LLM Research Papers: The 2026 List (January to May)
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
Model Evaluation and Benchmarks
1. Architecture and Model Design
This first section collects papers on model architecture, model-release technical reports, and papers that help explain why current LLMs look the way they do.
One thing I find interesting about 2026 so far is that architecture work goes beyond making transformers larger. There is a lot of work around
hybrid architectures (for example, Nemotron 3, and Arcee Trinity),
state space layers (Nemotron 3 and Mamba-3),
MoE capacity allocation (Scaling Embeddings Outperforms Scaling Experts, and Step 3.5 Flash),
activation behavior (The Spike, the Sparse and the Sink),
and representation geometry (Symmetry in Language Statistics Shapes the Geometry of Model Representations).
All of these papers are quite interesting, which is why I bookmarked them in the first place. But if I had to pick one must-read, I’d probably be Nemotron 3 Super, because the article is super detailed (no pun intended), and it describes techniques used in a model that is already in production. And it’s one of the best models in its size class after all.
One of the interesting aspects of Nemotron 3 is its hybrid-architecture design, meaning that it alternates between regular attention layers and Mamba-2 (state space model) layers to be more efficient at long contexts. In 2026, long-context efficiency is king as more and more LLMs get plugged into agent harnesses (OpenClaw etc.), which requires working with longer and longer contexts.
That being said, 120B-A12B may be a bit too large for local inference on regular consumer hardware, but there is a Nemotron 3 Nano (4B) version as well.
Note that 2 days ago, Nvidia also released a scaled up-version of this, Nemotron 3 Ultra (550B-A55B), which scales the embedding and projection dimensions but otherwise uses the same building blocks. If you are interested in a visual, I posted about it on Substack Notes here.
This hybrid-architecture trend with alternating attention and alternative layers is a relatively popular development this year. The probably most popular open-weight LLM series that uses a similar hybrid design is probably Qwen3.6, which uses Gated DeltaNet layers instead of Mamba-2 layers for the non-attention portions. For more information, see my Hybrid Attention (https://sebastianraschka.com/llm-architecture-gallery/hybrid-attention/ write-up, which pools information from several of my previous substack articles where I wrote about these.
Also, in the paper list below, you may notice that there is now a Mamba-3 and Gated DeltaNet-2 (i.e., newer versions of Mamba-2 and GatedDeltaNet), and it will be interesting to see those in the upcoming open-weight LLMs (e.g., Nemotron-4 and Qwen4?).
Next to describing the hybrid-architecture design, the Nemotron-3 paper contains a whole lot of other interesting ablations, for example, around multi-token prediction for speculative decoding, NVFP4 pretraining versus BF16, synthetic MMLU-style data, and post-training quantization recipes, but covering these in detail would be out of scope for this overview.
1 Jan, Deep Delta Learning, https://arxiv.org/abs/2601.00417
6 Jan, MiMo-V2-Flash Technical Report, https://arxiv.org/abs/2601.02780
13 Jan, Ministral 3, https://arxiv.org/abs/2601.08584
29 Jan, Scaling Embeddings Outperforms Scaling Experts in Language Models, https://arxiv.org/abs/2601.21204
30 Jan, LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs, https://arxiv.org/abs/2602.00462
4 Feb, ERNIE 5.0 Technical Report, https://arxiv.org/abs/2602.04705
8 Feb, ViT-5: Vision Transformers for the Mid-2020s, https://arxiv.org/abs/2602.08071 (Most of this article is LLM-focused, but I couldn’t resist to include a new major vision transformer design.)
11 Feb, Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters, https://arxiv.org/abs/2602.10604
12 Feb, Nanbeige4.1-3B: A Small General Model That Reasons, Aligns, and Acts, https://arxiv.org/abs/2602.13367
16 Feb, Symmetry in Language Statistics Shapes the Geometry of Model Representations, https://arxiv.org/abs/2602.15029
17 Feb, GLM-5: From Vibe Coding to Agentic Engineering, https://arxiv.org/abs/2602.15763
18 Feb, Arcee Trinity Large Technical Report, https://www.arxiv.org/abs/2602.17004
4 Mar, The Spike, the Sparse and the Sink: Anatomy of Massive Activations and Attention Sinks, https://arxiv.org/abs/2603.05498
12 Mar, Tiny Aya: Bridging Scale and Multilingual Depth, https://arxiv.org/abs/2603.11510
15 Mar, Attention Residuals, https://arxiv.org/abs/2603.15031
16 Mar, Mamba-3: Improved Sequence Modeling Using State Space Principles, https://arxiv.org/abs/2603.15569
31 Mar, Attention to Mamba: A Recipe for Cross-Architecture Distillation, https://arxiv.org/abs/2604.14191
13 Apr, Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning, https://arxiv.org/abs/2604.12374
6 May, ZAYA1-8B Technical Report, https://arxiv.org/abs/2605.05365
13 May, Delta Attention Residuals, https://arxiv.org/abs/2605.18855
21 May, Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention, https://arxiv.org/abs/2605.22791
25 May, The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence, https://arxiv.org/abs/2605.26494
2. Efficient Training and Scaling
This section is about training systems, adaptation methods, and scaling recipes. These papers are not (all) about pre-training from scratch. Some focus on fine-tuning, distillation, test-time training, or making training work better on constrained hardware.
