Pith. sign in

Paper Citation Record · LEDGER

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2505.18198.

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

pith.paper-citation-record.v1
2505.18198 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:42.197585Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:40:48.124356Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:40:48.743693Z

Reference resolution

32 of 32 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3c2c5ba1-2f50-4489-86e1-b5ddc214e456 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:39.451952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:39.451952Z digest=sha256:1d6b2ce6719901b30b6069fb3caa8092d35b05a4d6d5216a8dd1211abe045ab9

Observation c4904325-712c-474b-8f56-49a6e9f14ae7 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:47.430941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:39.529924Z digest=sha256:7048785229b5889b8862c5457bdbf7e874ee2c837e2c9188a4b599b70393591a

Observation 138718f7-c90a-4f73-b171-ea5626186de0 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:47.183506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:39.621675Z digest=sha256:a46d3b506577f21a2d6a5df51a8c8fea9e72731b9bbce2819d4e06677683fcc7

Observation 0b4aa0bf-50c5-432c-86c2-5d30f301b643 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:46.795593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:39.684818Z digest=sha256:e84128765c2e2f1380cabc50c8c989096610f3e710c5faba94dae94b4565eaec

Observation 9e839672-8436-42ea-ae6d-cefd6eeab996 · outbound

This paper cites Classifier-Free Diffusion Guidance.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Classifier-Free Diffusion Guidance

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:39.768141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:39.768141Z digest=sha256:e171285f37224d66279cd324d257a2d87e0b477047bc30c104990b429e247cfb

Observation 4e7ab221-cd21-4d63-aad2-a6075642a09b · outbound

This paper cites EMMA: End-to-End Multimodal Model for Autonomous Driving.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving EMMA: End-to-End Multimodal Model for Autonomous Driving

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:39.831382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:39.831382Z digest=sha256:d8348b802f82ea7e08028c0175294daead41be4f388ccd40013ef0a2b6db1e47

Observation be331bfb-c53d-491d-8059-c79052f7cae6 · outbound

This paper cites a henb \.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving a henb \

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:26:46.484229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:39.900827Z digest=sha256:2b3f642eb4fd7f3cde33499c5457b369c704361502b2076b96aefab2c7dec179

Observation 392f791f-d55e-482f-a4a1-ea9077272f0a · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:46.192948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:39.968355Z digest=sha256:406ab4285d8f8042e7f5f8f04e7ddfe374e28d2fea92a4325185f0ccaf781fa9

Observation 85e8bab9-ccad-4feb-b1d5-47f8bdbf61c4 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:45.924221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:40.058928Z digest=sha256:00f8cc34d1cf9d3393ccd1a8e3b8f99ac3751526c2f020f6eabfbd0b0aeb9831

Observation 0ab38816-5ec6-419f-9610-4d13d18e1536 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:45.592480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:40.128463Z digest=sha256:711bd0dadedb5ef056538f2b0915db62017dfda049e4aff9ded5ca70c3857dff

Observation dbee9e65-f6a8-4eb4-b336-dc55aff77365 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:40.215643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:40.215643Z digest=sha256:4a904df74193b02c0f543a896882d43fa78247eeb0acc520b3199c2e1b6eccb4

Observation 92322b19-773b-46ca-84a1-98d2c9c9e8d7 · outbound

This paper cites DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:40.316385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:40.316385Z digest=sha256:4edf9f714dd831322c18495519a6d897fd64ae3483b95d246d722faf6a254d96

Observation 407b2203-9435-4146-bc17-458a1f060498 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:45.348551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:40.426832Z digest=sha256:4ff100b6f4dc25d97a87abdcefb6f7bd1f8f83c2d2921026e3e0ecea57a415e7

Observation 41ee0ed8-3506-4cc1-9015-7be51d962ec3 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:40.533731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:40.533731Z digest=sha256:17170eb7e530a52545b09b1155831736836f6b91389b7c95f49a0b141d4bb2ea

Observation 15cb63b7-9c6a-4f8c-9bbf-8c1e62f2a4a6 · outbound

This paper cites MTA: Multimodal Task Alignment for BEV Perception and Captioning.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving MTA: Multimodal Task Alignment for BEV Perception and Captioning

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:26:42.627301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:40.646360Z digest=sha256:6340a0e7e0d43fe383d97915818c5b38aaabb74c4c0d7de4ed92f29ebae21ccc

Observation 355ed798-3b6f-47a3-8e86-9705f00ce579 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:45.013271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:40.765089Z digest=sha256:bb82e8d60a1c0bd6c1809177126efdcdaf35cc15e69bba3122824e3b6c88da8d

