Mathematical Foundations and Optimization Techniques for Large Language Models
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 21
Special Issue Editors
Interests: large language model; embodied AI; recommendation
Interests: global optimization algorithm; robotics and artificial intelligence; geometric vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, large language models (LLMs) have become a cornerstone of artificial intelligence, driving advancements in natural language processing, knowledge reasoning, multimodal intelligence, and embodied/agentic AI. However, the rapid development of LLMs also presents significant challenges in mathematical foundations, computational optimization, and efficient deployment. This Special Issue aims to present innovative research on the mathematical theories and optimization methodologies underlying the design, training, and application of LLMs.
We invite researchers to submit original research articles, reviews, and perspectives on topics including, but not limited to, the following:
- Mathematical analysis of transformer architectures and attention mechanisms
- Modeling and optimization of multimodal LLMs
- Design and applications of LLMs for recommendation systems
- Development of LLMs for embodied AI applications
- Security and adversarial robustness in LLMs
- Optimization algorithms for training billion-parameter models
- Theoretical frameworks for model interpretability and explainability
- Distributed and federated learning strategies for LLMs
- Efficient inference techniques with provable computational bounds
- Information-theoretic approaches to knowledge representation
- Causal reasoning and logical formalization in LLMs
We encourage submissions that encompass mathematical analyses, innovative optimization methodologies, and practical implementations that address the scalability, efficiency, and theoretical limitations of LLMs. Both theoretical advancements and applied studies, including experimental validations, are welcome.
We look forward to your contributions!
Dr. Hao Chen
Dr. Yinlong Liu
Guest Editors
Manuscript Submission Information
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Keywords
- large language models (LLMs)
- mathematical foundations of LLMs
- optimization techniques for LLMs
- multimodal LLMs
- LLMs for recommendation systems
- embodied AI and LLMs
- LLM security and robustness
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