited work

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

Observation 1c64e8a4-73e0-4092-a08d-6f9fb7e44a1d · outbound

This paper cites Can Vehicle Motion Planning Generalize to Realistic Long- tail Scenarios?.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Can Vehicle Motion Planning Generalize to Realistic Long- tail Scenarios?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.763512Z

Source-reported events for the cited work

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

Observation 6ae217ff-228e-4edd-9729-a59e9dd372e5 · outbound

This paper cites React: Synergizing reasoning and acting in language models,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework React: Synergizing reasoning and acting in language models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.639683Z

Source-reported events for the cited work

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

Observation be0272fb-853a-4b2d-8cc9-9ceef056b7d0 · outbound

This paper cites Chatbot arena: An open platform for evaluating llms by human preference,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Chatbot arena: An open platform for evaluating llms by human preference,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.490268Z

Source-reported events for the cited work

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

Observation 73950805-e5c1-4517-94c4-554e475d7b88 · outbound

This paper cites A Survey on Data-Driven Scenario Generation for Automated Vehicle Testing,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework A Survey on Data-Driven Scenario Generation for Automated Vehicle Testing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.379712Z

Source-reported events for the cited work

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

Observation 30c000ec-a436-45fb-b11a-b5b8c6415c33 · outbound

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

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework A survey on safety-critical driving scenario generation—a methodological perspective,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.255311Z

Source-reported events for the cited work

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

Observation 6c29a4f4-cfd0-42e8-8704-38fac5c2c47d · outbound

This paper cites SceneControl: Diffusion for Controllable Traffic Scene Generation,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework SceneControl: Diffusion for Controllable Traffic Scene Generation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.106085Z

Source-reported events for the cited work

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

Observation ddad598a-5389-4fe8-a57d-9f1f60f76658 · outbound

This paper cites GeoScenario: An open DSL for autonomous driving scenario representation,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework GeoScenario: An open DSL for autonomous driving scenario representation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:47.004732Z

Source-reported events for the cited work

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

Observation c8210e08-17ed-4da0-91b5-44ea8ae1b8f9 · outbound

This paper cites SceGene: Bio-Inspired Traffic Scenario Genera- tion for Autonomous Driving Testing,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework SceGene: Bio-Inspired Traffic Scenario Genera- tion for Autonomous Driving Testing,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:46.881425Z

Source-reported events for the cited work

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

Observation 54257cfc-bdfe-4394-836a-22308fcd1f7a · outbound

This paper cites A comprehensive review on ontologies for scenario-based testing in the context of autonomous driving,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework A comprehensive review on ontologies for scenario-based testing in the context of autonomous driving,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:46.745755Z

Source-reported events for the cited work

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

Observation 9378043d-93bb-4003-8a9a-a53cfe04b67d · outbound

This paper cites Traffic Scenarios for Automated Vehicle Testing: A Review of Description Languages and Systems,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Traffic Scenarios for Automated Vehicle Testing: A Review of Description Languages and Systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:46.594041Z

Source-reported events for the cited work

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

Observation e93eca1e-f9c2-4e0e-b747-c4225d22509a · outbound

This paper cites Text-to-drive: Diverse driving behavior synthesis via large language models,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Text-to-drive: Diverse driving behavior synthesis via large language models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:46.484182Z

Source-reported events for the cited work

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

Observation b0092be5-819b-424e-8a0e-c5c7192601bf · outbound

This paper cites Scenic: a lan- guage for scenario specification and scene generation,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Scenic: a lan- guage for scenario specification and scene generation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:46.352536Z

Source-reported events for the cited work

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

Observation bf036d69-9f21-4c24-bc22-57f00d441a42 · outbound

This paper cites ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehi- cles,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehi- cles,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:46.109490Z

Source-reported events for the cited work

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

Observation 8062d74f-e287-4d08-8aa4-9c59415c112f · outbound

This paper cites Dialogue-based generation of self-driving simulation scenarios using Large Language Models.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Dialogue-based generation of self-driving simulation scenarios using Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:23:42.073641Z

Source-reported events for the cited work

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

Observation af0d15aa-b3f9-4111-875c-b7bb0730d934 · outbound

This paper cites Language Conditioned Traffic Generation,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Language Conditioned Traffic Generation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:45.871269Z

