Paper Citation Record · LEDGER

Why Braking? Scenario Extraction and Reasoning Utilizing LLM

As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.15874.

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

pith.paper-citation-record.v1
2507.15874 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:41:16.950593Z

measured 21 of 21 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

21 of 21 outbound references displayed

verified exact0
verified fuzzy13
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b4d3584-3f20-4154-a288-0463a60408e7 · outbound

This paper cites Krzanich.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Krzanich

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:19.533099Z

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.

Observation c4265718-7b10-48eb-9d88-b8d253f241ef · outbound

This paper cites Real-world scenario mining for the assessment of automated vehicles,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Real-world scenario mining for the assessment of automated vehicles,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:19.333461Z

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.

Observation 5aa693ab-b93a-435d-93a2-9fd58935d8cf · outbound

This paper cites Scenario extraction from a large real-world dataset for the assessment of automated vehicles,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Scenario extraction from a large real-world dataset for the assessment of automated vehicles,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:19.143145Z

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.

Observation e90d9357-6051-4aaf-bbd2-b586f62170c9 · outbound

This paper cites Chat2scenario: Scenario extraction from dataset through utilization of large language model,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Chat2scenario: Scenario extraction from dataset through utilization of large language model,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:18.972784Z

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.

Observation 0cd861e5-9fce-41b0-b485-37c9b4fbfefa · outbound

This paper cites Refav: Towards planning- centric scenario mining,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Refav: Towards planning- centric scenario mining,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:15.783036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6512ddd9-f0e8-489c-a309-d3f2a7628c4d · outbound

This paper cites Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:15.850805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65a7a89d-7bf0-4fbb-9623-a703b727c8e3 · outbound

This paper cites Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:18.838543Z

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.

Observation 9d3bd19e-2073-4a1b-a211-b87f2850c46a · outbound

This paper cites 6-layer model for a structured description and categorization of urban traffic and environment,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM 6-layer model for a structured description and categorization of urban traffic and environment,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:18.597552Z

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.

Observation ffe5e7bb-3963-416b-9f2b-703e0c9a4ec0 · outbound

This paper cites Scenario based testing of automated driving systems: A literature survey,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Scenario based testing of automated driving systems: A literature survey,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:18.449050Z

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.

Observation e1286c1e-2943-4091-ba27-25986be24380 · outbound

This paper cites 1001 ways of scenario generation for testing of self-driving cars: A survey,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM 1001 ways of scenario generation for testing of self-driving cars: A survey,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:18.272086Z

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.

Observation 0240ba11-5ce5-479c-9a19-6a2fa1b2786d · outbound

This paper cites Generat- ing useful accident-prone driving scenarios via a learned traffic prior,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Generat- ing useful accident-prone driving scenarios via a learned traffic prior,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:18.062079Z

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.

Observation 576559f7-1a1a-4105-af04-c42bb46d2538 · outbound

This paper cites Drivelm: Driving with graph visual question answering,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Drivelm: Driving with graph visual question answering,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:17.844826Z

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.

Observation 9850a620-0086-4ecd-9413-9efff8f02c81 · outbound

This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:16.352891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7532374-d612-4892-a6df-da7372b78012 · outbound

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

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Chain-of-thought prompting elicits reasoning in large language models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:17.660565Z

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.

Observation 035a7b45-dc91-4f7d-88a3-94e466d705c1 · outbound

This paper cites Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:17.389007Z

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.

Observation fed81936-85ea-4171-9a9e-44921601c594 · outbound

This paper cites What is a savitzky-golay filter?[lecture notes],.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM What is a savitzky-golay filter?[lecture notes],

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:41:17.239762Z

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.

Observation b3c5a603-43f4-4f4a-8271-dceea1156bdb · outbound

This paper cites A Survey of Large Language Models.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM A Survey of Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:16.669628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bf8f006-e7b9-4492-b83f-9f5f7abe748b · outbound

This paper cites GPT-4 Technical Report.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM GPT-4 Technical Report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:16.727159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7da48c8-2d0d-4997-8677-904998106e20 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Gemini: A Family of Highly Capable Multimodal Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:16.828779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47003495-989c-48a0-9a1b-e7b760923b91 · outbound

This paper cites Qwen3 Technical Report.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Qwen3 Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:16.900456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d050319-fedb-4d3e-9c4e-4cbe0845285f · outbound

This paper cites Nomic Embed: Training a Reproducible Long Context Text Embedder.

Why Braking? Scenario Extraction and Reasoning Utilizing LLM Nomic Embed: Training a Reproducible Long Context Text Embedder

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T16:41:16.950593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.