A curated roundup of notable LLM research papers that came out this year
As some of you know, I have the long-running habit of keeping a running list of research papers I want to read, revisit, or cite in future articles and projects.
Last year, I shared two organized paper lists, one covering January to June and another one covering July to December.
Several readers told me that these lists were very useful, so, in a similar spirit, I prepared a new list for the first half of 2026. This one covers papers I bookmarked from January through May 2026.
Please do not treat this as a complete list of everything published this year. There are so many papers published every day that this would be totally infeasible. Instead, this is a curated reference list based on papers I found interesting or relevant for my own work. I went through the titles, abstracts, and topic framing carefully while organizing the list, but I have to admit that I also only read a subset of the papers in detail.
Why make these lists in the first place? When I work on an article, book section, code example, or lecture, I often remember that I saw a relevant paper somewhere, but finding it again can be surprisingly annoying. A categorized Markdown list solves that problem for me, and I hope it is useful to you as well. (Even in the era of LLM-based web searching, having a specific context list is pretty useful, still.)
This year, the list is again heavy on reasoning models, reinforcement learning, and efficient inference, because I am biased towards bookmarking papers that are related to things I am currently working on. However, compared with the 2025 lists, I also bookmarked more papers around agent harnesses, tool use, long context, diffusion language models, and practical serving infrastructure, because that’s what I am currently pretty involved in and where the field is headed.
The categories for this research paper list are as follows. (Pro tip: In the web version of this article, you can use the table of contents on the left to jump directly to the sections that are most relevant to you.)
Architecture and Model Design
Efficient Training and Scaling
Inference Efficiency and KV Cache
Sparse Attention and Long Context
Reasoning and Test-Time Compute
Reinforcement Learning and RLVR
Agent Systems and Tool Use
Coding Agents and Software Engineering
Diffusion Language Models