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Paper Citation Record · LEDGER

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding

As of 18 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2508.17205.

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

pith.paper-citation-record.v1
2508.17205 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:03:30.867812Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

29 of 29 outbound references displayed

  • verified exact3
  • verified fuzzy17
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca657a7d-85d5-4680-8996-aa5cc47fa2df · outbound

This paper cites Evaluating multimodal vision-language model prompting strategies for visual question answering in road scene understanding.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Evaluating multimodal vision-language model prompting strategies for visual question answering in road scene understanding

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 45f13c46-d0bc-415c-9fb2-f32c2a34fb0d · outbound

This paper cites NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models

Reference 2

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Source-reported events for the cited work

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Observation 24612c3d-ae44-4d2e-a87b-363c54b1a0fa · outbound

This paper cites Delving into multi-modal multi-task foundation models for road scene understanding: From learning paradigm perspectives.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Delving into multi-modal multi-task foundation models for road scene understanding: From learning paradigm perspectives

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 05c91a86-02e0-4621-a08c-9fd9eb6bf622 · outbound

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

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Chain-of- thought prompting elicits reasoning in large language models

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-05T17:03:34.795181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ccc76cfa-d67d-4f83-a6a0-9aefa0c18f34 · outbound

This paper cites Prompt Tuning for Generative Multimodal Pretrained Models.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Prompt Tuning for Generative Multimodal Pretrained Models

Reference 5

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Source-reported events for the cited work

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Observation 23c6c351-afdf-44af-b671-92666f9eae3c · outbound

This paper cites Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving

Reference 6

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no resolver link, observed 2026-08-05T17:03:28.619855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 386c89af-40fa-40b3-9ce1-af5edea68829 · outbound

This paper cites When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis

Reference 7

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verified exact
local_arxiv, observed 2026-08-05T17:03:31.521991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ed91b398-2e44-4996-8b88-34b2364b633f · outbound

This paper cites The origins of computer weather prediction and climate modeling.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding The origins of computer weather prediction and climate modeling

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 37a39bbd-2769-4a78-9887-3fc4521814ef · outbound

This paper cites Coupling ensemble kalman filter with four-dimensional variational data assimilation.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Coupling ensemble kalman filter with four-dimensional variational data assimilation

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9f15c165-0eb6-490f-961c-5401ff840593 · outbound

This paper cites Representing equilibrium and nonequilibrium convection in large-scale models.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Representing equilibrium and nonequilibrium convection in large-scale models

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1421301e-3bd8-48d3-a622-d125c96d1061 · outbound

This paper cites The growing impact of satellite observations sensitive to humidity, cloud and precipitation.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding The growing impact of satellite observations sensitive to humidity, cloud and precipitation

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a0ed3f99-0dca-4e37-b5ce-5f2a139fa9ea · outbound

This paper cites The quiet revolution of numerical weather prediction.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding The quiet revolution of numerical weather prediction

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c200055d-db42-4ee8-a246-2c1288f80f16 · outbound

This paper cites Weather recognition based on edge deterioration and convolutional neural networks.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Weather recognition based on edge deterioration and convolutional neural networks

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8f3cb292-6474-4868-8748-c5a629a50603 · outbound

This paper cites an unresolved cited work.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Unresolved cited work

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a32b2d79-a14c-432e-a0ee-44532f7e5929 · outbound

This paper cites Hourly day-ahead solar irradiance prediction using weather forecasts by lstm.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Hourly day-ahead solar irradiance prediction using weather forecasts by lstm

Reference 15

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raw_fallback, observed 2026-08-05T17:03:33.338154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation c6fdb4b3-c12e-46ae-92b5-5473045e52e3 · outbound

This paper cites Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks

Reference 16

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verified exact
local_arxiv, observed 2026-08-05T17:03:31.295049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f0af62f2-b2ee-4cc0-a9f8-894cc4b29379 · outbound

This paper cites ClimateBert: A Pretrained Language Model for Climate-Related Text.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding ClimateBert: A Pretrained Language Model for Climate-Related Text

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1022bb82-436a-437b-8384-e353d60d9b9f · outbound

This paper cites ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change

Reference 18

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no resolver link, observed 2026-08-05T17:03:30.097200Z

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Observation 197973c3-61e6-44cf-a8d9-42b9559a1bd2 · outbound

This paper cites Weather and surface condition detection based on road-side webcams: Application of pre-trained convolutional neural network.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Weather and surface condition detection based on road-side webcams: Application of pre-trained convolutional neural network

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0616729b-4502-4bc3-8539-7b877dbfafc5 · outbound

This paper cites Deep learning based infrared thermal image analysis of complex pavement defect conditions considering seasonal effect.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Deep learning based infrared thermal image analysis of complex pavement defect conditions considering seasonal effect

Reference 20

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raw_fallback, observed 2026-08-05T17:03:32.936430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0f94f681-6780-4633-a14a-34ec75df5a3e · outbound

This paper cites Road surface state recognition based on semantic segmentation.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Road surface state recognition based on semantic segmentation

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d2903d20-dc73-4591-9833-617242026244 · outbound

This paper cites Machine learning algorithms for wet road surface detection using acoustic measurements.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Machine learning algorithms for wet road surface detection using acoustic measurements

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1fc0376a-0436-495d-ad29-90642fbd5509 · outbound

This paper cites A deep learning technique to improve road maintenance systems based on climate change.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding A deep learning technique to improve road maintenance systems based on climate change

Reference 23

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raw_fallback, observed 2026-08-05T17:03:32.402406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 338cd6c4-ad34-41c8-975b-4026bdeab269 · outbound

This paper cites Traffic congestion anomaly detection and prediction using deep learning.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Traffic congestion anomaly detection and prediction using deep learning

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-05T17:03:31.083607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9b6a5594-4ca6-4537-9eea-42960efafda6 · outbound

This paper cites Highway traffic congestion detection and evaluation based on deep learning techniques.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Highway traffic congestion detection and evaluation based on deep learning techniques

Reference 25

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raw_fallback, observed 2026-08-05T17:03:32.203376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 398cffe3-1d15-4016-82a6-f68a37191bce · outbound

This paper cites Traffic congestion detection from camera images using deep convolution neural networks.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Traffic congestion detection from camera images using deep convolution neural networks

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-05T17:03:31.967633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-05T17:03:30.607783Z digest=sha256:ac2780409c2222f650bd19c689079492970794deffcf11c2d8241d9635666ada

Observation 586d8a57-ff0c-4c3d-8ed5-c418345a4516 · outbound

This paper cites Cheu, Yisheng Lv, and Ruimin Ke.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Cheu, Yisheng Lv, and Ruimin Ke

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-05T17:03:31.783428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1e1ac392-ca74-48b1-a269-e1813e534cf7 · outbound

This paper cites GPT-4 Technical Report.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding GPT-4 Technical Report

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 54e14e02-2b3f-4383-8f6a-23c0f5cac038 · outbound

This paper cites Qwen2.5-VL Technical Report.

Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding Qwen2.5-VL Technical Report

Reference 29

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no resolver link, observed 2026-08-05T17:03:30.867812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.