Observation 06ff6f7e-c1e9-4388-9303-7d48f8d9c348 · outbound

This paper cites GPT-Driver: Learning to Drive with GPT.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving GPT-Driver: Learning to Drive with GPT

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:40.867231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:40.867231Z digest=sha256:79f36d05d263abbea6cb36f0f860f7491c6320dea1645a5666859509756fcb6b

Observation aede3e30-1287-42ea-9f8e-d88b61dfcfa9 · outbound

This paper cites GPT-4 Technical Report.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving GPT-4 Technical Report

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:40.979581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:40.979581Z digest=sha256:5e91692aa4ea0c5dd97b62947ec7fe8167bfb8803bda0a3cf632926acbaadfb2

Observation 75b43485-db34-4901-b5eb-78bf4cf74c64 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:44.697205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.112545Z digest=sha256:3350b1d60c34273f24f0556808d22ae5982bb45925b0a52cd55554bfef569f70

Observation 02f0bfa7-6355-46bd-aff6-101fb0d684c0 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:41.187562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:41.187562Z digest=sha256:8eecdb09141d4e72972373187e25bfeaf4c67018c12fc5bc422d20d5850696be

Observation b9702799-6151-45c2-bdb6-0f2f646d2747 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:41.273600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:41.273600Z digest=sha256:0f4f02966fbae3cf74a45cf3e4d4e773c8e3460047ad98558d0da0bd13ac8e18

Observation 2333153b-b367-4a62-a518-a8dae534b436 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:44.372310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.352454Z digest=sha256:9e03e690bc44b06656adf3b1968cccfdbf696b993ffb259e5e1faf3f4678d654

Observation b763d62b-21ed-46b4-9dea-d46fefcc8fdc · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:44.203642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.375545Z digest=sha256:eed4f3496898efe7b08ed992f9dabe92a2cdee7c1021699cc053f380dd628b29

Observation d772c536-81a0-4c64-8aa9-ad378cc8d3c9 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:43.995680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.457959Z digest=sha256:ec122611f3146e53aea680161829ee4e1fbd59552d04c66b951ef56942079106

Observation 00d350de-4ab0-4a48-b77f-a6b0c8120eda · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:43.717974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.536300Z digest=sha256:c88b2c0a6dbe17897ed3f5585f6a6cc336b17d31d82868921b0e3f023c5004bd

Observation ae59b383-af8f-4554-b983-7396f10b9899 · outbound

This paper cites BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:41.632604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:41.632604Z digest=sha256:de2ae70503dd4bfd4a246d822cd92124f2414a5935853660326d3ceb60d9dddd

Observation c2df6924-b4af-4fe5-9f72-5b91365c7c59 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:43.606652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.711667Z digest=sha256:cad18bb3349db3b4e8873a1842b5e3c73e20fc827ca6f3bbe7dad3286cf3bb36

Observation 62af31d0-cb66-4405-90d3-ef41de1722fc · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:43.462187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.797832Z digest=sha256:869aa3c3820d3ea8f4aad77d823ed0342fba9f2cf46e6c978de299c6669ab844

Observation b01ada29-adc7-4c11-b892-1e49781153d7 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:43.308686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:41.879370Z digest=sha256:84417c43c071aea7fe48c6d27cc1db04359c5f3370c47a1c0a6633ef0fe22aa4

Observation e61df2d5-cc67-40ef-b39d-ac2e7fcbcc95 · outbound

This paper cites an unresolved cited work.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:26:43.124303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-07T15:26:42.014721Z digest=sha256:cba0b4aa6d5b88cd1e6a9d5726575bdcf92ab518e60a2dc8a373884537c94db2

Observation 90101643-26e0-4848-be52-20a40739e176 · outbound

This paper cites online" 'onlinestring :=.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving online" 'onlinestring :=

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:42.090828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:42.090828Z digest=sha256:49aadf120aa218abad9e28cd50a21f2cbe867ec8ebd26200750562fb8131f0eb

Observation 9f452529-3582-49da-8f39-f853cef5e733 · outbound

This paper cites write newline.

LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving write newline

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:42.197585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:42.197585Z digest=sha256:ce5f8efbb9cad06d6ae2a16ad655ffe9ec312de8d15c67cf788e765267a8cf10

Pith citing papers

Observation b310e224-177f-4067-85a8-434229380fed · inbound

SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling cites this paper.

SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving

Reference 15

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T22:40:48.794657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T22:40:48.124356Z digest=sha256:672b33d036102a1192e330a55cf597e3c9a2a9ba8c9f5ff2558bca2e33d387ea