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LeCun’s LeWorldModel Explained: Finally a Stable JEPA World Model?
Sep 2, 2026 · original
In this post, we break down LeWorldModel (LeWM), a new stable Joint-Embedding Predictive Architecture (JEPA) world model, introduced in a paper co-authored by Yann LeCun. LeWM paper authors: Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, Randall Balestriero ( Source ) Introduction What Is LeCun’s Vision for World Models? It’s been 4 years since Yann LeCun laid out his vision for a more human-like AI in a famous 62-page paper, A Path Towards Autonomous Machine Intelligence . At the heart of that vision sits the world model. As humans, we carry a mental model of how the world behaves. If we push a cup, we expect it to slide. Push it too far, and we expect it to fall. LeCun says that similarly, an intelligent agent should be able to imagine the consequences of its actions before taking them. What Is JEPA? The architecture LeCun proposed for learning such a model is JEPA, short for
170,927 AI Papers Reveal the Biggest Research Shifts of the First Half of 2026
Jun 30, 2026 · original
We analyzed 170,927 AI research papers posted to arXiv since the beginning of 2025 up to June 26 th across its four main machine-learning categories, cs.CL (computation and language), cs.CV (computer vision), cs.LG (machine learning), and cs.AI (artificial intelligence, with the goal to find out what is actually changing in AI research right now. This is also the first edition of our AI Research Pulse , a recurring report from AI Papers Academy tracking how AI research evolves over time. The method is based on keyword matching against the title and abstract of every paper, using a curated set of topics, model families, and institutions. We split the window into three consecutive half-year periods, H1 2025, H2 2025, and H1 2026. Because the field grew ~25% overall, we track share of papers rather than absolute counts. Fastest Growing Topics The above chart ranks which research topics are
Microsoft’s SkillOpt: 2x Accuracy Without Touching the Model
Jun 16, 2026 · original
In this post, we break down SkillOpt: Executive Strategy for Self-Evolving Agent Skills , a new Microsoft research paper that introduces a gradient-descent-like approach for automatically improving AI agent skills without fine-tuning the underlying model. SkillOpt paper authors: Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei Gao, Qi Dai, Bei Liu, Kai Qiu, Yuqing Yang, Dongdong Chen, Xue Yang, Chong Luo ( Source ) Introduction Training Skills Like Neural Networks What if AI skills could be trained like neural networks? That’s exactly the idea behind Microsoft’s SkillOpt paper, which turns agent skills into something that can be automatically optimized, much like neural network weights. As AI agents become increasingly capable and common, success depends on more than just choosing the right foundation model. It also depends on the procedures surr
DeepSeek-V4 Explained: The End of Standard Attention in LLMs?
May 24, 2026 · original
In this post, we break down DeepSeek-V4, a new large language model architecture from DeepSeek designed for highly efficient reasoning over million-token contexts. DeepSeek-V4 paper title ( Source ) Why Long-Context Reasoning Is Becoming Critical for AI Models Agentic workflows are rapidly scaling up, and as they do, they require models to reason over longer and longer contexts. At the same time, the rise of reasoning models and test-time scaling has pushed models to spend more computation verifying and refining their own outputs. As a result, efficiently handling extremely long contexts is becoming more important than ever. However, there’s still a major bottleneck. The standard attention mechanism scales quadratically with sequence length, making ultra-long context reasoning incredibly expensive. Introducing DeepSeek-V4 DeepSeek’s new paper tackles this problem head-on. It is titled De
Google Nested Learning Explained: Hope Architecture, Continual Learning, and the End of Frozen LLMs
Apr 27, 2026 · original
In this post, we break down Google’s Nested Learning paper and the Hope architecture, a new approach to continual learning in LLMs that aims to overcome catastrophic forgetting. Nested Learning paper authors: Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni ( Source ) Introduction The Memory Limitation of Modern AI Current LLMs are deployed as fixed systems after training What if the biggest limitation of today’s AI isn’t intelligence, but memory? Today’s models are trained once on massive datasets, and then deployed as fixed systems. Once training ends, these models essentially stop learning. They are frozen in time. Even though they can adapt within a conversation, that learning doesn’t last. Transformers and the “Frozen Model” Problem Over the last few years, progress has been driven by large language models (LLMs) powered by the Transformer architecture, which was introdu
