The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning Chen et al.: https://
arxiv.org/abs/2601.06002 #ArtificialIntelligence #DeepLearning #MachineLearning
RESEARCH
-
Molecular Structure of Thought: Mapping Long Chain-of-Thought Reasoning
By
–
-
Can Diffusion Models Replace Traditional LLMs for Fast AI Agents?
By
–
Can diffusion models replace traditional LLMs to build lightning-fast AI agents? Researchers from Southeast University, Alibaba, and NTU Singapore just released a reality check on diffusion-based language models like LLaDA and Dream. They tested whether the speed of
-
Book Impact on Graph Databases and Linked Data Thinking
By
–
This book had a big impact on my thinking (graph databases, linked data, semantic knowledgebases, contextual metadata, connected IoT, context engineering, etc.): http://
amzn.to/4cBYJIi "Linked: How Everything is Connected to Everything Else — What It Means for Business, -
Pretraining Vision and LLMs: Building Foundation Models on AWS
By
–
Pretrain Vision and #LLMs (Large Language Models) in #Python — Techniques for Building & Deploying Foundation Models on #AWS: http://
amzn.to/3pT9seD v/ @PacktDataML ————
#AI #MachineLearning #DeepLearning #MLOps #DataScience #ComputerVision #DataScientist #GenAI #GenerativeAI -
Meta-Learning Approaches in Deep Learning: MAML Framework Discussion
By
–
Cool! I only had a quick sim earlier today but really enjoyed a number of ideas even unrelated to the claw part, esp around the skills system. In deep learning there were a number of meta learning approaches (Eg MAML paper in 2017) where the goal is to optimize for the model
-
Operating Systems Will Be Rebuilt Using Differentiable Programming
By
–
Prediction: we will end up rebuilding Operating Systems using automatically differentiable programming primitives. It won’t be a monumental task with LLMs, and it will lead to gradients back-propagating into the OS kernel. < 7 years.
→ View original post on X — @reza_zadeh, 2026-02-21 03:39 UTC
-
WAMs Replace VLAs: Video Models for Advanced Robot Manipulation
By
–
An elegant and simple pipeline Seonghyeon Ye (@SeonghyeonYe) VLAs (from VLMs) ❌ => WAMs (from Models) ✅ Why WAMs? 1️⃣ World Physics: VLMs know the internet, but Models implicitly model the physical laws essential for manipulation. 2️⃣ The "GPT Direction": VLAs are like BERT (rely heavily on task-specific post-training). WAMs are like GPT (pre-train & prompt), unlocking incredible zero-shot transfer! What I want to see in 2026: 📈 Scaling Laws: We will see much clearer scaling laws for robotics compared to VLAs. 🤝 Human-to-Robot Transfer: Unlocking massive transfer capabilities using video as a shared representation space. 🤖 Zero-Shot Mastery: Moving from short-horizon tasks to long-horizon, dexterous manipulation without task-specific demonstrations. We recently open-sourced the checkpoints, training and inference code. Dive into the research! 👇 📄 Paper: arxiv.org/abs/2602.15922 💻 Code: github.com/dreamzero0/dreamz… 🤗 HF: huggingface.co/GEAR-Dreams/D… — https://nitter.net/SeonghyeonYe/status/2024501978106061056#m
→ View original post on X — @shiqi_yang_147, 2026-02-21 03:30 UTC
-
OpenAI Reports Positive Progress Across All Divisions
By
–
seeing so much positive progress across each part of openai right now, very proud of the team
-
Robbyant unveils spatial perception AI model for robots
By
–
.
@AntGroup
's subsidiary Robbyant (an Embodied AI company) unveils Spatial Perception AI Model, to enhance robots’ depth sensing and 3D environmental understanding capabilities in complex real-world environments. Read about Embodied AI & Robbyant's model: https://
businesswire.com/news/home/2026
0126215468/en/Ant-Group-Subsidiary-Robbyant-Unveils-Spatial-Perception-AI-Model-LingBot-Depth
…