One-sentence Explanation
This is a curated list of cutting-edge large language model (LLM) research papers from July to December 2025, organized by Sebastian Raschka Blog, with classifications covering reasoning models, inference-time scaling, model architectures, training efficiency and other dimensions.
Simple Explanation
You can think of this list as a curated collection of AI large model academic papers. You don’t need to sift through thousands of academic papers to quickly grasp the key research directions of the LLM field in the second half of 2025, such as optimizing model reasoning efficiency, new model architecture designs, and methods to improve training efficiency.
Applicable Scenarios
- AI academic researchers quickly track the latest frontier developments in the LLM field from July to December 2025
- AI developers reference the latest research results to optimize their own model solutions
- AI industry practitioners understand the latest technical directions in the field
Related Concepts
- Large Language Model (LLM): An artificial intelligence model that can understand and generate human language
- Reasoning Models: Design directions related to how models complete logical reasoning, question answering and other tasks
- Inference-time Scaling: Technologies that adjust parameters or scale during the model’s inference stage to optimize effects
- Training Efficiency: Technologies that improve model training speed and reduce training costs
The content of this article is sourced from: Sebastian Raschka Blog