Paper Citation Record · LEDGER

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models

As of 10 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 2 inbound Pith citation observations for arXiv:2508.02269.

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

pith.paper-citation-record.v1
2508.02269 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:08:43.193209Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:04:18.241312Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:39:28.484771Z

Reference resolution

21 of 21 outbound references displayed

verified exact6
verified fuzzy3
unresolved9
parse uncertain0
malformed identifier2
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f0d2601-2b34-4793-a50e-cff8621c2339 · outbound

This paper cites Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:41.316844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dda7eb6d-c681-4309-9aa5-41862c371dd0 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:41.499542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79b32b19-f288-4d7a-b055-ae744e375144 · outbound

This paper cites Wei Dong, Xianqing Chen, and Qiang Yang.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Wei Dong, Xianqing Chen, and Qiang Yang

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:41.850337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90df88f3-d639-4ca1-84fc-c6665163c042 · outbound

This paper cites Lan Feng, Quanyi Li, Zhenghao Peng, Shuhan Tan, and Bolei Zhou.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Lan Feng, Quanyi Li, Zhenghao Peng, Shuhan Tan, and Bolei Zhou

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:08:46.223409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 12d183ea-c0d1-4798-b7da-f100c12d5719 · outbound

This paper cites doi: 10.3390/risks10110199.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models doi: 10.3390/risks10110199

Reference 11

Resolution: verified exact; doi, observed 2026-08-06T05:08:44.351029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 35ec803f-c7fa-4a20-99fb-0cd8824a6183 · outbound

This paper cites Qiujing Lu, Xuanhan Wang, Yiwei Jiang, Guangming Zhao, Mingyue Ma, and Shuo Feng.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Qiujing Lu, Xuanhan Wang, Yiwei Jiang, Guangming Zhao, Mingyue Ma, and Shuo Feng

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:42.252217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f498cd5e-ecbc-424a-8dd6-f20f133438bc · outbound

This paper cites Qwen Team.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Qwen Team

Reference 15

Resolution: verified exact; doi, observed 2026-08-06T05:08:44.122094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e66addf1-d7d7-420d-88f8-31a4bd1d86b8 · outbound

This paper cites 11 Set of Allowed Routes Through Sector (with directionality) A B C D E F G Figure 8: One of the synthetic sectors used in the benchmarking.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models 11 Set of Allowed Routes Through Sector (with directionality) A B C D E F G Figure 8: One of the synthetic sectors used in the benchmarking

Reference 18

Resolution: verified exact; doi, observed 2026-08-06T05:08:43.424136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23423beb-37d0-4308-8c99-3aeed4f29dfb · outbound

This paper cites URL https: //ojs.aaai.org/index.php/AAAI/article/view/32951.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models URL https: //ojs.aaai.org/index.php/AAAI/article/view/32951

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:42.985805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f29ddd4d-221a-448a-98ff-f7fd5d2c2b48 · outbound

This paper cites Values quoted are the average number of unique interacting pairs of aircraft computed across 10 generated scenarios on 10 synthetic sectors.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Values quoted are the average number of unique interacting pairs of aircraft computed across 10 generated scenarios on 10 synthetic sectors

Reference 20

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T05:08:45.956560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c1d125a6-d4b5-4d3c-8077-5519adb851d4 · outbound

This paper cites an unresolved cited work.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:08:45.691247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c1af2db-6a12-49fb-9e68-0dd1de92d0a7 · outbound

This paper cites Feedback game on $3$-chromatic Eulerian triangulations of surfaces.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Feedback game on $3$-chromatic Eulerian triangulations of surfaces

Reference 2002

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:08:44.631334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6bbc155e-0267-4671-875e-192c3305e536 · outbound

This paper cites Practical Quantum Computing: solving the wave equation using a quantum approach.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Practical Quantum Computing: solving the wave equation using a quantum approach

Reference 2003

Resolution: malformed identifier; local_arxiv, observed 2026-08-06T05:08:44.906182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd9fd1d2-7be2-4956-9fea-9d842f84a5f7 · outbound

This paper cites an unresolved cited work.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Unresolved cited work

Reference 2004

Resolution: verified exact; doi, observed 2026-08-06T05:08:43.613615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa907246-0a67-4425-90d1-af825012aa60 · outbound

This paper cites EUROCONTROL.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models EUROCONTROL

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:08:46.549241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ca20a03-ce75-4d7a-8ba8-8913257c5e33 · outbound

This paper cites doi: https://doi.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models doi: https://doi

Reference 2018

Resolution: verified exact; doi, observed 2026-08-06T05:08:43.792112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27acd2af-f6f1-4442-93e4-5012be0e1101 · outbound

This paper cites doi: 10.18653/v1/N19-1423.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models doi: 10.18653/v1/N19-1423

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:41.776463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 279f9fad-c56b-4235-a93f-39fe53026695 · outbound

This paper cites doi: 10.3390/machines10111101.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models doi: 10.3390/machines10111101

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:41.181894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abd59a1a-fca9-4c73-8b26-ae7cd5b033a1 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:08:46.815621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6fca0d95-8920-4fd8-aec8-75c03eb61aab · outbound

This paper cites Google DeepMind.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models Google DeepMind

Reference 2024

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T05:08:45.354242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2defe70b-80b0-4860-a69e-45ce02c138a7 · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T05:08:41.113370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c26874c0-05ba-4455-8624-95c8354b9be1 · inbound

Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems cites this paper.

Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:39:28.487264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a376faa1-b78d-4b8d-b9e8-fefca9d3704e · inbound

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications cites this paper.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T12:04:18.241312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.