GDPO Explained: How NVIDIA Fixes GRPO for Multi-Reward LLM Reinforcement Learning
Jan 27, 2026 · original
This post breaks down GDPO, NVIDIA’s solution to a key limitation of GRPO in reinforcement learning for large language models. GDPO Paper Authors ( Source ) Introduction Reinforcement Learning and Reasoning in Large Language Models Reinforcement learning (RL) has become a core design element in the training process of large language models (LLMs). In particular, RL is used to shape reasoning capabilities, where models learn to spend significant thinking time to solve complex problems step by step using long chains of thought. This wave was sparked in early 2025 with the release of DeepSeek-R1 , which demonstrated that RL plays a critical role in developing these reasoning abilities in LLMs. DeepSeek relied on an RL algorithm called GRPO , short for Group Relative Policy Optimization , which quickly became extremely popular. GRPO Limitation for Modern LLMs While GRPO has been very success
DeepSeek’s mHC Explained: Manifold-Constrained Hyper-Connections
Jan 4, 2026 · original
In this post we break down DeepSeek’s “mHC: Manifold-Constrained Hyper-Connections”, which may be a crucial building block for how LLMs are built in 2026. mHC paper authors ( Source ) Introduction About the same time last year (early January 2025), DeepSeek revolutionized the AI industry with the release of DeepSeek-R1. Now, DeepSeek set a great start for 2026 with a fascinating new paper titled “mHC: Manifold-Constrained Hyper-Connections”, which is already generating significant hype as a possible driver for the next major AI breakthrough in 2026. This paper builds on an earlier paper from ByteDance called Hyper-Connections, but we don’t assume prior knowledge of this paper in this review. But before looking at hyper connections, we need to first talk about residual connections to properly understand the motivation of the paper. Residual Connections Residual Connection Illustration ( S
Emergent Hierarchical Reasoning in LLMs Through Reinforcement Learning
Dec 25, 2025 · original
In this post, we break down the paper “Emergent Hierarchical Reasoning in LLMs Through Reinforcement Learning” and explain how reinforcement learning develops reasoning in large language models, and where the aha moments come from. Finally, we also review a new RL algorithm called HICRA that leverages these insights. Emergent Hierarchical Reasoning in LLMs Through Reinforcement Learning paper authors ( Source ) The Rise of Large Reasoning Models Near the end of 2024, OpenAI released a series of models called o1, which demonstrated reasoning capabilities we hadn’t really seen before. These models marked the rise of what are now called large reasoning models , which are LLMs that spend significant thinking time to solve complex problems, thinking through problems step by step using long chains of thought. This long reasoning utilizes computing resources at inference time to increase the mo
Less Is More: Tiny Recursive Model (TRM) Paper Explained
Oct 24, 2025 · original
In this post, we break down the paper “Less is More: Recursive Reasoning with Tiny Networks”, which introduced the Tiny Recursive Model (TRM), a simpler version of the Hierarchical Reasoning Model (HRM), that outperforms HRM and even top reasoning LLMs with a tiny 7M parameters model on challenging reasoning benchmarks. The Tiny Recursive Model (TRM) paper title and author ( Paper link ) Introduction A couple of months back, we’ve reviewed a new architecture called the Hierarchical Reasoning Model (HRM), that with just 27 million parameters, was able to beat top large language models (LLMs) on some of the most challenging reasoning benchmarks. Now, a new paper titled Less is More: Recursive Reasoning with Tiny Networks introduces a new model architecture inspired by the Hierarchical Reasoning Model, called Tiny Recursive Model (TRM) With only 7 million parameters, and a simpler architect
DINOv3 Paper Explained: The Computer Vision Foundation Model
Sep 24, 2025 · original
In this post, we break down Meta AI’s DINOv3 research paper, the latest in their open-source family of computer vision foundation models, now achieving state-of-the-art results with a frozen backbone on many benchmarks. DINOv3 paper authors ( Source ) Introduction Just as we have large language models (LLMs) serving as general-purpose foundation models for natural language processing (NLP), computer vision is following a similar path with foundation models that can be reused across many tasks. One of the most widely adopted in the past two years has been Meta AI’s DINOv2 , which has become one of the go-to backbones in the community. Now, Meta AI has released the next step in this family: DINOv3 . Much like what we’ve seen with LLMs, DINOv3 is both larger and trained on more data, expanding from 1 billion to 7 billion parameters , and from 142 million images to 1.7 billion images . More

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