Facts Only
A curated list of LLM research papers from January to May 2026 was compiled, focusing on hybrid architectures, efficient inference, and agent systems.
Nemotron 3 Super is a hybrid model combining Mamba-2 (state space layers) and attention layers, designed for long-context efficiency.
Nemotron 3 Ultra (550B parameters) was released in May 2026, scaling embedding dimensions while retaining the hybrid architecture.
Qwen3.6 uses a hybrid design with Gated DeltaNet layers alongside attention mechanisms.
Mamba-3 and Gated DeltaNet-2 are newer versions of state space and linear attention layers, respectively.
Key papers include "Scaling Embeddings Outperforms Scaling Experts" (January 2026) and "Step 3.5 Flash" (February 2026), which explore MoE and efficient scaling.
"Symmetry in Language Statistics Shapes the Geometry of Model Representations" (February 2026) examines how linguistic patterns influence model internals.
"The Spike, the Sparse and the Sink" (March 2026) analyzes activation behavior and attention sinks in large models.
ERNIE 5.0 and GLM-5 technical reports were published in February 2026, detailing advancements in model architecture and agentic engineering.
The list includes papers on diffusion language models, reinforcement learning, and practical serving infrastructure.
The curator acknowledges a bias toward papers relevant to their work, particularly in reasoning models, agent harnesses, and long-context efficiency.
The list is not exhaustive but serves as a reference for trends in early 2026 LLM research.
Executive Summary
The first half of 2026 has seen significant advancements in large language model (LLM) research, with a focus on hybrid architectures, efficient inference, and agent systems. A curated list of research papers highlights trends such as the integration of state space layers (e.g., Mamba-3) with traditional attention mechanisms, as seen in models like Nemotron 3 and Qwen3.6. These hybrid designs aim to improve long-context efficiency, a critical requirement for agent-based applications. Notable papers include Nemotron 3 Super, which details a production-ready model combining Mamba-2 and attention layers, and Step 3.5 Flash, which explores mixture-of-experts (MoE) scaling strategies. Other key areas include sparse attention mechanisms, reinforcement learning, and diffusion language models. The list also reflects growing interest in practical deployment challenges, such as model serving infrastructure and tool use for agents. While the selection is biased toward the author's research interests, it provides a snapshot of emerging directions in LLM development, particularly in balancing computational efficiency with performance.
The papers span a range of topics, from architectural innovations like Gated DeltaNet-2 to evaluations of model behavior under different training regimes. For instance, "Scaling Embeddings Outperforms Scaling Experts" challenges conventional MoE approaches, while "Symmetry in Language Statistics Shapes the Geometry of Model Representations" offers insights into how linguistic patterns influence model internals. The inclusion of technical reports from major labs (e.g., ERNIE 5.0, GLM-5) underscores the rapid pace of both open and proprietary research. However, the list is not exhaustive and reflects the curator's specific focus areas, such as reasoning models and agentic systems. The emphasis on hybrid architectures and long-context efficiency suggests these will remain central themes as LLMs are increasingly deployed in real-world applications.
Full Take
This curated list of LLM research papers from early 2026 reveals several notable patterns in the field. First, the dominance of hybrid architectures—combining traditional attention with state space layers (e.g., Mamba) or linear attention variants (e.g., Gated DeltaNet)—suggests a shift toward more efficient long-context processing. This trend aligns with the growing demand for agentic systems that require extended context windows, as seen in frameworks like OpenClaw. The emphasis on hybrid designs also reflects a broader move away from purely scaling model size, with papers like "Scaling Embeddings Outperforms Scaling Experts" challenging the conventional wisdom of MoE approaches. This could signal a maturation in the field, where architectural innovation takes precedence over brute-force scaling.
Second, the list highlights a tension between open and proprietary research. While models like Nemotron 3 and Qwen3.6 are open-weight, their technical reports are published by well-resourced labs (Nvidia, Alibaba), raising questions about accessibility and reproducibility. The inclusion of papers like "Tiny Aya" and "Nanbeige4.1-3B" suggests efforts to democratize high-performance models, but the sheer volume of research—much of it from industry labs—may still create barriers for independent researchers. The curator's admission of bias toward their own interests (e.g., agent systems) also underscores the subjective nature of such lists, which inevitably shape perceptions of what constitutes "important" research.
Finally, the focus on practical deployment—such as inference efficiency, KV cache optimization, and tool use—indicates a pivot from academic benchmarks to real-world applicability. This shift is double-edged: while it addresses critical gaps in usability, it may also accelerate the commodification of LLM research, where commercial interests drive the agenda. The absence of papers on ethical considerations or societal impacts in this list is striking, given the rapid integration of LLMs into high-stakes domains. A peer reviewer might flag this as a limitation, asking whether the field's current trajectory adequately addresses risks like bias, misalignment, or environmental costs.
**Bridge Questions:**
1. How might the trend toward hybrid architectures influence the trade-offs between model interpretability and performance?
2. What would it take for smaller research teams to replicate or build upon these hybrid designs, given the computational resources required?
3. If long-context efficiency becomes the primary benchmark, what secondary effects might this have on model behavior (e.g., attention dilution, spurious correlations)?
**Patterns detected:** None. The content is a scholarly curation of research trends, with no evident manipulation patterns. The curator's transparency about bias and the focus on technical advancements align with academic norms.
Sentinel — Human
LIKELY_HUMAN (confidence: 0.05)