A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper examines how large language models are transforming scientific research and discusses the opportunities and challenges they present for the scientific community.
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This paper reviews synthetic data as a solution to supply chain machine learning challenges, covering methods, applications, and research opportunities.
Diana‐Margarita Córdova‐Esparza
A systematic review of 82 studies (2023–2025) on AI-powered educational agents, proposing design principles and safeguards, and finding hybrid human–AI workflows outperform fully autonomous tutors.
Muthukuda Arachchige Dona Shiroma Jeeva Shirajanie Niriella
This paper examines AI's integration into Sri Lankan sentencing, highlighting risks of bias and proposing regulatory frameworks for equitable judicial use.
Shriniket Dixit, Anant Kumar, Kathiravan Srinivasan, et al.
This review explores AI's contributions to improving CRISPR-based genome editing, addressing challenges like off-target effects and delivery, and discussing future research directions.
Raouf Boutaba, Mohammad A. Salahuddin, Noura Limam, et al.
A comprehensive survey of machine learning applications across networking domains, covering techniques, challenges, and future research directions.
Tiantian Guo, Tongpo Zhang, Eng Gee Lim, et al.
This review traces wavelet theory from construction to applications, highlighting rational wavelet transform advantages and the evolution of Wavelet Neural Networks.
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This survey comprehensively reviews 3D Gaussian Splatting, covering its technologies, challenges, and opportunities for becoming a mainstream 3D representation method.
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This paper surveys the opportunities and challenges in federated learning, including an extension to vertical federated learning.
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This paper provides a comprehensive taxonomy, opportunities, and challenges of Representation Engineering (RepE) for large language models, a paradigm that steers model behavior by directly manipulating internal representations.
Hailong Jiang, Feng Yu, Emran Hossain, et al.
Introduces SeGaBench to test if LLMs can recover semantic optimization opportunities missed by compilers, showing strong models produce correct, validated artifacts in most cases.
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A survey of adversarial robustness in deep learning, focusing on opportunities and challenges, including formal verification of local robustness.