Paper Citation Record · LEDGER

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis

As of 20 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.07853.

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

pith.paper-citation-record.v1
2505.07853 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:22:46.540150Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:06:25.134699Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T19:06:25.457629Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c9b88c83-2d56-4716-b555-2a008ad0487f · outbound

This paper cites Early estimates of motor vehicle traffic fatalities and fatality rate by sub-categories in 2022.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Early estimates of motor vehicle traffic fatalities and fatality rate by sub-categories in 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.346044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 05125761-7703-4d84-b2bb-437c0498f6a6 · outbound

This paper cites An econometric framework for integrating aggregate and disaggregate level crash analysis.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis An econometric framework for integrating aggregate and disaggregate level crash analysis

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.329092Z

Source-reported events for the cited work

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Observation 3792390d-35b5-44b2-826a-3e4b24a4c373 · outbound

This paper cites Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.313549Z

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Observation 02ae4704-e6a7-435f-9dd3-1d8d3abc3a39 · outbound

This paper cites Relationships among urban freeway accidents, traffic flow, weather, and lighting conditions.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Relationships among urban freeway accidents, traffic flow, weather, and lighting conditions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.298728Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4fd5b68-f011-43e6-a3c7-a1b84ab00d15 · outbound

This paper cites A joint econometric analysis of seat belt use and crash-related injury severity.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A joint econometric analysis of seat belt use and crash-related injury severity

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.283076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ab4310f3-0a71-42ca-8981-007a2365bcd4 · outbound

This paper cites The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.268741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fc4276ad-eb90-43a8-b082-f187c34e54e9 · outbound

This paper cites Unraveling the dynamics of single-vehicle versus multi-vehicle crashes: a comparative analysis through binary classification.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Unraveling the dynamics of single-vehicle versus multi-vehicle crashes: a comparative analysis through binary classification

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.253559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4db4cb51-fdaa-41b3-9327-e7227ce81ba4 · outbound

This paper cites An empirical assessment of the effects of economic recessions on pedestrian- injury crashes using mixed and latent-class models.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis An empirical assessment of the effects of economic recessions on pedestrian- injury crashes using mixed and latent-class models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.234473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6fa4d8a0-c517-4cec-b164-09f337ed4f34 · outbound

This paper cites A comparison of the mixed logit and latent class methods for crash severity analysis.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A comparison of the mixed logit and latent class methods for crash severity analysis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.215867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1a1cca11-2712-44ad-acd5-9e2cb4b13d87 · outbound

This paper cites A note on generalized ordered outcome models.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A note on generalized ordered outcome models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.200078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec41994e-a3ac-4a86-b55a-6bd504bf092a · outbound

This paper cites Analyzing the continuum of fatal crashes: A generalized ordered approach.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Analyzing the continuum of fatal crashes: A generalized ordered approach

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.183122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35a42d84-a5a8-4dcc-92f0-501f972282fc · outbound

This paper cites A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.165888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f7724370-b1de-4475-9d32-08853fad78ed · outbound

This paper cites A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.151167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d16929bb-8335-494d-bab8-342b20fa72f9 · outbound

This paper cites The analysis of vehicle crash injury-severity data: A markov switching approach with road-segment heterogeneity.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis The analysis of vehicle crash injury-severity data: A markov switching approach with road-segment heterogeneity

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.134220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9a043913-22a3-41dc-b765-e2dba8c5f2b3 · outbound

This paper cites Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.119909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a6c0ed85-d71d-4270-b60c-933bd0d25bc3 · outbound

This paper cites A deep learning based traffic crash severity prediction framework.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A deep learning based traffic crash severity prediction framework

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.103552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b64f4243-4222-416c-8598-3fb16019e88a · outbound

This paper cites A unified approach to interpreting model predictions.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A unified approach to interpreting model predictions

Reference 17

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Source-reported events for the cited work

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Observation 6f360499-dd49-41ac-9149-e70567eaceef · outbound

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CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:22:47.077793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77224d61-d3e9-48f3-8439-47ad2222e6ca · outbound

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

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:46.423711Z

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Observation eb5f84d1-12f0-44b4-b582-9605b234126a · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:46.427722Z

Source-reported events for the cited work

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Observation 42e36117-0c85-4093-8fb7-20e01376778a · outbound

This paper cites Improving language understanding by generative pre-training.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Improving language understanding by generative pre-training

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:46.432070Z

Source-reported events for the cited work

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Observation d22b3d49-7787-456a-99aa-13a3d45d7c97 · outbound

This paper cites A simple mathematical theory for Simple Volatile Memristors and their spiking circuits.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis A simple mathematical theory for Simple Volatile Memristors and their spiking circuits

Reference 22

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2b7555a4-e6da-4f2a-b053-968a407a3650 · outbound

This paper cites Chatgraph: Interpretable text classification by converting chatgpt knowledge to graphs.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Chatgraph: Interpretable text classification by converting chatgpt knowledge to graphs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.041210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a113dfbf-884a-4d75-994b-5adc273e4939 · outbound

This paper cites SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question Answering?

