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Author

Miles Q. Li

Observed names: Li, Miles Q., Miles Li
ORCID: 0000-0001-7091-3268
Observed ORCID: 0000-0001-7091-3268
OpenAlex ID: A5063351983
Malware classification and composition analysis: A survey of recent developments
2021 · Adel Abusitta, Miles Q. Li, et al. · Journal of Information Security and Applications · 121
Survey on Explainable AI: Techniques, challenges and open issues
2024 · Adel Abusitta, Miles Q. Li, et al. · Expert Systems with Applications · 116
Security concerns for Large Language Models: A survey
2025 · Miles Q. Li, Benjamin C.M. Fung · Journal of Information Security and Applications · 74
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I-MAD: Interpretable malware detector using Galaxy Transformer
2021 · Miles Q. Li, Benjamin C.M. Fung, et al. · Computers & Security · 54
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A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents
2025 · Li, Miles Q., Fung, Benjamin C. M., et al. · arXiv (Cornell University) · 20
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VDGraph2Vec: Vulnerability Detection in Assembly Code using Message Passing Neural Networks
2022 · Ashita Diwan, Miles Q. Li, et al. · International Conference on Machine Learning and Applications · 13
On the Effectiveness of Interpretable Feedforward Neural Network
2022 · Miles Q. Li, Benjamin C. M. Fung, et al. · International Joint Conference on Neural Networks (IJCNN) · 9
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I-MAD: A Novel Interpretable Malware Detector Using Hierarchical Transformer.
2019 · Miles Q. Li, Benjamin C. M. Fung, et al. · arXiv (Cornell University) · 9
Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents
2026 · Li, Miles Q., Fung, Benjamin C. M., et al. · arXiv (Cornell University) · 7
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DyAdvDefender: An instance-based online machine learning model for perturbation-trial-based black-box adversarial defense
2022 · Miles Q. Li, Benjamin C.M. Fung, et al. · Information Sciences · 6
Lacuna: A Research Map for Machine Learning
2026 · Weiss, Martin, Li, Miles Q., et al. · arXiv (Cornell University) · 3
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LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models
2026 · Jiang, Zhiyuan, Hong, Weihao, et al. · arXiv (Cornell University) · 3
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A Novel and Dedicated Machine Learning Model for Malware Classification
2021 · Miles Q. Li, Benjamin C. M. Fung, et al. · International Conference on Software Technologies (ICSOFT) · 3
Training Dynamics of a 1.7b Llama Model: A Data-Efficient Approach
2025 · Miles Q. Li, Benjamin C. M. Fung, et al. · SSRN Electronic Journal · 2
On the Effectiveness of Incremental Training of Large Language Models
2024 · Li, Miles Q., Fung, Benjamin C. M., et al. · arXiv (Cornell University) · 2
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Interpretable Malware Classification based on Functional Analysis
2022 · Miles Li, Benjamin Fung · International Conference on Software Technologies (ICSOFT) · 2
Empty Commitments: When Agents Promise What They Cannot Deliver
2026 · Tang, Jiaqi, Shen, Bingyu, et al. · arXiv (Cornell University) · 1
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Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection
2026 · Guan, Xinlei, Arosemena, David, et al. · arXiv (Cornell University) · 1
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GPT-based self-supervised anomaly detection in command lines
2026 · Miles Q. Li, Julien Keutchayan, et al. · Journal of Computer Virology and Hacking Techniques · 1
How to Tame Pre-Trained Transformers for Authorship Verification
2025 · Miles Q. Li, Benjamin C. M. Fung, et al. · SSRN Electronic Journal · 1
A Novel Deep Multi-head Attentive Vulnerable Line Detector
2023 · Miles Q. Li, Benjamin C.M. Fung, et al. · Procedia Computer Science · 1
Who May Release an Agent's Promise? Standing over Commitments in Multi-Party Chat
2026 · Wei, Lan, Shen, Bingyu, et al. · arXiv (Cornell University)
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MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs
2026 · Li, Boyang, Shen, Bingyu, et al. · arXiv (Cornell University)
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Adaptive Prompt Embedding Optimization for LLM Jailbreaking
2026 · Li, Miles Q., Fung, Benjamin C. M., et al. · arXiv (Cornell University)
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Training Dynamics of a 1.7B LLaMa Model: A Data-Efficient Approach
2025 · Miles Q. Li, Benjamin C. M. Fung, et al. · International Joint Conference on Neural Networks (IJCNN)
Works count: 29
Citation count: 449
H-index: 8
I10-index: 6
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