Source-reported events for the cited work

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

Observation 537f5ba2-ab6c-4b45-8623-0d3428c51eba · outbound

This paper cites Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:45.627447Z

Source-reported events for the cited work

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

Observation d19b54ca-f4cc-4786-9c11-96139abd8146 · outbound

This paper cites DriveSceneGen: Generating Diverse and Realistic Driving Scenarios From Scratch,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework DriveSceneGen: Generating Diverse and Realistic Driving Scenarios From Scratch,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:45.419651Z

Source-reported events for the cited work

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

Observation 6d19dd4b-565c-41d4-9915-7c72d7cc951f · outbound

This paper cites Realgen: Retrieval augmented generation for controllable traffic scenarios,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Realgen: Retrieval augmented generation for controllable traffic scenarios,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:45.226773Z

Source-reported events for the cited work

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

Observation 90c8c10d-d445-4e0f-909e-c81bbd4c0b0f · outbound

This paper cites UniSim: A Neural Closed-Loop Sensor Simulator,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework UniSim: A Neural Closed-Loop Sensor Simulator,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:44.979822Z

Source-reported events for the cited work

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

Observation 4b413680-ba23-4077-89be-723e58668013 · outbound

This paper cites On ad- versarial robustness of trajectory prediction for autonomous vehicles,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework On ad- versarial robustness of trajectory prediction for autonomous vehicles,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:44.793709Z

Source-reported events for the cited work

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

Observation 31088be1-9ab8-4379-944b-e1cf45bab983 · outbound

This paper cites Stay on track: A frenet wrapper to overcome off-road trajectories in vehicle motion prediction,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Stay on track: A frenet wrapper to overcome off-road trajectories in vehicle motion prediction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:44.610266Z

Source-reported events for the cited work

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

Observation b2485a4d-cf2f-4e40-85f0-d654c4be3e01 · outbound

This paper cites Vehicle trajectory prediction works, but not everywhere,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Vehicle trajectory prediction works, but not everywhere,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:44.414407Z

Source-reported events for the cited work

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

Observation a15012bc-6ce4-4f95-867f-1783b0bc6c71 · outbound

This paper cites AutoGen: Enabling Next- Gen LLM Applications via Multi-Agent Conversation,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework AutoGen: Enabling Next- Gen LLM Applications via Multi-Agent Conversation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:44.226472Z

Source-reported events for the cited work

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

Observation d81e1e20-091e-426d-a198-5a2b2e5bb896 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Chain-of-thought prompting elicits reasoning in large language models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:44.002017Z

Source-reported events for the cited work

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

Observation 42f9b59e-cf5e-49da-848c-47810048df0b · outbound

This paper cites Toolformer: Language models can teach themselves to use tools,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Toolformer: Language models can teach themselves to use tools,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:43.794050Z

Source-reported events for the cited work

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

Observation ac2c52fe-01d8-4dfa-8fb5-3ca7cd4250ac · outbound

This paper cites Urban Driver: Learning to Drive from Real- world Demonstrations Using Policy Gradients,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Urban Driver: Learning to Drive from Real- world Demonstrations Using Policy Gradients,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:43.538027Z

Source-reported events for the cited work

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

Observation 66f5594a-5045-4ec1-9e1a-6fde84590dda · outbound

This paper cites GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:43.336944Z

Source-reported events for the cited work

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

Observation 6043922e-9626-4089-a855-1a4b160a61a4 · outbound

This paper cites From prediction to planning with goal conditioned lane graph traversals,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework From prediction to planning with goal conditioned lane graph traversals,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:43.137125Z

Source-reported events for the cited work

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

Observation 1e58f568-2d19-4ea7-a17e-e57c690f3184 · outbound

This paper cites DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:42.884079Z

Source-reported events for the cited work

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

Observation 343056c3-6eff-45d6-9bfc-84018dbde67e · outbound

This paper cites Parting with Misconceptions about Learning-based Vehicle Motion Planning,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework Parting with Misconceptions about Learning-based Vehicle Motion Planning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:42.616583Z

Source-reported events for the cited work

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

Observation f380f5ac-9a4b-4a93-b00b-ec93a1590b0d · outbound

This paper cites MBAPPE: MCTS-built-around prediction for planning explicitly,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework MBAPPE: MCTS-built-around prediction for planning explicitly,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:23:42.387733Z

Source-reported events for the cited work

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

Observation 5439393f-e6dd-49e8-ba42-307dde664582 · outbound

This paper cites The Hungarian method for the assignment problem,.

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework The Hungarian method for the assignment problem,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:41.783008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation a9a6dc00-c976-459f-b9fb-058bb4dbf1a3 · inbound

Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security cites this paper.

Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework

Reference 157

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:45:01.650404Z

Source-reported events for the cited work

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