Reference 24

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Source-reported events for the cited work

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Observation 76ba85e8-85f3-4a59-864d-856ae7e43c0f · outbound

This paper cites MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering

Reference 25

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Source-reported events for the cited work

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Observation 5b31d51c-ae8b-44fb-8001-423eafa88f5f · outbound

This paper cites Leveraging large language models with chain-of-thought and prompt engineering for traffic crash severity analysis and inference.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Leveraging large language models with chain-of-thought and prompt engineering for traffic crash severity analysis and inference

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:47.025053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e747f55a-3174-4d8c-8590-34e76b368e5a · outbound

This paper cites Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses

Reference 27

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Observation c7b592a8-9cd8-4a92-84e8-8a2aafb6e004 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 28

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Source-reported events for the cited work

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Observation 7a063990-7ba5-4fdb-8c5b-dbdee7b8dcfd · outbound

This paper cites Llama 3 model card.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Llama 3 model card

Reference 29

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Observation 3034f18c-9bd0-45ae-bf75-8567c5123594 · outbound

This paper cites Gpt-4o (version 2024-11-20) [large language model], 2024.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Gpt-4o (version 2024-11-20) [large language model], 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.997999Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 66a69e4d-a5db-4d2e-b468-c62f53f253be · outbound

This paper cites Gpt-4o-mini (version 2024-07-18) [large language model], 2024.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Gpt-4o-mini (version 2024-07-18) [large language model], 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.981981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d951dd37-b265-4d91-9786-8326e00f6703 · outbound

This paper cites an unresolved cited work.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Unresolved cited work

Reference 32

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Observation a96a1785-abbe-4a19-aa79-07e32209167c · outbound

This paper cites GPT-4 Technical Report.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis GPT-4 Technical Report

Reference 33

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Observation 904886d3-d879-4eab-a39a-394da6872429 · outbound

This paper cites TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:46.489368Z

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Observation bfef07c4-bdc4-4f2b-a49a-23c9e007ef0f · outbound

This paper cites Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Exploring the Potential of Large Language Models in Public Transportation: San Antonio Case Study

Reference 35

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Source-reported events for the cited work

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Observation dadd599f-59d9-43ef-933b-aa5c1a6d4295 · outbound

This paper cites Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:46.498214Z

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Observation ee5dc0e0-e41d-4a59-979c-2d8c15753cec · outbound

This paper cites What does bert look at? an analysis of bert’s attention.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis What does bert look at? an analysis of bert’s attention

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.951846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e1a5cba-9768-4c4b-aeb7-f64d4f2ea4b2 · outbound

This paper cites Attention is not explanation.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Attention is not explanation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.937155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fddbc9a9-d26d-447e-8fca-5b46b883b5e8 · outbound

This paper cites Explaining black box predictions and unveiling data artifacts through influence functions.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Explaining black box predictions and unveiling data artifacts through influence functions

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.922828Z

Source-reported events for the cited work

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Observation 98323933-ec0f-4320-93c5-1f4956f72e0e · outbound

This paper cites Axiomatic attribution for deep networks.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Axiomatic attribution for deep networks

Reference 40

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Source-reported events for the cited work

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Observation 6502a8bf-d6a8-40d9-9cf1-ac88b6c4728a · outbound

This paper cites From language modeling to instruction following: Understanding the behavior shift in llms after instruction tuning.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis From language modeling to instruction following: Understanding the behavior shift in llms after instruction tuning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.897443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 31ce5e80-4c91-4eea-8b83-ab056424b1e9 · outbound

This paper cites Retrieval- enhanced knowledge editing in language models for multi-hop question answering.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Retrieval- enhanced knowledge editing in language models for multi-hop question answering

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.881637Z

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Observation 5fdd798d-fc24-47c1-a201-de585af7969e · outbound

This paper cites Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data

Reference 43

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This paper cites Lora: Low-rank adaptation of large language models.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Lora: Low-rank adaptation of large language models

Reference 44

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Source-reported events for the cited work

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Observation 51a7d06b-ae16-4146-8061-5343e48e673d · outbound

This paper cites Decoupled weight decay regularization.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Decoupled weight decay regularization

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:46.536219Z

Source-reported events for the cited work

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Observation 3dfd91f3-3062-424a-8088-b5afec619ba4 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:46.847152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 41fe9ed6-f67d-4514-a3ac-efed3ca5e00f · inbound

STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models cites this paper.

STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-05T19:06:25.539818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.