2026 January to May List of LLM Research Papers: Sebastian Raschka (magazine.sebastianraschka.com)

Sebastian Raschka has released a curated list of notable research papers on large language models (LLMs) from January to May 2026, reflecting key themes in the ongoing evolution of AI and machine learning. The list emphasizes advancements in reasoning models, reinforcement learning, and efficient inference while highlighting emerging topics like agent harnesses and tool use. This collection serves not only as a resource for fellow researchers but also streamlines the often cumbersome process of paper discovery, crucial in a rapidly growing field. Significantly, Raschka draws attention to innovative hybrid architecture designs like the Nemotron 3 Super, which incorporates a mix of traditional attention layers and Mamba-2 layers to enhance long-context processing efficiency—a growing necessity as LLM applications become more complex. This trend mirrors the development of other LLMs, such as Qwen3.6, which employ similar hybrid strategies. The list is categorized into various sections, such as Architecture and Model Design, Efficient Training, and Inference Efficiency, allowing readers to easily navigate topics of interest. The careful curation promises to be a valuable asset for both established researchers and newcomers eager to stay abreast of cutting-edge developments in AI/ML.