Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:22:46.540150Z
Paper Citation Record · LEDGER
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis
As of 16 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T19:06:25.134699Z
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
- unresolved19
- parse uncertain0
- malformed identifier0
- metadata mismatch1
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 2026-08-16T06:30:59.297886+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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.415856Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6f360499-dd49-41ac-9149-e70567eaceef · 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 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 2026-08-16T06:30:59.297886+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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Unavailable: canonical work link unavailable.
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
Unavailable: canonical work link unavailable.
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
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-15T23:22:46.816211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.444442Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.449157Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-16T06:30:59.297886+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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.458202Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.462914Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.467646Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T23:22:46.966999Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.484966Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.493635Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.515278Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-16T06:30:59.297886+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.527452Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68424cee-3257-4ad2-8046-21342a0829fd · outbound
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:22:46.531645Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Unavailable: canonical work link unavailable.
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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+00:00.