Survey of LLM Research Papers for the First Half of 2026

Sebastian Raschka, a well-known machine learning specialist, has compiled a selection of research papers on large language models (LLMs) for the first half of 2026, which he has curated for himself. The list is organized by category: architecture and design, efficient training and scaling, inference efficiency and KV cache, sparse attention and long context, reasoning and test-time compute scaling, reinforcement learning and RLVR, agentic systems and tool use, code agents and software engineering, diffusion language models, and evaluation and benchmarks. In 2026, according to the author, key trends include hybrid architectures (e.g., Nvidia's Nemotron 3, combining attention and Mamba-2 layers), state-space models (Mamba-3), efficient expert distribution in MoE, activation analysis (The Spike, the Sparse and the Sink), and representation geometry. Notable mentions include the Qwen3.6 model with Gated DeltaNet, as well as the releases of Nemotron 3 Nano (4B) and Nemotron 3 Ultra (550B-A55B). The selection also includes works on multi-token prediction, synthetic data, and post-training quantization.