Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery | Paper Citation Record

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This paper cites The digital crime tsunami.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery The digital crime tsunami

Reference 1

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This paper cites and Choo, K.K.R., 2014.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Choo, K.K.R., 2014

Reference 2

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This paper cites and Martell, R., 2013.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Martell, R., 2013

Reference 3

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This paper cites and Browne, A., 2013.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Browne, A., 2013

Reference 4

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This paper cites A Comprehensive Overview of Large Language Models.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery A Comprehensive Overview of Large Language Models

Reference 5

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This paper cites Common Crawl: Data Collection and Use Cases for NLP.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Common Crawl: Data Collection and Use Cases for NLP

Reference 6

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This paper cites Foundational Models Defining a New Era in Vision: A Survey and Outlook.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Foundational Models Defining a New Era in Vision: A Survey and Outlook

Reference 7

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This paper cites and Chacko, V., 2023.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Chacko, V., 2023

Reference 8

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This paper cites Attention Is All You Need.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Attention Is All You Need

Reference 9

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This paper cites BERT: A Review of Applications in Natural Language Processing and Understanding.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery BERT: A Review of Applications in Natural Language Processing and Understanding

Reference 10

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This paper cites and Agarwal, S., 2020.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Agarwal, S., 2020

Reference 11

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Reference 12

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This paper cites ”Digital forensics research: The next 10 years.” digital investigation 7 (2010): S64-S73.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”Digital forensics research: The next 10 years.” digital investigation 7 (2010): S64-S73

Reference 13

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This paper cites ”A tool for extracting static and volatile forensic artifacts of windows 8.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”A tool for extracting static and volatile forensic artifacts of windows 8

Reference 14

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This paper cites ”Cloud forensics: Technical chal- lenges, solutions and comparative analysis.” Digital investigation 13 (2015): 38-57.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”Cloud forensics: Technical chal- lenges, solutions and comparative analysis.” Digital investigation 13 (2015): 38-57

Reference 16

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This paper cites ”Mobile forensics: advances, challenges, and research oppor- tunities.” IEEE Security & Privacy 15.6 (2017): 42-51.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”Mobile forensics: advances, challenges, and research oppor- tunities.” IEEE Security & Privacy 15.6 (2017): 42-51

Reference 17

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This paper cites ”Current and future trends in mobile device forensics: A survey.” ACM Computing Surveys (CSUR) 51.3 (2018): 1-31.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”Current and future trends in mobile device forensics: A survey.” ACM Computing Surveys (CSUR) 51.3 (2018): 1-31

Reference 18

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This paper cites ”A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues.” IEEE Communications Surveys & Tutorials 22.2 (2020): 1191-1221.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues.” IEEE Communications Surveys & Tutorials 22.2 (2020): 1191-1221

Reference 19

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This paper cites ”Internet of things forensics: A review.” Internet of Things 11 (2020): 100220.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”Internet of things forensics: A review.” Internet of Things 11 (2020): 100220

Reference 20

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This paper cites ”Internet of things forensics: Recent advances, taxonomy, require- ments, and open challenges.” Future Generation Computer Systems 92 (2019): 265-275.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”Internet of things forensics: Recent advances, taxonomy, require- ments, and open challenges.” Future Generation Computer Systems 92 (2019): 265-275

Reference 21

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This paper cites ”The impact of automation and arti ﬁ- cial intelligence on digital forensics.” Wiley Interdisciplinary Reviews: Forensic Science 3.6 (2021): e1418.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”The impact of automation and arti ﬁ- cial intelligence on digital forensics.” Wiley Interdisciplinary Reviews: Forensic Science 3.6 (2021): e1418

Reference 22

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This paper cites ”Explainable arti ﬁcial intelligence for digital forensics.” Wiley Interdisciplinary Reviews: Forensic Science 4.2 (2022): e1434.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery ”Explainable arti ﬁcial intelligence for digital forensics.” Wiley Interdisciplinary Reviews: Forensic Science 4.2 (2022): e1434

Reference 23

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Al-Dhaqm, S

Reference 24

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Alsewari, A.A., 2021

Reference 25

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Gadekallu, T.R., 2022

Reference 26

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This paper cites Handbook of digital forensics and investigation.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Handbook of digital forensics and investigation

Reference 27

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Nisbet, A., 2016

Reference 28

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Khan, I.U., Arti ﬁcial General Intelligence (AGI) Secu- rity

Reference 29

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This paper cites LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding

Reference 30

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis

Reference 31

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization

Reference 32

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This paper cites GPT-4 Technical Report.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery GPT-4 Technical Report

Reference 33

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery LLaMA: Open and Efficient Foundation Language Models

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Gemini: A Family of Highly Capable Multimodal Models

Reference 35

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Qwen Technical Report

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 37

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Reference 38

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Chiriatti, M., 2020

Reference 39

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Peng, H., 2024

Reference 40

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Batool, L., 2024

Reference 41

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This paper cites and Tselikas, N.D., 2023.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Tselikas, N.D., 2023

Reference 42

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This paper cites The Llama 3 Herd of Models.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery The Llama 3 Herd of Models

Reference 43

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Docling Technical Report

Reference 45

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Staar, P., 2022, August

Reference 46

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This paper cites and Staar, P., 2022.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Staar, P., 2022

Reference 47

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This paper cites Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency

Reference 48

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This paper cites and Sheppard, J., 2023.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Sheppard, J., 2023

Reference 49

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This paper cites and Liu, T., 2023.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Liu, T., 2023

Reference 50

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This paper cites Hermes 3 Technical Report.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Hermes 3 Technical Report

Reference 51

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Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Sutskever, I., 2023, July

Reference 52

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This paper cites and Lee, Y.J., 2024.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery and Lee, Y.J., 2024

Reference 53

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This paper cites huggingface.co/Intel/llava-llama-3-8b.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery huggingface.co/Intel/llava-llama-3-8b

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:34:53.315641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3079434e-f1d5-4b6d-af16-0f2c2b66a1d4 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T14:34:52.728921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8c682a5-3696-4e14-a775-d5c08121f43d · outbound

This paper cites an unresolved cited work.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:34:53.292973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 70814396-726f-4687-bd0d-2b4bd246fdd7 · outbound

This paper cites an unresolved cited work.

Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:34:53.272461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers