Paper Citation Record · LEDGER

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization

As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.02014.

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

pith.paper-citation-record.v1
2506.02014 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:24:35.461264Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact2
verified fuzzy14
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9d957bd3-8a27-4a42-8874-8d942d8bdfec · outbound

This paper cites A survey of au- tonomous driving: Common practices and emerging technologies,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization A survey of au- tonomous driving: Common practices and emerging technologies,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.667187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 81678a5f-fc85-4bea-a23b-2fa442dd0020 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization End-to-end autonomous driving: Challenges and frontiers,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:32.654009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 329bc6a6-95d9-4ceb-ae8d-6e6f3ba3cfcb · outbound

This paper cites Multi-modal fusion transformer for end-to-end autonomous driving,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Multi-modal fusion transformer for end-to-end autonomous driving,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:32.782627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d691431-9410-4cbe-a5f7-ae387ed4aedb · outbound

This paper cites Scene-adaptive and region-aware multi-modal prompt for open vocabulary object detection,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Scene-adaptive and region-aware multi-modal prompt for open vocabulary object detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.437010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fd1c9fdc-d96a-4bd2-bd1e-3af0504c8da9 · outbound

This paper cites Pseudoprop: Robust pseudo-label generation for semi-supervised object detection in autonomous driving systems,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Pseudoprop: Robust pseudo-label generation for semi-supervised object detection in autonomous driving systems,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.237955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ac13a652-f961-473a-8102-e59bb2d46b96 · outbound

This paper cites VLM-MPC: Vision Language Foundation Model (VLM)-Guided Model Predictive Controller (MPC) for Autonomous Driving.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization VLM-MPC: Vision Language Foundation Model (VLM)-Guided Model Predictive Controller (MPC) for Autonomous Driving

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:33.156456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c848593-766e-4df0-8e90-7e51a29eb486 · outbound

This paper cites Contextvlm: Zero-shot and few-shot context understanding for autonomous driving using vision language models,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Contextvlm: Zero-shot and few-shot context understanding for autonomous driving using vision language models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:38.073393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 542b2c62-c3ca-422f-9cd4-811a425ca526 · outbound

This paper cites Generative adversarial networks,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Generative adversarial networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.819026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 91c34906-808c-49ec-9f6f-a53b0c34c6b8 · outbound

This paper cites Dall-e: Creating images from text,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Dall-e: Creating images from text,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.650363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 672bec2e-47d7-4854-bad8-22988c3e107d · outbound

This paper cites Denoising diffusion probabilistic models,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Denoising diffusion probabilistic models,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:33.649057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b2985d6-1ceb-41ba-85dc-f437f0db095f · outbound

This paper cites Diffusion models beat gans on image synthesis,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Diffusion models beat gans on image synthesis,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:33.756206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc8d2d04-8b0c-4ba5-9797-8e71a741cf46 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language under- standing,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Photorealistic text-to-image diffusion models with deep language under- standing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.467858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2bbb4d33-6d16-44d8-98d3-36ac8be73f5f · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization High- resolution image synthesis with latent diffusion models,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:33.899796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b92eb170-6d53-45c6-a206-287f9f8bac62 · outbound

This paper cites Kolors: Effective training of diffusion model for photorealistic text-to-image synthesis,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Kolors: Effective training of diffusion model for photorealistic text-to-image synthesis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.282623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d53fd901-c510-4e3c-9733-7289cdb9a0b6 · outbound

This paper cites Carla: An open urban driving simulator,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Carla: An open urban driving simulator,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:37.101504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0336bbbd-92d2-47bf-b17c-2d27be21a6f6 · outbound

This paper cites Airsim: High-fidelity visual and physical simulation for autonomous vehicles,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Airsim: High-fidelity visual and physical simulation for autonomous vehicles,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.133397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 747ad2c7-cbeb-42df-ac50-98929679307c · outbound

This paper cites Mars: An instance-aware, modular and realistic simulator for autonomous driving,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Mars: An instance-aware, modular and realistic simulator for autonomous driving,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.921469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b15814ee-8eab-4432-b4c0-51c3a9f2758a · outbound

This paper cites Editable scene simulation for autonomous driving via collaborative llm- agents,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Editable scene simulation for autonomous driving via collaborative llm- agents,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.696449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1c25d434-1ed2-4c84-9017-102801d1de69 · outbound

This paper cites BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.422133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fe96ad3-5916-4d82-a1e8-7fa765ccb715 · outbound

This paper cites Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.502410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b0faf71-d18b-41fc-9a5a-66f9fd3bb50c · outbound

This paper cites All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:35.983381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2fdcabc7-cca0-4b9e-936f-8e904d73ebfb · outbound

This paper cites Prompt engineering for chatgpt: a quick guide to techniques, tips, and best practices,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Prompt engineering for chatgpt: a quick guide to techniques, tips, and best practices,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.646651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c255f226-6223-4e4c-b374-8f63285faa4b · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.704657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80a63cd2-c49e-4284-80c9-33203b0a57ce · outbound

This paper cites Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.774410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab5d59d5-7009-49be-b7ba-913e63d03361 · outbound

This paper cites Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.830812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27c369c9-4996-4972-832e-695793fe7308 · outbound

This paper cites An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:35.709502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8dd3c263-0ff2-4eac-bf7f-a135ef3f95b4 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:34.962254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5736b048-e000-4521-935b-f235d07b4ec6 · outbound

This paper cites Survey on knowledge distillation for large language models: methods, eval- uation, and application,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Survey on knowledge distillation for large language models: methods, eval- uation, and application,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.519369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dcf47bdf-7acf-495f-a21b-af1f21fcfffa · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Qlora: Efficient finetuning of quantized llms,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:35.158003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a7d4b58-54a9-4649-9abe-388ec126d186 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:35.245352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8ba5683-628c-43ae-aa39-a11796fc51d8 · outbound

This paper cites Overcoming forgetting catas- trophe in quantization-aware training,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Overcoming forgetting catas- trophe in quantization-aware training,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.316294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 33b45107-37fc-45a7-874d-6c9b39092e65 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration,.

Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization Awq: Activation-aware weight quantization for on-device llm compression and acceleration,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:36.137426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.